Dataset Information

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

ABSTRACT:

Purpose

To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC).

Materials and methods

This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Radiomics signatures (RS) based on five machine learning algorithms were constructed from the venous-phase CT images. AUC and DeLong test were used to evaluate and compare the performance of the RS. We assessed the dual-energy generalization ability of the best RS. An individualized nomogram combined the best RS and clinical variables was developed, and its discrimination, calibration, and clinical usefulness were determined.

Results

RS obtained with support vector machine (SVM) showed promising predictive capability with AUC of 0.91 and 0.83 in the training and test sets, respectively. The AUC of the best RS in the DECT validation cohort (AUC, 0.71) was significantly lower than that of the training set (Delong test, p = 0.035). The clinical-radiomic nomogram accurately predicted pathologic disagreement in the training and test sets, fitting well in the calibration curves. Decision curve analysis confirmed the clinical usefulness of the nomogram.

Conclusion

CT-based radiomics nomogram showed potential as a clinical aid for predicting pathologic disagreement status between biopsy samples and resected specimens in GC. When practicability and stability are considered, the SECT-based radiomics model is not recommended for DECT generalization.

Critical relevance statement

Radiomics can identify disagreement in pathology between endoscopic biopsy and postoperative specimen.

SUBMITTER: Liu Y

PROVIDER: S-EPMC10323070 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

Liu Yiyang Y Zhao Shuai S Wu Zixin Z Liang Hejun H Chen Xingzhi X Huang Chencui C Lu Hao H Yuan Mengchen M Xue Xiaonan X Luo Chenglong C Liu Chenchen C Gao Jianbo J

Insights into imaging 20230705 1

<h4>Purpose</h4>To develop a noninvasive radiomics-based nomogram for identification of disagreement in pathology between endoscopic biopsy and postoperative specimens in gastric cancer (GC).<h4>Materials and methods</h4>This observational study recruited 181 GC patients who underwent pre-treatment computed tomography (CT) and divided them into a training set (n = 112, single-energy CT, SECT), a test set (n = 29, single-energy CT, SECT) and a validation cohort (n = 40, dual-energy CT, DECT). Rad ...[more]

PMID: 37405591

Similar Datasets

Project description:Rationale: Cancer phosphoproteomics can provide insights regarding kinases that can be targeted for therapeutic applications. Monitoring the phosphoproteomics in cancer is expected to play a key role in optimizing treatments with kinase inhibitors. Clinical phosphoproteomics in surgical tissues and patient-derived models has been studied intensively. However, the reported data may not accurately reflect the phosphosignaling status in patients due to the effect of ischemia occurring during surgery or changes in the characteristics of cancer cells when establishing the models. In contrast, endoscopic biopsies have an advantage for clinical phosphoproteomics because they can be rapidly cryo-preserved. We aimed to develop a highly sensitive method for phosphoproteomics in endoscopic biopsies of gastric cancer. Methods: Three tumor biopsies and three normal gastric biopsies were obtained by endoscopy at one time, and subjected to our optimized phosphoproteomics. Phosphopeptides were enriched with an immobilized metal affinity chromatography, and labeled with Tandem Mass Tag reagent. Quantified phosphosites were compared between the pairs of tumor/normal biopsies within same patient. Cancer-specific activated pathways and kinases were identified by pathway enrichment analysis and kinase-substrate enrichment analysis. Results: Our protocol enabled the identification of more than 10,000 class 1 phosphosites from endoscopic biopsies. A comparison between samples from cancer tissue and normal mucosa demonstrated differences in the phosphosignaling, including biomarkers of response to DNA damage. Finally, cancer-specific activation of DNA damage response signaling was validated by additional phosphoproteomics of other patients and western blotting of gastric cancer/normal cells. Conclusion: In summary, our pioneering approach will facilitate more accurate clinical phosphoproteomics in endoscopic biopsies, which can be applied to monitor the activities of therapeutic kinases and, ultimately, can be a useful tool to precision medicine.

Project description:BackgroundKidney biopsy is the standard of care for the diagnosis of various kidney diseases. In particular, chronic histopathologic lesions, such as interstitial fibrosis and tubular atrophy, can provide prognostic information regarding chronic kidney disease progression. In this study, we aimed to evaluate historadiological correlations between CT-based radiomic features and chronic histologic changes in native kidney biopsies and to construct and validate a radiomics-based prediction model for chronicity grade.MethodsWe included patients aged ≥ 18 years who underwent kidney biopsy and abdominal CT scan within a week before kidney biopsy. Left kidneys were three-dimensionally segmented using a deep learning model based on the 3D Swin UNEt Transformers architecture. We additionally defined isovolumic cortical regions of interest near the lower pole of the left kidneys. Shape, first-order, and high-order texture features were extracted after resampling and kernel normalization. Correlations and diagnostic metrics between extracted features and chronic histologic lesions were examined. A machine learning-based radiomic prediction model for moderate chronicity was developed and compared according to the segmented regions of interest (ROI).ResultsOverall, moderate correlations with statistical significance (P < 0.05) were found between chronic histopathologic grade and top-ranked radiomic features. Total parenchymal features were more strongly correlated than cortical ROI features, and texture features were more highly ranked. However, conventional imaging markers, including kidney length, were poorly correlated. Top-ranked individual radiomic features had areas under receiver operating characteristic curves (AUCs) of 0.65 to 0.74. Developed radiomics models for moderate-to-severe chronicity achieved AUCs of 0.89 (95% confidence interval [CI] 0.75-0.99) and 0.74 (95% CI 0.52-0.93) for total parenchymal and cortical ROI features, respectively.ConclusionSignificant historadiological correlations were identified between CT-based radiomic features and chronic histologic changes in native kidney biopsies. Our findings underscore the potential of CT-based radiomic features and their prediction model for the non-invasive assessment of kidney fibrosis.

Dataset Information

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

Purpose

Materials and methods

Results

Conclusion

Critical relevance statement

Publications

Virtual biopsy using CT radiomics for evaluation of disagreement in pathology between endoscopic biopsy and postoperative specimens in patients with gastric cancer: a dual-energy CT generalizability study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets