A virtual biopsy study of microsatellite instability in gastric cancer based on deep learning radiomics

CT-based radiomics model for predicting perineural invasion status in gastric cancer

PURPOSE

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients.

METHODS

This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models.

RESULTS

A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility.

CONCLUSION

We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Advances and challenges in gastric cancer testing: the role of biomarkers

Advances in the identification of molecular biomarkers and the development of targeted therapies have enhanced the prognosis of patients with advanced gastric cancer. Several established biomarkers have been widely integrated into routine clinical diagnostics of gastric cancer to guide personalized treatment. Human epidermal growth factor receptor 2 (HER2) was the first molecular biomarker to be used in gastric cancer with trastuzumab being the first approved targeted therapy for HER2-positive gastric cancer. Programmed death-ligand 1 positivity and microsatellite instability can guide the use of immunotherapies, such as pembrolizumab and nivolumab. More recently, zolbetuximab has been approved for patients with claudin 18.2-positive diseases in some countries. More targeted therapies, including savolitinib for MET-positive patients, are currently under clinical investigation. However, the clinical application of these diagnostic approaches could be hampered by many existing challenges, including invasive and costly sampling methods, variability in immunohistochemistry interpretation, high costs and long turnaround times for next-generation sequencing, the absence of standardized and clinically validated diagnostic cut-off values for some biomarkers, and tumor heterogeneity. Novel testing and analysis techniques, such as artificial intelligence-assisted image analysis and multiplex immunohistochemistry, and emerging therapeutic strategies, including combination therapies that integrate immune checkpoint inhibitors with targeted therapies, offer potential solutions to some of these challenges. This article reviews recent progress in gastric cancer testing, outlines current challenges, and explores future directions for biomarker testing and targeted therapy for gastric cancer.

Development and validation of a CT-based radiomic nomogram for predicting surgical resection risk in patients with adhesive small bowel obstruction

BACKGROUND

Adhesive small bowel obstruction (ASBO) is a common emergency that requires prompt medical attention, and the timing of surgical intervention poses a considerable challenge. Although computed tomography (CT) is widely used, its effectiveness in accurately identifying bowel strangulation is limited. The potential of radiomics models to predict the necessity for surgical resection in ASBO cases is not yet fully explored.

OBJECTIVES

The aim of this study is to identify risk factors for surgical resection in patients with ASBO and to develop a predictive model that integrates radiomic features with clinical data. This model designed to estimate the likelihood of surgical intervention and aid in clinical decision-making for acute ASBO cases.

METHODS

From January 2019 to February 2022, we enrolled 188 ASBO patients from our hospital, dividing them randomly into a training cohort (n = 131) and a test cohort (n = 57) using a 7:3 ratio. We collected baseline clinical data and extracted radiomic features from CT images to compute a radiomic score (Rad-score). A nomogram was developed that combines clinical characteristics and Rad-score. The performance of clinical, radiomic, and combined nomogram models was evaluated in both cohorts.

RESULTS

Of the 188 patients, 92 underwent surgical resection, while 96 did not. The nomogram integrated factors such as white blood cell count, duration of obstruction, and preoperative infection indicators (fever, tachycardia, peritonitis), along with CT findings (elevated wall density, thickened wall, mesenteric fluid, ascites, bowel wall gas, small bowel feces, and hyperdensity of mesenteric fat) (p < 0.1). This combined model accurately predicted the need for surgical resection, with area under the curve (AUC) values of 0.761 (95% CI, 0.628-0.893) for the test cohort. Calibration curves showed strong agreement between predicted and observed outcomes, and decision curve analysis validated the model's utility for acute ASBO cases.

CONCLUSION

We developed and validated a CT-based nomogram that combines radiomic features with clinical data to predict the risk of surgical resection in ASBO patients. This tool offers valuable support for treatment planning and decision-making in emergent situations.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Accuracy of machine learning in diagnosing microsatellite instability in gastric cancer: A systematic review and meta-analysis

BACKGROUND

Significant challenges persist in the early identification of microsatellite instability (MSI) within current clinical practice. In recent years, with the growing utilization of machine learning (ML) in the diagnosis and management of gastric cancer (GC), numerous researchers have explored the effectiveness of ML methodologies in detecting MSI. Nevertheless, the predictive value of these approaches still lacks comprehensive evidence. Accordingly, this study was carried out to consolidate the accuracy of ML in the prompt detection of MSI in GC.

METHODS

PubMed, the Cochrane Library, the Web of Science, and Embase were retrieved up to March 20, 2024. The risk of bias in the encompassed studies was evaluated utilizing a risk assessment tool for predictive models. Models were then subjected to subgroup analysis based on the modeling variables.

RESULTS

A total of 12 studies, encompassing 11,912 patients with GC, satisfied the predefined inclusion criteria. ML models established in these studies were primarily based on pathological images, clinical features, and radiomics. The results suggested that in the validation sets, the pathological image-based models had a synthesized c-index of 0.86 [95 % CI (0.83-0.89)], with sensitivity and specificity being 0.86 [95 % CI (0.76-0.92)] and 0.83 [95 % CI (0.78-0.87)], respectively; radiomics feature-based models achieved respective values of 0.87 [95 % CI (0.81-0.92)], 0.77 [95 % CI (0.70-0.83)] and 0.81 [95 % CI (0.74-0.87)]; radiomics feature-based models + clinical feature-based models achieved respective values of 0.87 [95 % CI (0.81-0.93)], 0.78 [95 % CI (0.70-0.84)] and 0.79 [95 % CI (0.69-0.86)].

CONCLUSIONS

ML has demonstrated optimal performance in detecting MSI in GC and could serve as a prospective early adjunctive detection tool for MSI in GC. Future research should contemplate minimally invasive or non-invasive, readily collectible, and efficient predictors to augment the predictive accuracy of ML.

A radiomics signature derived from CT imaging to predict MSI status and immunotherapy outcomes in gastric cancer: a multi-cohort study

BACKGROUND

Accurate microsatellite instability (MSI) testing is essential for identifying gastric cancer (GC) patients eligible for immunotherapy. We aimed to develop and validate a CT-based radiomics signature to predict MSI and immunotherapy outcomes in GC.

METHODS

This retrospective multicohort study included a total of 457 GC patients from two independent medical centers in China and The Cancer Imaging Archive (TCIA) databases. The primary cohort (n = 201, center 1, 2017-2022), was used for signature development via Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression analysis. Two independent immunotherapy cohorts, one from center 1 (n = 184, 2018-2021) and another from center 2 (n = 43, 2020-2021), were utilized to assess the signature's association with immunotherapy response and survival. Diagnostic efficiency was evaluated using the area under the receiver operating characteristic curve (AUC), and survival outcomes were analyzed via the Kaplan-Meier method. The TCIA cohort (n = 29) was included to evaluate the immune infiltration landscape of the radiomics signature subgroups using both CT images and mRNA sequencing data.

RESULTS

Nine radiomics features were identified for signature development, exhibiting excellent discriminative performance in both the training (AUC: 0.851, 95%CI: 0.782, 0.919) and validation cohorts (AUC: 0.816, 95%CI: 0.706, 0.926). The radscore, calculated using the signature, demonstrated strong predictive abilities for objective response in immunotherapy cohorts (AUC: 0.734, 95%CI: 0.662, 0.806; AUC: 0.724, 95%CI: 0.572, 0.877). Additionally, the radscore showed a significant association with PFS and OS, with GC patients with a low radscore experiencing a significant survival benefit from immunotherapy. Immune infiltration analysis revealed significantly higher levels of CD8 + T cells, activated CD4 + B cells, and TNFRSF18 expression in the low radscore group, while the high radscore group exhibited higher levels of T cells regulatory and HHLA2 expression.

CONCLUSION

This study developed a robust radiomics signature with the potential to serve as a non-invasive biomarker for GC's MSI status and immunotherapy response, demonstrating notable links to post-immunotherapy PFS and OS. Additionally, distinct immune profiles were observed between low and high radscore groups, highlighting their potential clinical implications.