ArticleAnnals of surgical oncology2024
Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.
Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 22 citations in OpenAlex.
- Pooled it
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Deep learning or radiomics based on CT for predicting the response of gastric cancer to neoadjuvant chemotherapy: a meta-analysis and systematic review.Frontiers in oncology · 2024Pooled it
- Trial
- A Radiomics-Clinical Nomogram for Pre-Treatment Prediction of Neoadjuvant Chemotherapy Response in Locally Advanced Gastric Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Predictive imaging in abdominal oncology: current trends and future directions.Japanese journal of radiology · 2026Review
- Prediction of gross extrathyroidal extension in papillary thyroid carcinoma using a deep learning radiomics model based on post-processing techniques for multi-phase contrast-enhanced MRI images.Frontiers in endocrinology · 2026Article
- Clinical-radiomic prognostic model integrating staging before and after neoadjuvant chemotherapy in gastric cancer: a multicenter retrospective study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2025Article
- Value of the systemic immune inflammation index in predicting the efficacy of neoadjuvant chemotherapy in patients with gastric cancer: a multicenter retrospective clinical study.Journal of gastrointestinal oncology · 2025Article
- Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.Journal of cardiothoracic surgery · 2025Article
- The artificial intelligence revolution in gastric cancer management: clinical applications.Cancer cell international · 2025Review
- Application and progress of nomograms in gastric cancer.Frontiers in medicine · 2025Review
- Prediction of tumor deposits in stage I-III gastric cancer: a clinically applicable nomogram integrating clinicopathology outcomes.Frontiers in medicine · 2025Article
- Combining radiomics and deep learning to predict liver metastasis of gastric cancer on CT image.Frontiers in oncology · 2025Article
- Prediction of neoadjuvant therapy efficacy in gastric cancer: the interplay between biomarkers and radiomics and its potential for clinical translation.Frontiers in oncology · 2025Review
- Clinical implications of CT-detected ascites in gastric cancer: association with peritoneal metastasis and systemic inflammatory response.Insights into imaging · 2024Article
- A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
17 authors at 1 institution in 1 country.
Funding
Abstract
backgroundWe aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC).
methodsWe prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively.
resultsThe radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis.
conclusionsIn patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.