Dataset Information

Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

ABSTRACT: Objectives: To develop a radiomic model based on multiparametric magnetic resonance imaging (MRI) for predicting treatment response prior to commencing concurrent chemotherapy and radiation therapy (CCRT) for locally advanced cervical cancer. Materials and methods: The retrospective study enrolled 120 patients (allocated to a training or a test set) with locally advanced cervical cancer who underwent CCRT between December 2014 and June 2017. All patients enrolled underwent MRI with nine sequences before treatment and again at the end of the fourth week of treatment. Responses were evaluated by MRI according to RECIST standards, and patients were divided into a responder group or non-responder group. For every MRI sequence, a total of 114 radiomic features were extracted from the outlined tumor habitat. On the training set, the least absolute shrinkage and selection operator method was used to select key features and to construct nine habitat signatures. Then, three kinds of machine learning models were compared and applied to integrate these predictive signatures and the clinical characteristics into a radiomic model. The discrimination ability, reliability, and calibration of our radiomic model were evaluated. Results: The radiomic model, which consisted of three habitat signatures from sagittal T2 image, axial T1 enhanced-MRI image, and ADC image, respectively, has shown good predictive performance, with area under the curve of 0.820 (95% CI: 0.713-0.927) in the training set and 0.798 (95% CI: 0.678-0.917) in the test set. Meanwhile, the model proved to perform better than each single signature or clinical characteristic. Conclusions: A radiomic model employing features from multiple tumor habitats held the ability for predicting treatment response in patients with locally advanced cervical cancer before commencing CCRT. These results illustrated a potential new tool for improving medical decision-making and therapeutic strategies.

SUBMITTER: Fang M

PROVIDER: S-EPMC7214615 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

Fang Mengjie M Kan Yangyang Y Dong Di D Yu Tao T Zhao Nannan N Jiang Wenyan W Zhong Lianzhen L Hu Chaoen C Luo Yahong Y Tian Jie J

Frontiers in oncology 20200505

<b>Objectives:</b> To develop a radiomic model based on multiparametric magnetic resonance imaging (MRI) for predicting treatment response prior to commencing concurrent chemotherapy and radiation therapy (CCRT) for locally advanced cervical cancer. <b>Materials and methods:</b> The retrospective study enrolled 120 patients (allocated to a training or a test set) with locally advanced cervical cancer who underwent CCRT between December 2014 and June 2017. All patients enrolled underwent MRI with ...[more]

PMID: 32432035

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Project description:BackgroundTo 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).MethodsA 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.ResultsThe 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).ConclusionsThe 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.

Project description:BackgroundConcurrent chemoradiotherapy followed by adjuvant chemotherapy (CCRT-AC) is currently recommended as the standard treatment for locally advanced nasopharyngeal carcinoma (LA-NPC). Neoadjuvant chemotherapy followed by concurrent chemoradiotherapy (NAC-CCRT) is an alternative strategy for decreasing tumor size and controlling micrometastases before main treatment. The aim of this study was to investigate and compare survival outcomes between LA-NPC patients treated with CCRT-AC and those treated with NAC-CCRT.MethodsThis retrospective cohort study included consecutive histologically confirmed LA-NPC patients that were treated with NAC-CCRT or CCRT-AC at Siriraj Hospital during the March 2010 to October 2014 study period. CCRT in both protocols consisted of 3-week cycles of cisplatin 100 mg/m2 with concurrent radiotherapy. Either NAC or AC consisted of 3-week cycles of cisplatin on day 1 and fluorouracil/leucovorin on days 1-4 for a maximum three cycles. The primary endpoint was 5-year overall survival (OS). Flexible parametric survival analysis was used, because the proportional hazards assumption of Cox regression was violated.ResultsOf the 266 LA-NPC patients that received treatment during the study period, 79 received NAC-CCRT and 187 received CCRT-AC. Median follow-up was 37 months. Significantly more patients with advanced clinical stage (stage IVA-IVB) received NAC-CCRT (86% in NAC-CCRT vs. 29% in CCRT-AC; p < 0.001). Compared to CCRT-AC in crude analysis, 3-year and 5-year OS of NAC-CCRT were 72% vs. 86% and 62% vs. 75% respectively (p = 0.059). Interestingly, the 3-year and 5-year post-estimation adjusted OS was 84% and 74% for NAC-CCRT and 81% and 70% for CCRT-AC, respectively (HR: 0.83, 95% confidence interval (CI): 0.45-1.56; p = 0.571). Also, adjusted analysis of distant-metastasis survival, NAC-CCRT showed HR was 0.79 (95% CI:0.37-1.72, p = 0.557). Conversely, adjusted analysis of locoregional relapse (LLR)-free survival revealed NAC-CCRT to have a significantly higher risk of LRR (HR: 2.18, 95% CI: 0.98-4.87; p = 0.057).ConclusionsThe results suggested that prognosis in the NAC-CCRT treated patients was not superior to that of the CCRT-AC treated individuals. In patients that receive neoadjuvant chemotherapy, locoregional relapse should be of concern. High-risk distant metastasis patients (N3 stage) that could achieve survival advantage from NAC-CCRT is an interesting and important topic for further study.

Dataset Information

Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

Publications

Multi-Habitat Based Radiomics for the Prediction of Treatment Response to Concurrent Chemotherapy and Radiation Therapy in Locally Advanced Cervical Cancer.

Similar Datasets

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