Predicting peritoneal recurrence and disease-free survival from CT images in gastric cancer with multitask deep learning: a retrospective study.
The Lancet. Digital health
2022; 4 (5): e340-e350
BACKGROUND: Peritoneal recurrence is the predominant pattern of relapse after curative-intent surgery for gastric cancer and portends a dismal prognosis. Accurate individualised prediction of peritoneal recurrence is crucial to identify patients who might benefit from intensive treatment. We aimed to develop predictive models for peritoneal recurrence and prognosis in gastric cancer.METHODS: In this retrospective multi-institution study of 2320 patients, we developed a multitask deep learning model for the simultaneous prediction of peritoneal recurrence and disease-free survival using preoperative CT images. Patients in the training cohort (n=510) and the internal validation cohort (n=767) were recruited from Southern Medical University, Guangzhou, China. Patients in the external validation cohort (n=1043) were recruited from Sun Yat-sen University Cancer Center, Guangzhou, China. We evaluated the prognostic accuracy of the model as well as its association with chemotherapy response. Furthermore, we assessed whether the model could improve the ability of clinicians to predict peritoneal recurrence.FINDINGS: The deep learning model had a consistently high accuracy in predicting peritoneal recurrence in the training cohort (area under the receiver operating characteristic curve [AUC] 0·857; 95% CI 0·826-0·889), internal validation cohort (0·856; 0·829-0·882), and external validation cohort (0·843; 0·819-0·866). When informed by the artificial intelligence (AI) model, the sensitivity and inter-rater agreement of oncologists for predicting peritoneal recurrence was improved. The model was able to predict disease-free survival in the training cohort (C-index 0·654; 95% CI 0·616-0·691), internal validation cohort (0·668; 0·643-0·693), and external validation cohort (0·610; 0·583-0·636). In multivariable analysis, the model predicted peritoneal recurrence and disease-free survival independently of clinicopathological variables (p<0·0001 for all). For patients with a predicted high risk of peritoneal recurrence and low survival, adjuvant chemotherapy was associated with improved disease-free survival in both stage II disease (hazard ratio [HR] 0·543 [95% CI 0·362-0·815]; p=0·003) and stage III disease (0·531 [0·432-0·652]; p<0·0001). By contrast, chemotherapy had no impact on disease-free survival for patients with a predicted low risk of peritoneal recurrence and high survival. For the remaining patients, the benefit of chemotherapy depended on stage: only those with stage III disease derived benefit from chemotherapy (HR 0·637 [95% CI 0·484-0·838]; p=0·001).INTERPRETATION: The deep learning model could allow accurate prediction of peritoneal recurrence and survival in patients with gastric cancer. Prospective studies are required to test the clinical utility of this model in guiding personalised treatment in combination with clinicopathological criteria.FUNDING: None.
View details for DOI 10.1016/S2589-7500(22)00040-1
View details for PubMedID 35461691
Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study
LANCET DIGITAL HEALTH
2021; 3 (6): E371-E382
View details for Web of Science ID 000654685600008
Noninvasive Prediction of Occult Peritoneal Metastasis in Gastric Cancer Using Deep Learning.
JAMA network open
2021; 4 (1): e2032269
Importance: Occult peritoneal metastasis frequently occurs in patients with advanced gastric cancer and is poorly diagnosed with currently available tools. Because the presence of peritoneal metastasis precludes the possibility of curative surgery, there is an unmet need for a noninvasive approach to reliably identify patients with occult peritoneal metastasis.Objective: To assess the use of a deep learning model for predicting occult peritoneal metastasis based on preoperative computed tomography images.Design, Setting, and Participants: In this multicenter, retrospective cohort study, a deep convolutional neural network, the Peritoneal Metastasis Network (PMetNet), was trained to predict occult peritoneal metastasis based on preoperative computed tomography images. Data from a cohort of 1225 patients with gastric cancer who underwent surgery at Sun Yat-sen University Cancer Center (Guangzhou, China) were used for training purposes. To externally validate the model, data were collected from 2 independent cohorts comprising a total of 753 patients with gastric cancer who underwent surgery at Nanfang Hospital (Guangzhou, China) or the Third Affiliated Hospital of Southern Medical University (Guangzhou, China). The status of peritoneal metastasis for all patients was confirmed by pathological examination of pleural specimens obtained during surgery. Detailed clinicopathological data were collected for each patient. Data analysis was performed between September 1, 2019, and January 31, 2020.Main Outcomes and Measures: The area under the receiver operating characteristic curve (AUC) and decision curve were analyzed to evaluate performance in predicting occult peritoneal metastasis.Results: A total of 1978 patients (mean [SD] age, 56.0 [12.2] years; 1350 [68.3%] male) were included in the study. The PMetNet model achieved an AUC of 0.946 (95% CI, 0.927-0.965), with a sensitivity of 75.4% and a specificity of 92.9% in external validation cohort 1. In external validation cohort 2, the AUC was 0.920 (95% CI, 0.848-0.992), with a sensitivity of 87.5% and a specificity of 98.2%. The discrimination performance of PMetNet was substantially higher than conventional clinicopathological factors (AUC range, 0.51-0.63). In multivariable logistic regression analysis, PMetNet was an independent predictor of occult peritoneal metastasis.Conclusions and Relevance: The findings of this cohort study suggest that the PMetNet model can serve as a reliable noninvasive tool for early identification of patients with clinically occult peritoneal metastasis, which will inform individualized preoperative treatment decision-making and may avoid unnecessary surgery and complications. These results warrant further validation in prospective studies.
View details for DOI 10.1001/jamanetworkopen.2020.32269
View details for PubMedID 33399858
Development and Validation of a Deep Learning CT Signature to Predict Survival and Chemotherapy Benefit in Gastric Cancer: A Multicenter, Retrospective Study.
Annals of surgery
We aimed to develop a deep learning-based signature to predict prognosis and benefit from adjuvant chemotherapy using preoperative computed tomography (CT) images.Current staging methods do not accurately predict the risk of disease relapse for patients with gastric cancer.We proposed a novel deep neural network (S-net) to construct a CT signature for predicting disease-free survival (DFS) and overall survival in a training cohort of 457 patients, and independently tested it in an external validation cohort of 1158 patients. An integrated nomogram was constructed to demonstrate the added value of the imaging signature to established clinicopathologic factors for individualized survival prediction. Prediction performance was assessed with respect to discrimination, calibration, and clinical usefulness.The DeLIS was associated with DFS and overall survival in the overall validation cohort and among subgroups defined by clinicopathologic variables, and remained an independent prognostic factor in multivariable analysis (P< 0.001). Integrating the imaging signature and clinicopathologic factors improved prediction performance, with C-indices: 0.792-0.802 versus 0.719-0.724, and net reclassification improvement 10.1%-28.3%. Adjuvant chemotherapy was associated with improved DFS in stage II patients with high-DeLIS [hazard ratio = 0.362 (95% confidence interval 0.149-0.882)] and stage III patients with high- and intermediate-DeLIS [hazard ratio = 0.611 (0.442-0.843); 0.633 (0.433-0.925)]. On the other hand, adjuvant chemotherapy did not affect survival for patients with low-DeLIS, suggesting a predictive effect (Pinteraction = 0.048, 0.016 for DFS in stage II and III disease).The proposed imaging signature improved prognostic prediction and could help identify patients most likely to benefit from adjuvant chemotherapy in gastric cancer.
View details for DOI 10.1097/SLA.0000000000003778
View details for PubMedID 31913871
Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer.
Annals of oncology : official journal of the European Society for Medical Oncology
The tumor immune microenvironment can provide prognostic and predictive information. A previously validated ImmunoScore of gastric cancer (ISGC) evaluates both lymphoid and myeloid cells in the tumor core and invasive margin with immunohistochemistry staining of surgical specimens. We aimed to develop a noninvasive radiomics-based predictor of ISGC.In this retrospective study including four independent cohorts of 1778 patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced CT images. A radiomic signature (RIS) was constructed to predict ISGC by using regularized logistic regression. We further evaluated its association with prognosis and chemotherapy response.A 13-feature radiomic signature for ISGC was developed and validated in 3 independent cohorts (area under the curve=0.786, 0.745, and 0.766). The RIS signature was significantly associated with both disease-free and overall survival in the training and all validation cohorts (HR range: 0.296-0.487, all P<0.001). In multivariable analysis, the RIS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HR range: 0.339-0.605, all P<0.003). For stage II and III disease, patients with a high RIS derived survival benefit from adjuvant chemotherapy, HR=0.436 (95% CI: 0.253-0.753), P=0.002; HR=0.591 (95% CI: 0.428-0.818), P<0.001, respectively; while those with a low RIS did not.The radiomic signature is a reliable tool for evaluation of immunoscore and retains the prognostic significance in gastric cancer. Future prospective studies are required to confirm its potential to predict treatment response and select patients who will benefit from chemotherapy.
View details for DOI 10.1016/j.annonc.2020.03.295
View details for PubMedID 32240794
Single-Cell Spatial Analysis of Tumor and Immune Microenvironment on Whole-Slide Image Reveals Hepatocellular Carcinoma Subtypes.
2020; 12 (12)
Hepatocellular carcinoma (HCC) is a heterogeneous disease with diverse characteristics and outcomes. Here, we aim to develop a histological classification for HCC by integrating computational imaging features of the tumor and its microenvironment. We first trained a multitask deep-learning neural network for automated single-cell segmentation and classification on hematoxylin- and eosin-stained tissue sections. After confirming the accuracy in a testing set, we applied the model to whole-slide images of 304 tumors in the Cancer Genome Atlas. Given the single-cell map, we calculated 246 quantitative image features to characterize individual nuclei as well as spatial relations between tumor cells and infiltrating lymphocytes. Unsupervised consensus clustering revealed three reproducible histological subtypes, which exhibit distinct nuclear features as well as spatial distribution and relation between tumor cells and lymphocytes. These histological subtypes were associated with somatic genomic alterations (i.e., aneuploidy) and specific molecular pathways, including cell cycle progression and oxidative phosphorylation. Importantly, these histological subtypes complement established molecular classification and demonstrate independent prognostic value beyond conventional clinicopathologic factors. Our study represents a step forward in quantifying the spatial distribution and complex interaction between tumor and immune microenvironment. The clinical relevance of the imaging subtypes for predicting prognosis and therapy response warrants further validation.
View details for DOI 10.3390/cancers12123562
View details for PubMedID 33260561
Natural killer cell and stroma abundance are independently prognostic and predict gastric cancer chemotherapy benefit.
BACKGROUND: Specific features of the tumor microenvironment (TME) may provide useful prognostic information. We conducted a systematic investigation of the cellular composition and prognostic landscape of TME in gastric cancer.METHODS: We evaluated the prognostic significance of major stromal and immune cells within TME. We proposed a composite TME-based risk score and tested it in six independent cohorts of 1,678 patients with gene expression or immunohistochemistry measurements. Further, we devised a new patient classification system based on TME characteristics.RESULTS: We identified natural killer cells, fibroblasts, and endothelial cells as the most robust prognostic markers. The TME risk score combining these cell types was an independent prognostic factor when adjusted for clinicopathologic variables (gene expression: HR [95% CI]: 1.42 [1.22-1.66]; immunohistochemistry: 1.34 [1.24-1.45], P<0.0001). Higher TME risk scores consistently associated with worse survival within every pathologic stage (HR range: 2.18-3.11, P<0.02) and among patients who received surgery only. The TME risk score provided additional prognostic value beyond stage, and combination of the two improved prognostication accuracy (likelihood-ratio test chi2 = 235.4 vs. 187.6, P<0.0001; net reclassification index: 23%). The TME risk score can predict the survival benefit of adjuvant chemotherapy in non-metastatic patients (stage I-III) (interaction test P<0.02). Patients were divided into four TME subtypes that demonstrated distinct genetic and molecular patterns and complemented established genomic and molecular subtypes.CONCLUSION: We developed and validated a TME-based risk score as an independent prognostic and predictive factor, which has the potential to guide personalized management of gastric cancer.
View details for DOI 10.1172/jci.insight.136570
View details for PubMedID 32229725