Prediction of serosal invasion in gastric cancer: development and validation of multivariate models integrating preoperative clinicopathological features and radiographic findings based on late arterial phase CT images

Background To develop and validate multivariate models integrating endoscopic biopsy, tumor markers, and CT findings based on late arterial phase (LAP) to predict serosal invasion in gastric cancer (GC). Methods The preoperative differentiation degree, tumor markers, CT morphological characteristics, and CT value-related and texture parameters of 154 patients with GC were analyzed retrospectively. Multivariate models based on regression analysis and machine learning algorithms were performed to improve the diagnostic efficacy. Results The differentiation degree, carbohydrate antigen (CA) 199, CA724, CA242, and multiple CT findings based on LAP differed significantly between T1–3 and T4 GCs in the primary cohort (all P < 0.05). Multivariate models based on regression analysis and random forest achieved AUCs of 0.849 and 0.865 in the primary cohort, respectively. Conclusion We developed and validated multivariate models integrating endoscopic biopsy, tumor markers, CT morphological characteristics, and CT value-related and texture parameters to predict serosal invasion in GCs and achieved favorable performance.

Neoadjuvant Chemotherapy Versus Direct Surgery for Locally Advanced Gastric Cancer With Serosal Invasion (cT4NxM0): A Propensity Score-Matched Analysis

BackgroundFor locally advanced gastric cancer (LAGC) with serosal invasion (cT4NxM0), adjuvant chemotherapy (AC) after D2 gastrectomy is the standard therapy in Asia. However, perioperative chemotherapy (PCT) combined with D2 gastrectomy is mostly suggested in Europe and America. As a part of PCT, the value of neoadjuvant chemotherapy (NAC) is unclear. We investigated whether NAC could further improve survival and other outcomes for these patients.MethodsPatients with cT4NxM0 gastric cancer who underwent D2 gastrectomy were analyzed. The patients were divided into two groups based on whether they received NAC: the neoadjuvant chemotherapy (NAC) and direct surgery (S) groups. After propensity score matching (1:1 ratio), survival and perioperative outcomes were analyzed between the two groups.ResultsA total of 902 patients met all the eligibility criteria and were enrolled. After propensity score matching, 221 matched pairs of patients were identified. The median overall survival (OS) and disease-free survival (DFS) of all patients were 75.10 and 43.67 months, respectively. The median OS of patients in the NAC and S groups were undefined and 29.80 months, respectively (P&lt;0.0001). The median DFS of patients in the NAC and S groups were undefined and 22.60 months (P&lt;0.0001). There were no significant differences in the radical degrees of operation between the two groups (P=0.07). However, there were significant differences in postoperative hospital stay (P&lt;0.001) and complications (P=0.037) between the two groups.ConclusionThis study suggested NAC can further improve prognosis and prevent recurrence in LAGC (cT4NxM0) patients. NAC is feasible and safe for LAGC (cT4NxM0) patients, and does not increase the risk of perioperative surgery.

Establishment of the Radiologic Tumor Invasion Index Based on Radiomics Splenic Features and Clinical Factors to Predict Serous Invasion of Gastric Cancer

BackgroundCurrently, there are shortcomings in diagnosing gastric cancer with or without serous invasion, making it difficult for patients to receive appropriate treatment. Therefore, we aimed to develop a radiomic nomogram for preoperative identification of serosal invasion.MethodsWe selected 315 patients with gastric cancer, confirmed by pathology, and randomly divided them into two groups: the training group (189 patients) and the verification group (126 patients). We obtained patient splenic imaging data for the training group. A p-value of &lt;0.05 was considered significant for features that were selected for lasso regression. Eight features were chosen to construct a serous invasion prediction model. Patients were divided into high- and low-risk groups according to the radiologic tumor invasion risk score. Subsequently, univariate and multivariate regression analyses were performed with other invasion-related factors to establish a visual combined prediction model.ResultsThe diagnostic accuracy of the radiologic tumor invasion score was consistent in the training and verification groups (p&lt;0.001 and p=0.009, respectively). Univariate and multivariate analyses of invasion risk factors revealed that the radiologic tumor invasion index (p=0.002), preoperative hemoglobin &lt;100 (p=0.042), and the platelet and lymphocyte ratio &lt;92.8 (p=0.031) were independent risk factors for serosal invasion in the training cohort. The prediction model based on the three indexes accurately predicted the serosal invasion risk with an area under the curve of 0.884 in the training cohort and 0.837 in the testing cohort.ConclusionsRadiological tumor invasion index based on splenic imaging combined with other factors accurately predicts serosal invasion of gastric cancer, increases diagnostic precision for the most effective treatment, and is time-efficient.

Deep Learning for Detection of Colonic Polyps from Computed Tomography Colonoscopy Images Combined with Colonoscopy

The objective of this study was to investigate the diagnosis of colonic polyps (CP) through the computed tomography (CT) images combined with colonoscopy based on Fourier central slice theorem algorithm. In this study, 86 patients with CP admitted to hospital were selected as research objects. CT imaging and colonoscopy were applied to diagnose the patients based on the algorithm of Fourier central slice theorem. The results showed that the diagnostic detection rates of CP and colon cancer (CC) were 88.2% and 94.2%, respectively. The occurrence site of CP was the sigmoid and ascending colon. 38 patients were positive for serosal invasion of CP while 42 patients were negative for serosal invasion of CP, and there were no statistical differences ( P > 0.05 ). The lesion positions of remaining 6 cases were hard to find and could not be detected accurately. Besides, the diagnostic accuracy of preoperative and postoperative stages III and IV was all 100.00%. The combination of CT imaging and colonoscopy was employed to diagnose CP, which was found to be able to accurately locate the lesions, to effectively evaluate the tumor stage before and after surgery, and to have a good diagnostic efficacy in detecting tumor serosal layer.

The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images

ObjectivesThe aim was to determine whether the dual-energy CT radiomics model derived from an iodine map (IM) has incremental diagnostic value for the model based on 120-kV equivalent mixed images (120 kVp) in preoperative restaging of serosal invasion with locally advanced gastric cancer (LAGC) after neoadjuvant chemotherapy (NAC).MethodsA total of 155 patients (110 in the training cohort and 45 in the testing cohort) with LAGC who had standard NAC before surgery were retrospectively enrolled. All CT images were analyzed by two radiologists for manual classification. Volumes of interests (VOIs) were delineated semi-automatically, and 1,226 radiomics features were extracted from every segmented lesion in both IM and 120 kVp images, respectively. Spearman’s correlation analysis and the least absolute shrinkage and selection operator (LASSO) penalized logistic regression were implemented for filtering unstable and redundant features and screening out vital features. Two predictive models (120 kVp and IM-120 kVp) based on 120 kVp selected features only and 120 kVp combined with IM selected features were established by multivariate logistic regression analysis. We then build a combination model (ComModel) developed with IM-120 kVp signature and ycT. The performance of these three models and manual classification were evaluated and compared.ResultThree radiomics models showed great predictive accuracy and performance in both the training and testing cohorts (ComModel: AUC: training, 0.953, testing, 0.914; IM-120 kVp: AUC: training, 0.953, testing, 0.879; 120 kVp: AUC: training, 0.940, testing, 0.831). All these models showed higher diagnostic accuracy (ComModel: 88.9%, IM-120 kVp: 84.4%, 120 kVp: 80.0%) than manual classification (68.9%) in the testing group. ComModel and IM-120 kVp model had better performances than manual classification both in the training (both p&lt;0.001) and testing cohorts (p&lt;0.001 and p=0.034, respectively).ConclusionsDual-energy CT-based radiomics models demonstrated convincible diagnostic performance in differentiating serosal invasion in preoperative restaging for LAGC. The radiomics features derived from IM showed great potential for improving the diagnostic capability.

Predicting postoperative peritoneal metastasis in gastric cancer with serosal invasion using a collagen nomogram

Accurate prediction of peritoneal metastasis for gastric cancer (GC) with serosal invasion is crucial in clinic. The presence of collagen in the tumour microenvironment affects the metastasis of cancer cells. Herein, we propose a collagen signature, which is composed of multiple collagen features in the tumour microenvironment of the serosa derived from multiphoton imaging, to describe the extent of collagen alterations. We find that a high collagen signature is significantly associated with a high risk of peritoneal metastasis (P < 0.001). A competing-risk nomogram including the collagen signature, tumour size, tumour differentiation status and lymph node metastasis is constructed. The nomogram demonstrates satisfactory discrimination and calibration. Thus, the collagen signature in the tumour microenvironment of the gastric serosa is associated with peritoneal metastasis in GC with serosal invasion, and the nomogram can be conveniently used to individually predict the risk of peritoneal metastasis in GC with serosal invasion after radical surgery.

Impact of proximal resection margin involvement on survival outcome in patients with proximal gastric cancer

AimThe aim of this study was to evaluate the risk factors for proximal resection margin involvement and its impact on survival outcome in patients with proximal gastric cancer.MethodsA total of 488 patients who underwent potentially curative resection for proximal gastric cancer were retrospectively reviewed. Clinicopathological characteristics and survival differences between patients with positive and negative resection margins were compared and prognostic factors were determined by Cox multivariate analysis.ResultsIn this study, 7.6% (37/488) of patients with proximal gastric cancer had a positive proximal resection margin after postoperative histopathological examination. Positive resection margins were significantly associated with advanced tumour stage and more aggressive biological features including larger tumour size, serosal invasion and lymphovascular invasion. Serosal invasion (OR 4.543, 95% CI 2.201 to 9.380, p<0.001) and lymphovascular invasion (OR 2.279, 95% CI 1.129 to 4.600, p<0.05) were independent risk factors for positive proximal resection margins. In terms of survival outcome, positive resection margins had an adverse impact on the prognosis of patients with proximal gastric cancer (median DFS: 20.7 vs 30.2 months, p<0.001). The multivariate analysis indicated that positive resection margins (HR 1.494, 95% CI 1.042 to 2.142, p=0.029), T stage (T3–T4, HR 2.264, 95% CI 1.484 to 3.454, p<0.001) and N stage (N1–N2 stage, HR 1.696, 95% CI 1.279 to 2.248, p<0.001; N3 stage, HR 2.691, 95% CI 1.967 to 3.681, p<0.001) were independent prognostic factors for patients with proximal gastric cancer.ConclusionProximal resection margin involvement was an indicator of more aggressive tumours and an independent prognostic factor for patients with proximal gastric cancer. Aggressive efforts should be made to achieve a negative resection margin if gastric cancer was deemed to be potentially resectable.