Predicting Chemotherapeutic Response for Far-advanced Gastric Cancer by Radiomics with Deep Learning Semi-automatic Segmentation

Abstract

Purpose: To build a dual-energy computed tomography (DECT) delta radiomics model to predict chemotherapeutic response for far-advanced gastric cancer (GC) patients. A semi-automatic segmentation method based on deep learning was designed, and its performance was compared with that of manual segmentation. Methods: This retrospective study included 86 patients with far-advanced GC treated with chemotherapy from September 2016 to December 2017 (66 and 20 in the training and testing cohorts, respectively). Delta radiomics features between the baseline and first follow-up DECT were modeled by random forest to predict the chemotherapeutic response evaluated by the second follow-up DECT. Nine feature subsets from confounding factors and delta radiomics features were used to choose the best model with 10-fold cross-validation in the training cohort. A semi-automatic segmentation method based on deep learning was developed to predict the chemotherapeutic response and compared with manual segmentation in the testing cohort, which was further validated in an independent validation cohort of 30 patients. Results: The best model, constructed by confounding factors and texture features, reached an average AUC of 0.752 in the training cohort. Our proposed semi-automatic segmentation method was more time-effective than manual segmentation, with average saving-time of 11.2333 ± 6.3989 minutes and 9.9889 ±5.5086 minutes in the testing cohort and the independent validation cohort, respectively (both p < 0.05). The predictive ability of the semi-automatic segmentation was also better than that of the manual segmentation both in the testing cohort and the independent validation cohort (AUC: 0.728 vs. 0.687 and 0.828 vs. 0.749, respectively). Conclusion: DECT delta radiomics serves as a promising biomarker for predicting chemotherapeutic response for far-advanced GC. Semi-automatic segmentation based on deep learning shows the potential for clinical use with increased reproducibility and decreased labor costs compared to the manual version.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

“…Jing-Wen et al used a deep learning software for semi-automated segmentation and developed a CT-radiomic approach to predict response to chemotherapy in patients with advanced adenocarcinoma [123].…”

Predicting Response to Systemic Chemotherapy for Advanced Gastric Cancer Using Pre-Treatment Dual-Energy CT Radiomics: A Pilot Study

“…Existing radiomics models for predicting response to systemic chemotherapy used both pre-treatment and post-treatment CT images ( 35 ). However, the post-treatment nature could narrow its extensive utility in clinical therapy decision-making ( 36 ).…”

“…Furthermore, our study demonstrates that radiomics features extracted from the virtual monochromatic images can reflect heterogeneity of gastric cancer and that radiomics may serve as a promising technique for predicting the response to treatment in patients with AGC. Existing radiomics models for predicting response to systemic chemotherapy used both pre-treatment and post-treatment CT images (35). However, the post-treatment nature could narrow its extensive utility in clinical therapy decision-making (36).…”

Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas

“…In a recent study, Lin et al trained a CNN to detect lymph node metastasis by analyzing perioperative CT images of patients with gastric cancer. In addition, and relevant to potential therapeutic de-escalation therapy and patient surveillance, CT scans may also be used to monitor responses to (neoadjuvant) chemotherapy in GEA [158,159]. Other attempts involved training a model to aid the detection of GEA using CT scans [160].…”