Deep learning radiomic nomogram can predict the number of lymph node metastasis in locally advanced gastric cancer: an international multicenter study

Abstract

Background: Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough. Patients and methods: We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis. Results: The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785e0.858) in the primary cohort, 0.797 (0.771e0.823) in the external validation cohorts, and 0.822 (0.756e0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n ¼ 271). Conclusion: A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images

“…Radiological images are routinely available in clinical practice. Unlike traditional biopsy-based assays that represent only part of the tumor, images reflect information on the entire tumor burden in a non-invasive manner and avoid the effects of tumor heterogeneity 16 , 17 . Radiomics is the science of quantifying patterns of tumor phenotypes on radiographic images in a high throughput manner and analyzing them with bioinformatics tools to build clinically relevant models that assess tumor and microenvironment heterogeneity 19 .…”

Intratumoral and peritumoral radiomics analysis for preoperative Lauren classification in gastric cancer

“…Radiomics utilizes automated quantitative characterization algorithms to transform a large number of excavatable spatial ROI-based image data into representative and effective radiomic features [ 6 ]. Recent advancements in radiomics have provided new ideas for individualized management of GC, including lymphatic metastasis prediction [ 7 , 8 ], distant metastasis prediction [ 9 ], therapeutic response evaluation [ 10 ], and prognostic evaluation [ 11 , 12 ]. These studies highlighted the value of radiomics, suggesting that radiomics could be a potential tool for the Lauren classification in GC.…”

A clinical model to predict distant metastasis in patients with superficial gastric cancer with negative lymph node metastasis and a survival analysis for patients with metastasis

“…A variety of nomograms have been built to predict the therapeutic benefits, postoperative survival rate, and LNM in patients with GC. [13][14][15] Here, we constructed a nomogram to predict the risk of DM on the basis of the clinical characteristics of patients with SGC-NLNM. To test the performance of our nomogram, ROC curves were generated in both the training and validation sets.…”