Dataset Information

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.

ABSTRACT:

Background

Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT.

Methods

A total of 298 eligible patients were randomly assigned into the training cohort and validation cohort with a ratio 2:1. An integrated feature selection and model training approach using support vector machine combined with genetic algorithm was performed to predict 3-year overall survival (OS). Patients were stratified into the high-risk and low-risk group based on the predicted survival status. Pulmonary function test and blood gas analysis indicators were associated with radiomic features. Dynamic changes of peripheral blood lymphocytes counts before and after CCRT had been documented.

Results

Nine features including 5 tumor-related features and 4 pulmonary features were selected in the predictive model. The areas under the receiver operating characteristic curve for the training and validation cohort were 0.965 and 0.869, and were reduced by 0.179 and 0.223 when all pulmonary features were excluded. Based on radiomics-derived stratification, the low-risk group yielded better 3-year OS (68.4% vs. 3.3%, p < 0.001) than the high-risk group. Patients in the low-risk group had better baseline FEV1/FVC% (96.3% vs. 85.9%, p = 0.046), less Grade ≥ 3 lymphopenia during CCRT (63.2% vs. 83.3%, p = 0.031), better recovery of lymphopenia from CCRT (71.4% vs. 27.8%, p < 0.001), lower incidence of Grade ≥ 2 radiation-induced pneumonitis (31.6% vs. 53.3%, p = 0.040), superior tumor remission (84.2% vs. 66.7%, p = 0.003).

Conclusion

Pretreatment radiomics features from tumor and TOE could boost the long-term survival forecast accuracy in LANSCLC patients, and the predictive results could be utilized as an effective indicator for survival risk stratification. Low-risk patients might benefit more from radical CCRT and further adjuvant immunotherapy.

Trial registration

retrospectively registered.

SUBMITTER: Chen NB

PROVIDER: S-EPMC9667605 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.

Chen Nai-Bin NB Xiong Mai M Zhou Rui R Zhou Yin Y Qiu Bo B Luo Yi-Feng YF Zhou Su S Chu Chu C Li Qi-Wen QW Wang Bin B Jiang Hai-Hang HH Guo Jin-Yu JY Peng Kang-Qiang KQ Xie Chuan-Miao CM Liu Hui H

Radiation oncology (London, England) 20221116 1

<h4>Background</h4>Definitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT.<h4>Methods</h4>A total of 298 eligible patients were ...[more]

PMID: 36384755

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Project description:Background To investigate the magnetic resonance imaging (MRI)-based radiomics value in predicting the survival of patients with locally advanced cervical squamous cell cancer (LACSC) treated with concurrent chemoradiotherapy (CCRT). Methods A total of 185 patients (training group: n = 128; testing group: n = 57) with LACSC treated with CCRT between January 2014 and December 2018 were retrospectively enrolled in this study. A total of 400 radiomics features were extracted from T2-weighted imaging, apparent diffusion coefficient map, arterial- and delayed-phase contrast-enhanced MRI. Univariate Cox regression and least absolute shrinkage and selection operator Cox regression was applied to select radiomics features and clinical characteristics that could independently predict progression-free survival (PFS) and overall survival (OS). The predictive capability of the prediction model was evaluated using Harrell’s C-index. Nomograms and calibration curves were then generated. Survival curves were generated using the Kaplan-Meier method, and the log-rank test was used for comparison. Results The radiomics score achieved significantly better predictive performance for the estimation of PFS (C-index, 0.764 for training and 0.762 for testing) and OS (C-index, 0.793 for training and 0.750 for testing), compared with the 2018 FIGO staging system (C-index for PFS, 0.657 for training and 0.677 for testing; C-index for OS, 0.665 for training and 0.633 for testing) and clinical-predicting model (C-index for PFS, 0.731 for training and 0.725 for testing; C-index for OS, 0.708 for training and 0.693 for testing) (P < 0.05). The combined model constructed with T stage, lymph node metastasis position, and radiomics score achieved the best performance for the estimation of PFS (C-index, 0.792 for training and 0.809 for testing) and OS (C-index, 0.822 for training and 0.785 for testing), which were significantly higher than those of the radiomics score (P < 0.05). Conclusions The MRI-based radiomics score could provide effective information in predicting the PFS and OS in patients with LACSC treated with CCRT. The combined model (including MRI-based radiomics score and clinical characteristics) showed the best prediction performance. Supplementary Information The online version contains supplementary material available at 10.1186/s40644-022-00474-2.

Project description:PurposeInduction chemotherapy plus concurrent chemoradiotherapy and concurrent chemoradiotherapy alone are both standard treatment regimens for managing locally advanced nasopharyngeal carcinoma. However, the results of comparisons between them in clinical trials vary. Therefore, we designed this meta-analysis to illustrate their advantages and disadvantages in patients with locally advanced nasopharyngeal carcinoma.MethodsWe thoroughly searched the PubMed, EMBASE, and Cochrane Library databases and then merged the effect indicators of hazard ratios and risk ratios using RevMan 5.1.ResultsSeven randomized controlled trials totaling 2,319 patients were included in our research. The synthesized results showed that induction chemotherapy plus concurrent chemoradiotherapy improved overall survival (HR = 0.75, 95% CI: 0.63-0.89, P = 0.001), progression-free survival (HR = 0.69, 95% CI: 0.60-0.80, P < 0.001), distant metastasis-free survival (HR = 0.65, 95% CI: 0.53-0.80, P < 0.001) and locoregional recurrence-free survival (HR = 0.68 95%, CI: 0.54-0.86, P = 0.001) versus concurrent chemoradiotherapy alone. It also increased the risk of anemia, thrombocytopenia, and neutropenia during concurrent chemoradiotherapy. However, the incidence of leukopenia and mucositis was similar in induction chemotherapy and induction chemotherapy plus concurrent chemoradiotherapy. Furthermore, the subgroup analysis showed better survival outcomes with induction chemotherapy plus concurrent chemoradiotherapy than with concurrent chemoradiotherapy alone in the triweekly cisplatin subgroup (all P < 0.01), whereas induction chemotherapy plus concurrent chemoradiotherapy could only improve progression-free survival and locoregional recurrence-free survival in the weekly cisplatin subgroup (HR = 0.78, P = 0.02; and HR = 0.66, P = 0.03, respectively).ConclusionsInduction chemotherapy plus concurrent chemoradiotherapy improved survival outcomes in patients with locally advanced nasopharyngeal carcinoma versus concurrent chemoradiotherapy. For the weekly cisplatin regimen subgroup, it did not improve remote control or overall survival versus concurrent chemoradiotherapy alone, warranting further clarification.

Dataset Information

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.

Background

Methods

Results

Conclusion

Trial registration

Publications

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment.

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