Dataset Information

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

ABSTRACT: Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.

SUBMITTER: Chen D

PROVIDER: S-EPMC10040964 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

Chen Dexin D Lai Jianbo J Cheng Jiaxin J Fu Meiting M Lin Liyan L Chen Feng F Huang Rong R Chen Jun J Lu Jianping J Chen Yuning Y Huang Guangyao G Yan Miaojia M Ma Xiaodan X Li Guoxin G Chen Gang G Yan Jun J

iScience 20230303 3

Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eo ...[more]

PMID: 36994190

Similar Datasets

Project description:BackgroundAfter radical surgery, early detection of recurrence and metastasis is a crucial factor in enhancing the prognosis and survival of patients with gastric cancer (GC). Therefore, assessing the risk of recurrence in gastric cancer patients and determining the timing for postoperative recurrence is crucial.MethodsThe clinicopathological data of 521 patients with recurrent gastric cancer, who underwent radical gastrectomy at Zhejiang Cancer Hospital between January 2010 and January 2017, were retrospectively analyzed. These patients were randomly divided into two groups: a training group (n = 365) and a validation group (n = 156). In the training set, patients were further categorized into early recurrence (n = 263) and late recurrence (n = 102) groups based on a 2-year boundary. Comparative analyses of clinicopathological features and prognoses were conducted between these two groups. Subsequently, a nomogram for predicting early recurrence was developed and validated.ResultsIn this study, the developed nomogram incorporated age, serous infiltration, lymph node metastasis, recurrence mode, and the tumour marker CA19-9. In the training cohort, the area under the curve (AUC value) was 0.739 (95% CI, 0.682-0.798), with a corresponding C-index of 0.739. This nomogram was subsequently validated in an independent validation cohort, yielding an AUC of 0.743 (95% CI, 0.652-0.833) and a C-index of 0.743. Furthermore, independent risk factors for prognosis were identified, including age, absence of postoperative chemotherapy, early recurrence, lymph node metastasis, abdominal metastasis, and vascular cancer embolus.ConclusionIndependent risk factors for gastric cancer recurrence following radical surgery were utilized to construct a nomogram for predicting early relapse. This nomogram effectively assesses the risk of recurrence, aids in treatment decision-making and follow-up planning in clinical settings, and demonstrated strong performance in the validation cohort.

Project description:BackgroundVenous invasion (VI) in pathological examination of surgically resected gastric cancer (GC) may predict postoperative recurrence, but there are no objective criteria for VI grading.Methods157 GC patients (pathological stages I 82, II 34, and III 41) who underwent surgery with curative intent were analyzed. VI was graded in pathological examination by elastica van Gieson staining based on the number of VIs per glass slide as follows: v0, 0; v1, 1-3; v2, 4-6; and v3, ≥ 7. Filling-type invasion in veins with a minor axis of ≥ 1 mm increased the grade by 1. The association of VI grade with prognosis was statistically analyzed.ResultsRecurrence increased with VI grade (v0 1.5%, v1 29.6%, v2 41.7%, v3 78.6%). VI grade as well as pathological (p) tumor, node, metastasis (TNM) stage was a significant recurrence predictor by the multivariate Cox analysis. VI grade was implicated in hematogenous and peritoneal recurrences independent of pTNM stage but not in nodal recurrence. GC was then divided into two tiers, without indication of adjuvant chemotherapy (AC) (pStage I, pT1 and pT3N0) and with AC indication (pStages remaining II/III), based on the ACTS-GC trial, which is common in Japan and East Asia. VI grade was a significant recurrence predictor in both tiers. v2/v3 revealed a significantly worse recurrence-free survival (RFS) than v0/v1 in GC without AC indication. v0/v1 exhibited RFS rate exceeding 95% even after 5 years but that of v2/v3 fell around 70% within one year postoperatively, suggesting that AC may be considered for this tier with v2/v3. GC with AC indication exhibited dismal RFS according to the VI grade. RFS rate fell below 80% within one year postoperatively when VI was positive, while recurrence was not observed in v0, which was, however, rare in this tier (10.9%). Differentiation grade did not significantly affect postoperative prognosis in both tiers.ConclusionsVI grade was a significant predictor of postoperative GC recurrence irrespective of the AC indication based on the ACTS-GC study and this VI grading system could be applied in future studies of adjuvant therapy in GC presently deemed without AC indication in Japan.

Project description:PurposeGastric cancer peritoneal carcinomatosis is fatal. Delay in detection of peritoneal metastases contributes to high mortality, highlighting the need to develop biomarkers that can help identify patients at high risk for peritoneal recurrence or metastasis.Experimental designWe performed a systematic discovery and validation for the identification of peritoneal recurrence prediction and peritoneal metastasis detection biomarkers by analyzing expression profiling datasets from 249 patients with gastric cancer, followed by analysis of 426 patients from three cohorts for clinical validation.ResultsGenome-wide expression profiling identified a 12-gene panel for robust prediction of peritoneal recurrence in patients with gastric cancer (AUC = 0.95), which was successfully validated in a second dataset (AUC = 0.86). Examination of 216 specimens from a training cohort allowed us to establish a six gene-based risk-prediction model [AUC = 0.72; 95% confidence interval (CI): 0.66-0.78], which was subsequently validated in an independent cohort of 111 patients with gastric cancer (AUC = 0.76; 95% CI: 0.67-0.83). In both cohorts, combining tumor morphology and depth of invasion further improved the predictive accuracy of the prediction model (AUC = 0.84). Thereafter, we evaluated the performance of the identical six-gene panel for its ability to detect peritoneal metastasis by analyzing 210 gastric cancer specimens (prior 111 patients plus additional 99 cases), which discriminated patients with and without peritoneal metastasis (AUC = 0.72). Finally, our biomarker panel was also remarkably effective for identifying peritoneal micrometastasis (AUC = 0.72), and its diagnostic accuracy was significantly enhanced when depth of invasion was included in the model (AUC = 0.85).ConclusionsOur novel transcriptomic signature for risk stratification and identification of high-risk patients with peritoneal carcinomatosis might serve as an important clinical decision making in patients with gastric cancer.

Project description:ObjectivesTo develop and validate a quantitative model for predicting serosal invasion based on multi-parameters in preoperative dual-energy CT (DECT).Materials and methodsA total of 342 LAGC patients who underwent gastrectomy and DECT from six centers were divided into one training cohort (TC), and two validation cohorts (VCs). Dual-phase enhanced DECT-derived iodine concentration (IC), water concentration, and monochromatic attenuation of lesions, along with clinical information, were measured and collected. The independent predictors among these characteristics for serosal invasion were screened with Spearman correlation analysis and logistic regression (LR) analysis. A quantitative model was developed based on LR classifier with fivefold cross-validation for predicting the serosal invasion in LAGC. We comprehensively tested the model and investigated its value in survival analysis.ResultsA quantitative model was established using IC, 70 keV, 100 keV monochromatic attenuations in the venous phase, and CT-reported T4a, which were independent predictors of serosal invasion. The proposed model had the area-under-the-curve (AUC) values of 0.889 for TC and 0.860 and 0.837 for VCs. Subgroup analysis showed that the model could well discriminate T3 from T4a groups, and T2 from T4a groups in all cohorts (all p < 0.001). Besides, disease-free survival (DFS) (TC, p = 0.015; and VC1, p = 0.043) could be stratified using this quantitative model.ConclusionThe proposed quantitative model using multi-parameters in DECT accurately predicts serosal invasion for LAGC and showed a significant correlation with the DFS of patients.Critical relevance statementThis quantitative model from dual-energy CT is a useful tool for predicting the serosal invasion of locally advanced gastric cancer.Key pointsSerosal invasion is a poor prognostic factor in locally advanced gastric cancer that may be predicted by DECT. DECT quantitative model for predicting serosal invasion was significantly and positively correlated with pathologic T stages. This quantitative model was associated with patient postoperative disease-free survival.

Dataset Information

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

Publications

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets