Startseite An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer
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An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer

  • Ahmad Ameri , Nazanin Rahnama , Fereshteh Talebi , Ainaz Sourati und Farzad Taghizadeh-Hesary EMAIL logo
Veröffentlicht/Copyright: 11. Mai 2023
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Oncologie
Aus der Zeitschrift Oncologie Band 25 Heft 3

Abstract

Objectives

This study aimed to evaluate the predictive value of the Cancer Aging Research Group (CARG) in Iranian patients as a representative of the Middle East North Africa (MENA) region population.

Methods

This prospective longitudinal study involved patients 65 years and older starting a new cytotoxic chemotherapy regimen. We did general (including Karnofsky performance status, KPS) and CARG-based assessments before chemotherapy. Chemotherapy toxicities were recorded during chemotherapy courses. The predictive values of CARG and KPS were evaluated using the area under the receiver-operating characteristic curve (AUC-ROC). Chemotherapy toxicities were sub-analyzed per hematologic and nonhematologic types.

Results

Chemotherapy-related toxicity was reported in 23.6 % of patients. The corresponding area under the receiver-operating characteristic curve (AUC-ROC) was 0.56 (95 %CI, 0.40–0.69) for total toxicity, 0.67 (95 % CI, 0.48–0.78) for hematologic toxicity, and 0.39 (95 %CI, 0.21–0.66) for nonhematologic toxicity.

Conclusions

CARG model had an acceptable ability to predict hematologic toxicities; however, its efficacy for total and nonhematologic toxicities was limited.

Keywords: cancer; chemotherapy; geriatric assessment; geriatric oncology

Introduction

Despite recent advancements, cytotoxic chemotherapy is the leading systemic therapy for cancer [1, 2]. Chemotherapy in older individuals is usually complicated by frailty, medical comorbidities, frailty, poor functional status. According to the 2020 report of the International Agency for Research on Cancer (IARC), older adults (65 years and older) constituted 51.5 % of the total new cancer cases worldwide [3]. Among 131,191 new cancer cases in 2020 in Iran, 66,251 (50.5 %) were older than 65 years old, and it will increase more in coming decades due to the demographic transition into population aging [4], [5], [6]. Similar efficacy but more toxicity is the reason for hope and fear of chemotherapy in older patients [7], [8], [9]. A study from Spain reported that 1.3 % of short-term mortalities were due to chemotherapy toxicities. Another study from the United Kingdom noted that chemotherapy-induced mortality was more common in older adults [10].

Distinguishing patients who can tolerate standard doses from those who require less intensive therapy is of crucial importance. Over the decades, conventional functional status measures, such as the Eastern Cooperative Oncology Group performance status (ECOG) and the Karnofsky performance status (KPS), have been applied without significant evidence for their efficiency in the older population [11]. Besides, ECOG and KPS measure the patient’s performance subjectively [12]. Objective evaluation of performance status can further guide practitioners to select an appropriate treatment for patients, reducing toxicity and improving their quality of life [13]. To address this requirement, Comprehensive Geriatric Assessment (CGA) was developed in the 1930s with the efforts of Marjory Warren [14]. CGA is a multidisciplinary diagnostic and treatment process to identify frail older patients’ medical, functional, and psychosocial limitations to ensure appropriate treatment is provided [15]. However, CGA is not available in all settings due to the need for coordination of multidisciplinary specialties, the time required for evaluation, and the lack of access to some disciplines (e.g., outpatient social work, pharmacy, and nutrition) in some practices [16]. To facilitate CGA in routine oncology practice, several screening tools have been developed to assess the risk of severe toxicities using information from CGA, including the Chemotherapy Risk Assessment Scale for High-Age Patients (CRASH) and the Cancer and Aging Research Group (CARG) chemotoxicity calculators. Per the 2018 ASCO guideline for geriatric oncology, either the CARG or CRASH tools are recommended to obtain estimates of chemotherapy toxicity risk [17]. However, CARG is more practical, using more data achievable during regular office visits [18].

The CARG model was introduced by Hurria et al. in 2011 to predict chemotherapy toxicity in patients with cancer ≥65 years, considering both objective and subjective criteria, and developed a practical tool [11]. This prediction tool comprises eleven questions in four domains: baseline characteristics, treatment, laboratory values, and geriatric assessment (Table 1). Cavdar et al. recently demonstrated that the CARG score strongly predicts chemotherapy toxicity in non-hematologic malignancies. In addition, it found that the predictive value of CARG is more than Geriatric 8 (G8) and Vulnerable Elders Survey (VES-13) (AUC-ROC 0.82 vs. 0.74 and 0.72) [19].

Table 1:

The cancer and aging research group study (CARG) prediction model for predicting chemotherapy toxicity in older patients.

Domain Variable Value/Response Score
Baseline characteristics Age, y ≥72 2
65–72 0
Cancer type GI or GU 2
Other 0
Planned treatment Planned CTx dosea Standard dose 2
Dose reduced upfront 0
Planned no. of CTx drugs Poly CTx 2
Mono CTx 0
Laboratory values Hemoglobin <11 g/dL (male), <10 g/dL (female) 3
≥11 g/dL (male), ≥10 g/dL (female) 0
Creatinine clearanceb <34 mL/min 3
≥34 mL/min 0
Geriatric assessment questions How is your hearing (with a hearing aid, if needed)? Fair, poor, or totally deaf 2
Excellent or good 0
Number of falls in the past 6 months ≥1 3
None 0
Can you take your own medicine? With some help/unable 1
Without help 0
Does your health limit you in walking one block? Somewhat limited/limited a lot 2
Not limited at all 0
During the past four weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting with friends, relatives, etc.)? Limited some of the time, most of the time, or all of the time 1
Limited none of the time or a little of the time 0

  1. CTx, chemotherapy; GI, gastrointestinal; GU, genitourinary; no., number; aBased on the national comprehensive cancer network (NCCN) guidelines. bUsing Cockcroft-Gault equation. Source: reference no. 11.

Accumulating evidence has reflected the impact of racial differences on chemotherapy toxicity [20], [21], [22], [23]. Exploring the ‘ethnic-specific genetic signatures’ can guide practitioners in selecting the appropriate chemotherapy regimen [24]. Examining and validating the prediction models of chemotherapy toxicity in different races is required until then. The development and validation studies of the CARG model included patients of three primary races: White-American, Black-American, and Asian. Hitherto, the CARG model has been merely evaluated in the United States (main CARG study), India, and Japan [7, 25, 26]. The CARG and Indian cohorts noted that the CARG model could predict chemotherapy toxicity in older adults [7, 25]. However, the Japanese cohort found that the CARG model has low discrimination in predicting hematologic toxicity [26]. Given the remarkable impact of race on chemotherapy toxicity, extrapolating the study results across different races is not an acceptable practice. Therefore, it requires external validation in different populations before general application. This prospective cohort was therefore designed to examine the CARG model in Iran as a representative of Middle Eastern and North African (MENA) countries [27] and compare its predictive value with the physician-reported KPS.

Materials and methods

Study design, setting, and participants

This study aimed to examine the CARG prediction tool in Iranian patients with cancer referred to the Clinical Oncology Department of Imam Hossein Hospital (Tehran, Iran). To this end, we took steps similar to the methods of the development cohort conducted by Hurria et al. [11]. Besides, we compared the CARG model with the physician-rated KPS to predict the chemotherapy toxicities. The study group was not limited to a single regimen or malignancy to challenge the model’s generalization. The eligibility criteria were: age ≥65 years, any solid tumor with any stage, and patients who were candidates for a new chemotherapy regimen.

After meeting the inclusion criteria and consenting to participate, we asked the patients to answer the geriatric assessment questions as the development CARG study. Before the first chemotherapy cycle, baseline characteristics (age, sex, weight, height, education level, marital status, and cancer type and stage), laboratory data (leukocyte count, hemoglobin, platelet count, and creatinine), and patients’ KPS were recorded. Table 1 represents the CARG prediction model. It is composed of eleven criteria in four categories: (1) baseline characteristics (age and cancer type), (2) planned treatment (standard or adjusted chemotherapy dose and the number of chemotherapy agents), (3) laboratory values (hemoglobin and creatinine clearance), and (4) general geriatric assessment (hearing status, recent falls, self-sufficiency in taking medications, and health problem limiting walking or daily social activities). Based on the overall risk score, participants were grouped into three categories: low-risk (sum score 0–5), intermediate-risk (sum score 6–9), and high-risk (sum score 10–19). The treating team was blinded to the calculated risk score and category of the patients during the study.

The Chemotherapy protocol was based on the physician’s discretion. The participants were carefully monitored for possible toxicities during the chemotherapy course before each cycle and were followed for two weeks after the chemotherapy ended. An experienced clinical oncologist (A.A.) evaluated the patients in each treatment course and between courses in case of existing toxicity and recorded grade 3 (hospitalization possibly indicated), grade 4 (life-threatening), and grade 5 (fatal), if any, adverse effects. Chemotherapy-related toxicities were recorded per the Common Terminology Criteria for Adverse Events (CTCAE) v5.0 [28]. A decline in hematologic parameters was recorded as an adverse effect only if it was associated with patients’ symptoms or in the case of chemotherapy delay or dose modification. G-CSF (granulocyte colony-stimulating factor) as primary prophylaxis was started with chemotherapy at the physician’s discretion based on several factors, including the patient’s age, performance status, polychemotherapy regimen, and primary disease status. Patients who required primary prophylaxis received G-CSF at the same dosage during the chemotherapy cycle. G-CSF support during chemotherapy (as a secondary prophylaxis) was considered for those who developed severe/febrile neutropenia. The CARG scores and CTCAE grades were recorded independently by F.T. and A.A., respectively. The Institutional Review Board of Shahid Beheshti University of Medical Sciences (SBMU) approved the study protocol. This study was performed in line with the principles of the Declaration of Helsinki, and the ethical committee of SBMU approved this study (approval number: IR.SBMU.RETECH.REC.1398.106). The reporting of this prospective study follows the STROBE checklist for cohort studies (available at: https://www.strobe-statement.org/checklists/).

Statistical methods

Categorical variables were summarized as numbers and percentages and were compared using the Chi-square test. Continuous variables were summarized using mean and standard deviation, and intergroup values were compared using the Mann-Whitney U test. The association of chemotherapy toxicities with CARG risk groups and KPS was evaluated using the Chi-square test. To this end, KPS scores were categorized into 70 and lower, 80, and 90–100 groups. The validity of the CARG model was evaluated by composing Receiver Operating Characteristic (ROC) curves and calculating its area under the curve (AUC). A similar analysis was done for KPS for comparison. All tests were two-sided, and the statistical significance was set to 0.05. We used IBM SPSS Statistics® (ver.26) for statistical analysis.

Results

Participants and treatment characteristics

Between November 2019 and May 2021, 207 patients with cancer were assigned to a new chemotherapy regimen at our center. Among 84 patients who fulfilled the eligibility criteria, 8 patients were missed for analysis: 6 missed the follow-up, and 2 did not consent to participate (Figure 1).

Figure 1: STROBE flow diagram.
Figure 1:

STROBE flow diagram.

This study examined 76 patients across 456 chemotherapy cycles. The study population had a mean age of 71.1 ± 5.9 years, of which 31 patients (40.8 %) were female. The three most common malignancies were head and neck cancers (24 cases, 31.6 %), gastrointestinal cancers (17 cases, 22.3 %), and breast cancer (10 cases, 13.1 %). Most patients were in the advanced stage (80.2 %), with stage IV disease in 46 patients (60.5 %). The baseline characteristics are detailed in Table 2. Most patients received first-line chemotherapy (62 patients, 78.9 %) with a polychemotherapy regimen (59 patients, 77.6 %) and reduced doses at the first cycle (39 patients, 51.3 %), according to physician discretion. Sixteen patients (21.0 %) required growth factor support during the chemotherapy course, which started in the initial cycle for 12 patients (75.0 %).

Table 2:

Baseline characteristics and treatment details of the study population.

n, %a Patients with any toxicity n (%)a, (%)b p-Valuec Patients with hematologic toxicity n (%)a, (%)b p-Valuec Patients with nonhematologic toxicity n (%)a, (%)b p-Valuec
Characteristics
Total 76 n=18 (23.6) n=14 (18.4) n=7 (9.2)
Age at diagnosis, years 0.75 0.40 0.41
Mean ( ± SD) 71.1 ( ± 5.9) 71.3 ( ± 6.1) 72.4 ( ± 7.1) 70.8 ( ± 4.2)
Range 65–88 65–88 65–88 65–88
≤69d, n, % 44 (57.9) 10 (22.7), (55.5) 6 (13.6), (42.8) 5 (11.3), (71.4)
>69, n, % 32 (42.1) 8 (25.0), (45.5) 8 (25.0), (57.2) 2 (6.2), (28.6)
Sex, n, % 0.85 0.66 0.90
Female 31 (40.8) 7 (22.6), (38.9) 5 (16.1), (35.7) 3 (9.6), (42.9)
Male 45 (59.2) 11 (24.4), (61.1) 9 (20.0), (64.3) 4 (8.9), (57.1)
Educatione 0.22 0.73 0.004
Preliminary (ISCED 0–2) 37 (75.5) 7 (18.9), (58.3) 6 (16.2), (66.7) 1 (2.7), (20.0)
Intermediate (ISCED 3–5) 7 (14.3) 3 (42.8), (25.0) 2 (28.5), (22.2) 3 (42.9), (60.0)
Advanced (ISCED 6–8) 5 (10.2) 2 (40.0), (16.7) 1 (20.0), (11.1) 1 (20.0), (20.0)
NR 27 6 5 2
Marital status 0.42 0.49 0.64
Married 74 (97.3) 18 (24.3), (100) 14 (18.9), (100) 7 (9.4), (100)
Single 2 (2.7) 0 0 0
Living 0.88 0.66 0.88
With wife/husband 43 (79.6) 9 (20.9), (69.2) 6 (13.9), (60.0) 5 (11.6), (100)
With children 3 (5.5) 1 (33.3), (7.7) 1 (33.3), (10.0) 0
Alone 8 (14.2) 3 (37.5), (23.1) 3 (37.5), (30.0) 0
NR 22 5 4 2
Cancer type 0.55 0.46 0.85
Head and neck 24 (31.6) 6 (25.0), (33.3) 5 (20.8), (35.7) 3 (12.5), (42.9)
GI 17 (22.3) 2 (11.7), (11.1) 1 (5.9), (7.1) 1 (5.9), (14.3)
Breast 10 (13.1) 2 (20.0), (11.1) 2 (20.0), (14.2) 0
Gyn 7 (9.2) 2 (28.5), (11.1) 1 (14.3), (7.1) 1 (14.3), (14.3)
Lung 6 (7.9) 3 (50.0), (16.7) 2 (33.3), (14.2) 1 (16.7), (14.3)
Other 12 (15.8) 3 (25.0), (16.7) 2 (16.7), (14.2) 1 (8.3), (14.3)
Clinical stage, n, % 0.37 0.21 0.48
Stage I 3 (3.9) 1 (33.3), (5.5) 1 (33.3), (7.1) 0
Stage II 12 (15.8) 3 (25.0), (16.7) 3 (25.0), (21.4) 0
Stage III 15 (19.7) 1 (6.7), (5.5) 0 1 (6.7), (14.3)
Stage IV 46 (60.5) 13 (28.2), (72.2) 10 (21.7), (71.5) 6 (13.0), (85.7)
Treatment
CTx regimen 0.87 0.82 0.81
TC 32 (42.1) 9 (28.1), (50.0) 6 (18.7), (42.8) 5 (15.6), (71.4)
FOLFOX 10 (13.1) 2 (20.0), (11.1) 1 (10.0), (7.1) 1 (10.0), (14.3)
AC 6 (7.9) 2 (33.3), (11.1) 2 (33.3), (14.2) 0
GC 4 (5.2) 1 (25.0), (5.5) 1 (25.0), (7.1) 0
FOLFIRI 3 (3.9) 0 0 0
CF 3 (3.9) 1 (33.3), (5.5) 1 (33.3), (7.1) 0
Capecitabine 3 (3.9) 0 0 0
Paclitaxel 2 (2.6) 0 0 0
Other 15 (19.7) 3 (20.0), (16.6) 3 (20.0), (21.4) 1 (6.7), (14.3)
No. of CTx drugs 0.50 0.92 0.13
Polychemotherapy 59 (77.6) 15 (25.4), (83.3) 11 (52.4), (78.6) 7 (33.3), (100)
Single agent 17 (22.4) 3 (17.6), (16.7) 3 (5.4), (21.5) 0
CTx dose (1st cycle) 0.13 0.09 0.63
Reduced dose 39 (51.3) 12 (30.7), (66.7) 10 (25.6), (71.4) 3 (7.7), (42.9)
Standard dose 37 (48.7) 6 (16.2), (33.3) 4 (10.8), (28.6) 4 (10.8), (57.1)
CTx line 0.36 0.65 0.18
First line 62 (78.9) 16 (25.8), (88.9) 12 (19.3), (85.8) 7 (11.3), (100)
>First line 14 (21.1) 2 (14.2), (11.1) 2 (14.3), (14.2) 0
Growth factor use 0.76 0.13 0.54
No 60 (79.0) 13 (21.7), (72.2) 9 (15.0), (64.3) 6 (10.0), (85.7)
Yes 16 (21.0) 5 (31.2), (27.8) 5 (31,2), (35.7) 1 (6.2), (14.3)

  1. AC, adriamycin plus cyclophosphamide; CF, cisplatin plus fluorouracil; CTx, chemotherapy; FOLFIRI, folinic acid, fluorouracil, plus irinotecan; FOLFOX, folinic acid, fluorouracil, plus oxaliplatin; GC, gemcitabine plus cisplatin; GI, gastrointestinal; Gyn, gynecologic; ISCED, international standard classification of education; NR, not reported; TC, weekly paclitaxel (taxol) plus carboplatin. a% of the relevant group (of reported data) (i.e. horizontal order). b% of the toxicity group (of reported data) (i.e. vertical order). cUsing Pearson Chi-Square test. dThe cutoff is the median value of total cohort. eAccording to international standard classification of education (ISCED) 2011. Note: toxicity data pertains to grades 3–4.

In addition, Table 2 details the distribution of variables based on chemotherapy toxicities. Overall, 18 patients (23.6 %) experienced high-grade toxicity, which was hematologic toxicity in 11 patients (14.4 %), nonhematologic toxicity in 4 patients (5.2 %), and both toxicities in 3 patients (3.9 %). All patients with nonhematologic toxicity received first-line chemotherapy with a multidrug regimen, and 3 patients who had both hematologic and nonhematologic toxicities had stage IV disease. Hematologic toxicities were nonsignificantly more common in patients who had received AC and CF (cisplatin plus fluorouracil) regimens (33.3 %, p=0.82). For other variables, the association analysis demonstrated that neither of the evaluated variables was significantly associated with chemotherapy toxicity (p>0.05); except for patients with intermediate education grades (according to International Standard Classification of Education, level 3–5) who had a higher chance for nonhematologic toxicities (p=0.004).

Chemotherapy toxicity

Table 3 demonstrates the type and number of toxicities in the overall cohort. The most common hematologic toxicities were leukopenia (11 patients, 14.4 %) in the form of neutropenia (10 cases, 13.1 %) and anemia (6 patients, 7.9 %), respectively. Of 10 patients with neutropenia, 8 (80 %) were respondents to growth factor support. In this cohort, severe chemotherapy-induced thrombocytopenia (grade 3–5) was not detected. Severe nonhematologic toxicities were limited to neuropathy, oral mucositis, and diarrhea, and no grade 3–5 toxicities were detected for fatigue, nausea, dehydration, thrombosis, syncope, and electrolyte imbalance. Like hematologic toxicity, most nonhematologic toxicities were in grade 3 (85.7 %). All 4 patients who experienced severe (grade 3, 4) peripheral neuropathy have been treated with a TC regimen. Grade 3–4 peripheral neuropathy was detected in 4 out of 35 patients who had received the TC regimen (11.4 %, p=0.01). No peripheral neuropathy was detected in 3 patients who received docetaxel or nab-paclitaxel and 2 patients who received paclitaxel as monotherapy. Three patients with oral mucositis had rectal cancer receiving the FOLFOX regimen, metastatic laryngeal cancer receiving the TC regimen, and brain primitive neuroectodermal tumor (PNET) receiving Vincristine, Adriamycin, plus Cyclophosphamide, followed by Ifosfamide plus Etoposide (VAC-IE) regimen. Both patients who reported diarrhea during chemotherapy were treated with a TC regimen (p=0.09).

Table 3:

Toxicity distribution in the overall cohort.

Grades 3–4 n, %a Grade 3 n (%)a, n (%)b Grade 4c n (%)a, n (%)b
Hematologic toxicity n=14 n=9 n=5
Type
Neutropenia
Lymphopenia 10 (13.1) 9 (11.8), (90.0) 1 (1.3), (10.0)
Anemia 1 (1.3) 1 (1.3), (100) 0
Thrombocytopenia 6 (7.9) 6 (7.9), (100) 0
0 0 0
Number
1 4 (5.2) 4 (5.2), (100) 0
2 7 (9.2) 4 (5.2), (57.1) 3 (42.9)
3 3 (3.9) 1 (1.3), (33.3) 2 (66.7)
Nonhematologic toxicity n = 7 n = 6 n = 1
Type
Peripheral neuropathy 4 (5.2) 3 (3.9), (75.0) 1 (1.3), (25.0)
Oral mucositis d 3 (3.9) 3 (3.9), (100) 0
Diarrhea e 2 (2.6) 1 (1.3), (50.0) 1 (1.3), (50.0)
Number
1 3 (3.9) 3 (3.9), (100) 0
2 2 (2.6) 1 (1.3), (50.0) 1 (1.3), (50.0)
3 0 0 0

  1. a% of the total cohort, b% of the toxicity group (i.e., vertical order), cAt least one grade 4 toxicity, dNo patient received concurrent radiotherapy to head and neck region. eNo patient received concurrent radiotherapy to abdominopelvic region.

The association of chemotherapy toxicities with the CARG score and KPS

Table 4 presents the association of chemotherapy toxicities with CARG score and physician-rated KPS. Concerning the CARG model, there was no significant difference in the incidence of chemotherapy toxicities across the risk groups (p>0.05)._ Concerning the KPS score, the single patient with KPS 70 was excluded from the analysis. Collectively, patients with KPS 80 were more prone to chemotherapy toxicities compared to KPS 90–100 (75 % vs. 21 % respectively, p=0.04). There was no significant difference in the incidence of hematologic toxicity across the KPS risk groups (p=0.55); however, patients with KPS 80 had a significantly higher chance for nonhematologic toxicities compared to KPS 90–100 (50 % vs. 7 %, respectively, p=0.01). Four out of 5 patients who had KPS 90–100 and developed nonhematologic toxicities (80 %) received standard-dose chemotherapy in the first cycle. Both patients with KPS 80 developed grade 3 peripheral neuropathy even with a reduced-dose TC regimen in the first cycle.

Table 4:

Ability of CARG score vs. physician-rated KPS to predict chemotherapy toxicity.

Overall cohort n=76 n, %a Total toxicity n=18 n (%)a, (%)b, (%)c p-Valued AUCe Hematologic toxicity n=14 n (%)a, (%)b, (%)c p-Valued AUCe Nonhematologic toxicity n=7 n (%)a, (%)b, (%)c p-Valued AUCe
CARG score 0.32 0.56 0.19 0.67 0.86 0.39
Low (0–5) 26 (34.2) 4 (5.2), (15.4), (22.2) 2 (2.6), (7.7), (14.2) 3 (3.9), (11.5), (42.9)
Intermediate (6–9) 35 (46.1) 11 (14.5), (31.4), (61.1) 9 (11.8), (25.7), (64.3) 3 (3.9), (8.6), (42.9)
High (10–19) 15 (19.7) 3 (3.9), (20.0) (16.7) 3 (3.9), (20.0), (21.4) 1 (1.3), (6.7), (14.2)
KPS 0.04 0.56 0.22 0.55 0.01 0.61
90–100 71 (93.4) 15 (19.7), (21.1), (83.3) 12 (15.8), (16.9), (85.7) 5 (6.5), (7.1), (71.4)
80 4 (5.2) 3 (3.9), (75.0), (16.7) 2 (2.6), (50.0), (14.3) 2 (2.6), (50.0), (28.6)
70 1 (1.3) 0 0 0

  1. AUC, area under curve; CARG, cancer aging research group; KPS, Karnofsky performance status. a% of the total cohort, b% of the CARG or KPS group (i.e., horizontal order), c% of the toxicity group (i.e., vertical order), dusing Pearson Chi-Square test, ethe area under the receiver operation characteristic (ROC) curve.

The AUC-ROC of the two models were similar for total chemotherapy toxicities (CARG: 0.562, 95 % CI 0.40–0.69 vs. KPS: 0.565, 95 % CI 0.40–0.72). However, subanalysis revealed that AUC-ROC of the KPS model is larger for nonhematologic toxicity (KPS: 0.61 [95 % CI 0.37–0.86] vs. CARG: 0.39 [95 % CI 0.21–0.66]), and AUC-ROC of CARG model is larger for hematologic toxicities (CARG: 0.67 [95 % CI 0.48–0.78] vs. KPS: 0.55 [95 % CI 0.37–0.72]). At the best cutoff point equal to 7, the sensitivity and specificity of the CARG model for hematologic toxicity were 0.69 and 0.63, respectively (Table 4 and Figure 2). As noted earlier, four patients required G-CSF support during the chemotherapy course. Among these, no one was CARG-low risk (one high risk and three intermediate risk).

Figure 2: ROC curves of CARG and KPS models for predicting total (A), hematologic (B), and nonhematologic (C) chemotherapy toxicities in older patients. Abbreviations: AUC, area under curve; CARG, cancer aging research group; KPS, Karnofsky performance status; ROC, receiver operating characteristic.
Figure 2:

ROC curves of CARG and KPS models for predicting total (A), hematologic (B), and nonhematologic (C) chemotherapy toxicities in older patients. Abbreviations: AUC, area under curve; CARG, cancer aging research group; KPS, Karnofsky performance status; ROC, receiver operating characteristic.

Discussion

In this cohort, the rate of chemotherapy-related toxicities was 23.6 %, more in the form of hematologic toxicity (18.4 %). The most common hematologic and nonhematologic toxicities were leukopenia (11.8 %) and peripheral neuropathy (5.2 %), respectively. Four patients who required G-CSF support amid the chemotherapy course were intermediate-high risk per the CARG model. The CARG model’s ability to predict hematologic toxicity was acceptable (AUC-ROC=0.67); however, it had poor discrimination in predicting total and nonhematologic toxicities (AUC-ROC=0.56 and 0.39, respectively) [29]. Compared with the CARG model, physician-rated KPS has a similar value for predicting total chemotherapy toxicity (AUC-ROC=0.56), poor discrimination for hematologic toxicity (AUC-ROC=0.55 vs. 0.67), and acceptable discrimination for nonhematologic toxicity (AUC-ROC=0.61 vs. 0.39).

Compared to the main CARG and the Indian validation studies, we recorded significantly lower rates of chemotherapy-related toxicity. The total grade 3–5 toxicity rate was 23 % vs. 53 % in the CARG development cohort, 58 % in the CARG validation cohort, and 52 % in the Indian cohort. We found no grade 5 toxicity; however, it was reported in 2 % and 4 % of patients in the main CARG and Indian studies, respectively. Dislike the main CARG study, the nonhematologic toxicities in this cohort were limited to diarrhea, neuropathy, and oral mucositis, and no grade 3–5 toxicities were recorded for fatigue, nausea, dehydration, thrombosis, syncope, and electrolytes imbalance. In the CARG, Indian, and Japanese cohorts, the risk of toxicity was increased with an increasing CARG risk score. However, the current study did not reflect this correlation in the evaluated Iranian patients. In contrast to the current study, in the development and validation CARG cohorts as well as the Indian cohort, CARG score had acceptable discrimination to predict total chemotherapy toxicity (AUC-ROC: 0.56 vs. 0.72, 0.65, and 0.63, respectively). On the other hand, the AUC-ROC of physician-rated KPS was similar and not satisfactory in predicting total toxicity in the current and the development CARG, validation CARG, and Indian cohorts (0.56 vs. 0.53, 0.54, and 0.52, respectively). In contrast to the current study, the Japanese cohort found that the CARG model was predictive for nonhematologic toxicity but not for hematologic toxicity [26].

The discrepancy between the main CARG/Indian/Japanese cohorts and current study findings is likely multifactorial. First, participants in the current study had younger mean age compared with the CARG development and CARG validation studies). It has been established that chemotherapy-related toxicity increases with age [8]. Second, the percentage of women in the current study was lower than in CARG development and validation studies (40 % vs. 56 % and 55 %). Strong evidence has noted that women are more susceptible to chemotherapy toxicities, including hematologic toxicities (e.g., anemia, leukopenia, and neutropenia) and nonhematologic toxicities (e.g., nausea and vomiting, constipation, stomatitis, and alopecia) [30], [31], [32], [33]. Third, fewer patients in the present cohort received standard-dose chemotherapy at the initial cycle (48 % vs. 76 % in the main CARG and Indian validation studies and 70 % in the Japanese cohort). Fourth, the most applied chemotherapy regimen in the current study was weekly paclitaxel/carboplatin, which is a well-tolerated regimen [34]. In addition, weekly patient visits for clinical examination and toxicity evaluation during the chemotherapy course can reduce the rate of chemotherapy toxicity. Although no subanalysis was done, this point might be considered as the possible contributing factor to fewer toxicities in this cohort. Fifth, participants in the CARG studies were mainly White (85 %); however, the current study evaluated chemotherapy toxicity in Iranian patients of a different race from the Middle East and North Africa region. Increasing evidence has put forward the patient’s race as a predictive factor of chemotherapy toxicity, which can be mediated by genetic and epigenetic factors [20], [21], [22], [23]. A pooled analysis of clinical trials showed that cisplatin-based chemotherapy led to more neutropenia and anemia in Asian patients with lung cancer than non-Asian patients [21]. Another study found similar results for patients with breast cancer receiving FEC (fluorouracil, epirubicin, plus cyclophosphamide) regimen [20]. In a randomized trial on patients with colon cancer treated with 5-fluorouracil-based chemotherapy, Black patients experienced less nausea, vomiting, diarrhea, and oral mucositis than White patients [23]. These studies were a few examples to illustrate the importance of evaluating the chemotherapy toxicity prediction tools in populations of different races before their general application.

These differences might arise from the genetic polymorphisms between different races. For example, patients with lung cancer with MTHFR rs1801131 and MDM2 rs1690924 polymorphisms tend to experience more platinum-induced gastrointestinal toxicity. MTHFR rs1801133CT/TT carriers are prone to platinum-induced hematological toxicity [35]. In patients with cervical cancer, ERCC1 rs3212986 polymorphism is associated with cisplatin-induced gastrointestinal toxicity [36]. Therefore, genetic polymorphisms in different communities can be an independent factor in developing chemotherapy toxicity.

Overall, CARG has several limitations. For example, it cannot precisely identify each risk factor for chemotherapy toxicity as a CGA can. Besides, CARG is merely a diagnostic tool. However, CGA is made to precisely identify the frailties of patients and, above all, to set up specific geriatric interventions in the face of the identified elements of fragility to correct the reversible factors, which promote toxicity, to limit their risk of toxicity, and thus to allow the treatment feasibility [18, 37]. The comparison between the current and previous studies reveal that CARG (and possibly other) chemotherapy prediction tool requires validation before its clinical application. Besides, this cohort had several learning points for the clinical practice of physicians in Iran. First, we realized that the CARG model for Iranian patients was predictive only for hematologic toxicity. Second, the cut-off point to distinguish low-risk patients for hematologic toxicities is 6 instead of 5 in the main study. Third, in this cohort, 39 patients received reduced-dose chemotherapy at the initial cycle based on the physician’s clinical judgment. In spite of a reduced dose, 30 % of patients experienced chemotherapy toxicity. This notion illustrates the importance of improving clinical judgment and developing a rationally-designed prediction tool for our clinical practice.

These findings should be interpreted in light of the current study’s limitations: First, several interpreting factors were not analyzed in this study, including comorbidities, medications, nutritional status, and psychological state, all may affect the toxicities; second, this study did not evaluate the patients’ ethnicity.; third, the data of this study were per the real-world management of patients. Hence, there were cases who required growth factor support from the beginning due to intensive chemotherapy regimens (12 patients, 15.7 %) or during the course of chemotherapy due to the development of neutropenia (4 patients, 5.2 %). This inevitable issue can affect the study results. Despite these limitations, our study possesses several strengths: First, the prospective design. Second, separate analysis and report per hematologic and nonhematologic toxicities. Third, the clinical evaluation of patients by a single physician to improve the internal validity. In the main study, patients were reviewed for chemotherapy toxicities by two physicians, which can lead to inter-observer bias. This study was conducted to examine the CARG model in Iranian patients, not to validate it. We are aiming to continue this study to evaluate the validity of this model with more participants.

Conclusions

This prospective cohort was designed to examine the reproducibility of the CARG prediction tool in older Iranian patients with cancer. We found that the CARG score had an acceptable ability to predict hematologic toxicities; however, its efficacy for total and nonhematologic toxicities was limited. This issue reflects the importance of evaluating chemotherapy prediction tools in different populations and, if required, developing rationally designed, race-specific toxicity prediction models to improve toxicity prediction in patients with cancer. Future studies with larger sample sizes and patients from different races are invited to delineate this notion. Also, meta-analyses of available studies can be helpful to this end.

Corresponding author: Farzad Taghizadeh-Hesary, ENT and Head and Neck Research Center and Department, The Five Senses Health Institute, School of Medicine, Iran University of Medical Sciences, Tehran, Iran; and Department of Radiation Oncology, Iran University of Medical Sciences, Tehran, Iran, E-mail:

Funding source: Clinical Research Development Unit of Imam Hossein Educational Hospital

Funding source: Orchid Pharmed pharmaceutical company

Acknowledgment

Special thanks to the patients and their caregivers who took the time to participate in this study.

  1. Research funding: This study was funded by Clinical Research Development Unit of Imam Hossein Educational Hospital and Orchid Pharmed pharmaceutical company.

  2. Author contributions: Conceptualization: A.A., Methodology: A.A., F.T.H., Software: F.T., F.T.H., Validation: A.A., Formal analysis: F.T., Investigation: N.R., F.T., Resources: N.R., F.T., Data Curation: A.S., Writing-original draft: F.T.H., Writing-review & editing: A.A., F.T.H. F.T., Visualization: A.S., Supervision: A.A., Project administration: N/A, Funding acquisition: A.A. All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interest: The authors declare that they have no competing interests. The summary of this study has been presented as e-poster in ESMO 2022 (link: https://oncologypro.esmo.org/meeting-resources/esmo-congress/an-evaluation-of-cancer-aging-research-group-carg-score-to-predict-chemotherapy-toxicity-in-iranian-older-cancer-patients). And it was published in the Abstract book of the congress by Annals of Oncology (link: https:// www.annalsofoncology.org/article/S0923-7534(22)03547-5/fulltext).

  4. Informed consent: Informed consent was obtained from all individual participants included in the study.

  5. Ethics approval: This study was performed in line with the principles of the Declaration of Helsinki. The ethical committee of Shahid Beheshti University of Medical Sciences approved this study (approval number: IR.SBMU.RETECH.REC.1398.106).

References

1. Elmore, LW, Greer, SF, Daniels, EC, Saxe, CC, Melner, MH, Krawiec, GM, et al.. Blueprint for cancer research: critical gaps and opportunities. CA: Cancer J Clin 2021;71:107–39. https://doi.org/10.3322/caac.21652.Suche in Google Scholar PubMed

2. Moezian, GSA, Javadinia, SA, Sales, SS, Fanipakdel, A, Elyasi, S, Karimi, G. Oral silymarin formulation efficacy in management of AC-T protocol induced hepatotoxicity in breast cancer patients: a randomized, triple blind, placebo-controlled clinical trial. J Oncol Pharm Pract 2022;28:827–35. https://doi.org/10.1177/10781552211006182.Suche in Google Scholar PubMed

3. International Agency for Research on Cancer (IARC). Estimated number of new cases in 2020 2021. Available from: https://gco.iarc.fr/today/online-analysis-pie?v=2020&mode=population&mode_population=continents&population=900&populations=900&key=total&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=0&ages_group%5B%5D=17&nb_items=7&group_cancer=1&include_nmsc=0&include_nmsc_other=1&half_pie=0&donut=0.Suche in Google Scholar

4. International Agency for Research on Cancer (IARC). Iran fact sheet 2020. Available from: chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/viewer.html?pdfurl=https%3A%2F%2Fgco.iarc.fr%2Ftoday%2Fdata%2Ffactsheets%2Fpopulations%2F364-iran-islamic-republic-of-fact-sheets.pdf&clen=349353&chunk=true.Suche in Google Scholar

5. International Agency for Research on Cancer (IARC). Estimated number of incident cases Asia, Iran, Islamic Republic of, both sexes, ages 65+ 2020. Available from: https://gco.iarc.fr/today/online-analysis-multi-bars?v=2020&mode=cancer&mode_population=countries&population=900&populations=935_364&key=total&sex=0&cancer=39&type=0&statistic=5&prevalence=0&population_group=0&ages_group%5B%5D=13&ages_group%5B%5D=17&nb_items=10&group_cancer=1&include_nmsc=0&include_nmsc_other=1&type_multiple=%257B%2522inc%2522%253Atrue%252C%2522mort%2522%253Afalse%252C%2522prev%2522%253Afalse%257D&orientation=horizontal&type_sort=0&type_nb_items=%257B%2522top%2522%253Atrue%252C%2522bottom%2522%253Afalse%257D#collapse-others.Suche in Google Scholar

6. Manoochehry, S, Rasouli, HR. Iranian population policy and aging: new health concerns. Int J Travel Med Glob Health 2017;5:70–1. https://doi.org/10.15171/ijtmgh.2017.14.Suche in Google Scholar

7. Hurria, A, Mohile, S, Gajra, A, Klepin, H, Muss, H, Chapman, A, et al.. Validation of a prediction tool for chemotherapy toxicity in older adults with cancer. J Clin Oncol 2016;34:2366–71. https://doi.org/10.1200/jco.2015.65.4327.Suche in Google Scholar PubMed PubMed Central

8. Versteeg, K, Konings, I, Lagaay, A, van de Loosdrecht, A, Verheul, H. Prediction of treatment-related toxicity and outcome with geriatric assessment in elderly patients with solid malignancies treated with chemotherapy: a systematic review. Ann Oncol 2014;25:1914–8. https://doi.org/10.1093/annonc/mdu052.Suche in Google Scholar PubMed

9. Nguyen, NP, Ali, A, Vinh-Hung, V, Gorobets, O, Chi, A, Mazibuko, T, et al.. Stereotactic body radiotherapy and immunotherapy for older patients with oligometastases: a proposed paradigm by the international geriatric radiotherapy group. Cancers 2022;15:244. https://doi.org/10.3390/cancers15010244.Suche in Google Scholar PubMed PubMed Central

10. Cameron, D. England’s 30-day chemotherapy mortality: a measure of quality of care? Lancet Oncol 2016;17:1172–3. https://doi.org/10.1016/s1470-2045(16)30405-3.Suche in Google Scholar PubMed

11. Hurria, A, Togawa, K, Mohile, SG, Owusu, C, Klepin, HD, Gross, CP, et al.. Predicting chemotherapy toxicity in older adults with cancer: a prospective multicenter study. J Clin Oncol 2011;29:3457–65. https://doi.org/10.1200/jco.2011.34.7625.Suche in Google Scholar

12. Kelly, CM, Shahrokni, A. Moving beyond Karnofsky and ECOG performance status assessments with new technologies. J Oncol 2016;2016:6186543–13. https://doi.org/10.1155/2016/6186543.Suche in Google Scholar PubMed PubMed Central

13. Hsu, T, Chen, R, Lin, SC, Djalalov, S, Horgan, A, Le, LW, et al.. Pilot of three objective markers of physical health and chemotherapy toxicity in older adults. Curr Oncol 2015;22:385–91. https://doi.org/10.3747/co.22.2623.Suche in Google Scholar PubMed PubMed Central

14. Jiang, S, Li, P. Current development in elderly comprehensive assessment and research methods. BioMed Res Int 2016;2016:3528248–10. https://doi.org/10.1155/2016/3528248.Suche in Google Scholar PubMed PubMed Central

15. Kenis, C, Bron, D, Libert, Y, Decoster, L, Van Puyvelde, K, Scalliet, P, et al.. Relevance of a systematic geriatric screening and assessment in older patients with cancer: results of a prospective multicentric study. Ann Oncol 2013;24:1306–12. https://doi.org/10.1093/annonc/mds619.Suche in Google Scholar PubMed

16. Ward, KT, Reuben, DB. Comprehensive geriatric assessment. Waltham, MA: UpToDate; 2016:13–8 pp.Suche in Google Scholar

17. Mohile, SG, Dale, W, Somerfield, MR, Schonberg, MA, Boyd, CM, Burhenn, PS, et al.. Practical assessment and management of vulnerabilities in older patients receiving chemotherapy: ASCO guideline for geriatric oncology. J Clin Oncol 2018;36:2326–47. https://doi.org/10.1200/jco.2018.78.8687.Suche in Google Scholar

18. Tarchand, GR, Morrison, V, Klein, MA, Watkins, E. Use of comprehensive geriatric assessment in oncology patients to guide treatment decisions and predict chemotherapy toxicity. Fed Pract 2021;38:S22–8. https://doi.org/10.12788/fp.0128.Suche in Google Scholar PubMed PubMed Central

19. Cavdar, E, Iriagac, Y, Karaboyun, K, Avci, O, Seber, ES. Prospective comparison of the value of CARG, G8, and VES-13 toxicity tools in predicting chemotherapy-related toxicity in older Turkish patients with cancer. J Geriatr Oncol 2022;13:821–7. https://doi.org/10.1016/j.jgo.2022.03.004.Suche in Google Scholar PubMed

20. Han, HS, Reis, IM, Zhao, W, Kuroi, K, Toi, M, Suzuki, E, et al.. Racial differences in acute toxicities of neoadjuvant or adjuvant chemotherapy in patients with early-stage breast cancer. Eur J Cancer 2011;47:2537–45. https://doi.org/10.1016/j.ejca.2011.06.027.Suche in Google Scholar PubMed

21. Hasegawa, Y, Kawaguchi, T, Kubo, A, Ando, M, Shiraishi, J, Isa, S-i, et al.. Ethnic difference in hematological toxicity in patients with non-small cell lung cancer treated with chemotherapy: a pooled analysis on Asian versus non-Asian in phase II and III clinical trials. J Thorac Oncol 2011;6:1881–8. https://doi.org/10.1097/jto.0b013e31822722b6.Suche in Google Scholar

22. Loh, M, Chua, D, Yao, Y, Soo, R, Garrett, K, Zeps, N, et al.. Can population differences in chemotherapy outcomes be inferred from differences in pharmacogenetic frequencies? Pharmacogenomics J 2013;13:423–9. https://doi.org/10.1038/tpj.2012.26.Suche in Google Scholar PubMed

23. McCollum, AD, Catalano, PJ, Haller, DG, Mayer, RJ, Macdonald, JS, Benson, ABIII, et al.. Outcomes and toxicity in african-american and caucasian patients in a randomized adjuvant chemotherapy trial for colon cancer. J Natl Cancer Inst 2002;94:1160–7. https://doi.org/10.1093/jnci/94.15.1160.Suche in Google Scholar PubMed

24. O’Donnell, PH, Dolan, ME. Cancer pharmacoethnicity: ethnic differences in susceptibility to the effects of chemotherapy. Clin Cancer Res 2009;15:4806–14. https://doi.org/10.1158/1078-0432.ccr-09-0344.Suche in Google Scholar PubMed PubMed Central

25. Ostwal, V, Ramaswamy, A, Bhargava, P, Hatkhambkar, T, Swami, R, Rastogi, S, et al.. Cancer Aging Research Group (CARG) score in older adults undergoing curative intent chemotherapy: a prospective cohort study. BMJ open 2021;11:e047376. https://doi.org/10.1136/bmjopen-2020-047376.Suche in Google Scholar PubMed PubMed Central

26. Suto, H, Inui, Y, Okamura, A. Validity of the cancer and aging research group predictive tool in older Japanese patients. Cancers 2022;14:2075. https://doi.org/10.3390/cancers14092075.Suche in Google Scholar PubMed PubMed Central

27. Maghbouleh, N, Schachter, A, Flores, RD. Middle Eastern and North African Americans may not be perceived, nor perceive themselves, to be White. Proc Natl Acad Sci USA 2022;119:e2117940119. https://doi.org/10.1073/pnas.2117940119.Suche in Google Scholar PubMed PubMed Central

28. National Cancer Institute. Common terminology criteria for adverse events (CTCAE) version 5.0; 2017. Available from https://ctep.cancer.gov/protocoldevelopment/electronic_applications/docs/CTCAE_v5_Quick_Reference_8.5x11.pdf.Suche in Google Scholar

29. Yang, S, Berdine, G. The receiver operating characteristic (ROC) curve. Southwest Respir Crit Care Chron 2017;5:34–6. https://doi.org/10.12746/swrccc.v5i19.391.Suche in Google Scholar

30. Cristina, V, Mahachie, J, Mauer, M, Buclin, T, Van Cutsem, E, Roth, A, et al.. Association of patient sex with chemotherapy-related toxic effects: a retrospective analysis of the PETACC-3 trial conducted by the EORTC gastrointestinal group. JAMA Oncol 2018;4:1003–6. https://doi.org/10.1001/jamaoncol.2018.1080.Suche in Google Scholar PubMed PubMed Central

31. Klimm, B, Reineke, T, Haverkamp, H, Behringer, K, Eich, HT, Josting, A, et al.. Role of hematotoxicity and sex in patients with Hodgkin’s lymphoma: an analysis from the German Hodgkin Study Group. J Clin Oncol 2005;23:8003–11. https://doi.org/10.1200/jco.2005.205.60.Suche in Google Scholar

32. Singh, S, Parulekar, W, Murray, N, Feld, R, Evans, WK, Tu, D, et al.. Influence of sex on toxicity and treatment outcome in small-cell lung cancer. J Clin Oncol 2005;23:850–6. https://doi.org/10.1200/jco.2005.03.171.Suche in Google Scholar PubMed

33. van den Berg, H, Paulussen, M, Le Teuff, G, Judson, I, Gelderblom, H, Dirksen, U, et al.. Impact of gender on efficacy and acute toxicity of alkylating agent-based chemotherapy in Ewing sarcoma: secondary analysis of the Euro-Ewing99-R1 trial. Eur J Cancer 2015;51:2453–64. https://doi.org/10.1016/j.ejca.2015.06.123.Suche in Google Scholar PubMed

34. van der Burg, MEL, Vergote, I, Onstenk, W, Boere, IA, Leunen, K, van Montfort, CAGM, et al.. Long-term results of weekly paclitaxel carboplatin induction therapy: an effective and well-tolerated treatment in patients with platinum-resistant ovarian cancer. Eur J Cancer 2013;49:1254–63. https://doi.org/10.1016/j.ejca.2012.11.027.Suche in Google Scholar PubMed

35. Liu, W, Wang, Y, Luo, J, Yuan, H, Luo, Z. Genetic polymorphisms and platinum-based chemotherapy-induced toxicities in patients with lung cancer: a systematic review and meta-analysis. Front Oncol 2020;9:1573. https://doi.org/10.3389/fonc.2019.01573.Suche in Google Scholar PubMed PubMed Central

36. Soares, S, Nogueira, A, Coelho, A, Assis, J, Pereira, D, Bravo, I, et al.. Relationship between clinical toxicities and ERCC1 rs3212986 and XRCC3 rs861539 polymorphisms in cervical cancer patients. Int J Biol Markers 2018;33:116–23. https://doi.org/10.5301/ijbm.5000279.Suche in Google Scholar PubMed

37. Lee, H, Lee, E, Jang, IY. Frailty and comprehensive geriatric assessment. J Kor Med Sci 2020;35:e16. https://doi.org/10.3346/jkms.2020.35.e16.Suche in Google Scholar PubMed PubMed Central

Received: 2023-03-05
Accepted: 2023-04-27
Published Online: 2023-05-11

© 2023 the author(s), published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Experience of patients with metastatic breast cancer in France: results of the 2021 RÉALITÉS survey and comparison with 2015 results
  4. An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer
  5. Anemarrhenasaponin I suppresses ovarian cancer progression via inhibition of SHH signaling pathway
  6. Early diagnosis and prognosis of hepatocellular carcinoma based on a ceRNA array
  7. Treatment with camrelizumab plus tyrosine kinase inhibitors with or without TACE for intermediate-advanced hepatocellular carcinoma: a clinical efficacy and safety study
  8. CISD2 protects against Erastin induced hepatocellular carcinoma ferroptosis by upregulating FSP1
  9. Screening and biomarker assessment of ferroptosis genes FLT3 and ALOX5 in lung adenocarcinoma
  10. Anoikis-related gene signature as novel prognostic biomarker to predict immunotherapy with bladder urothelial carcinoma
  11. Effects of stress response induced by laparoscopic colectomy and laparotomy on TLR-mediated innate immune responses in colon cancer patients
  12. CSF-1R promotes vasculogenic mimicry via epithelial-mesenchymal transition in nasopharyngeal carcinoma cells
  13. Case Report
  14. Anaplastic extramedullary plasmacytoma resistant to novel therapies: a case report
  15. Miscellaneous
  16. A summary of the second MACR international scientific conference (2nd MSC)
https://doi.org/10.1515/oncologie-2023-0096
Schlagwörter für diesen Artikel
cancer; chemotherapy; geriatric assessment; geriatric oncology
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Experience of patients with metastatic breast cancer in France: results of the 2021 RÉALITÉS survey and comparison with 2015 results
  4. An evaluation of cancer aging research group (CARG) score to predict chemotherapy toxicity in older Iranian patients with cancer
  5. Anemarrhenasaponin I suppresses ovarian cancer progression via inhibition of SHH signaling pathway
  6. Early diagnosis and prognosis of hepatocellular carcinoma based on a ceRNA array
  7. Treatment with camrelizumab plus tyrosine kinase inhibitors with or without TACE for intermediate-advanced hepatocellular carcinoma: a clinical efficacy and safety study
  8. CISD2 protects against Erastin induced hepatocellular carcinoma ferroptosis by upregulating FSP1
  9. Screening and biomarker assessment of ferroptosis genes FLT3 and ALOX5 in lung adenocarcinoma
  10. Anoikis-related gene signature as novel prognostic biomarker to predict immunotherapy with bladder urothelial carcinoma
  11. Effects of stress response induced by laparoscopic colectomy and laparotomy on TLR-mediated innate immune responses in colon cancer patients
  12. CSF-1R promotes vasculogenic mimicry via epithelial-mesenchymal transition in nasopharyngeal carcinoma cells
  13. Case Report
  14. Anaplastic extramedullary plasmacytoma resistant to novel therapies: a case report
  15. Miscellaneous
  16. A summary of the second MACR international scientific conference (2nd MSC)
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