A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

Simple Summary Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer. However, current techniques of diagnosis are susceptible to pathologist variability, and histopathological prognostic methods are insufficient to cover all features of muscle-invasive bladder cancer. In this work, we developed weakly supervised models based on deep learning for the diagnosis of bladder cancer and prediction of overall survival in muscle-invasive bladder cancer patients using whole slide digitized histological images in two cohorts. Encouragingly, results showed that our models can not only assist clinicians in the accurate diagnosis of bladder cancer, but also facilitate differential risk stratification in patients with muscle-invasive bladder cancer and improve personalized treatment decisions accordingly. Furthermore, the regions most relevant for diagnosis or prognosis can be further analyzed to increase the amount of information extracted from pathological images. Finally, we identified six genes closely related to cancer progression based on the predicted risk scores, which potentially led to new biomarker discoveries. Abstract (1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

https://www.mdpi.com/2072-6694/14/23/5807/pdf?version=1669367602

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

To develop and validate a multiphase CT-based radiomics model for preoperative risk stratification of patients with localized clear cell renal cell carcinoma (ccRCC).A total of 425 patients with localized ccRCC were enrolled and divided into training, validation, and external testing cohorts. Radiomics features were extracted from three-phase CT images (unenhanced, arterial, and venous), and radiomics signatures were constructed by the least absolute shrinkage and selection operator (LASSO) regression algorithm. The radiomics score (Rad-score) for each patient was calculated. The radiomics model was established and visualized as a nomogram by incorporating significant clinical factors and Rad-score. The predictive performance of the radiomics model was evaluated by the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).The AUC of the triphasic radiomics signature reached 0.862 (95% CI: 0.809-0.914), 0.853 (95% CI: 0.785-0.921), and 0.837 (95% CI: 0.714-0.959) in three cohorts, respectively, which were higher than arterial, venous, and unenhanced radiomics signatures. Multivariate logistic regression analysis showed that Rad-score (OR: 4.066, 95% CI: 3.495-8.790) and renal vein invasion (OR: 12.914, 95% CI: 1.118-149.112) were independent predictors and used to develop the radiomics model. The radiomics model showed good calibration and discrimination and yielded an AUC of 0.872 (95% CI: 0.821-0.923), 0.865 (95% CI: 0.800-0.930), and 0.848 (95% CI: 0.728-0.967) in three cohorts, respectively. DCA showed the clinical usefulness of the radiomics model in predicting the Leibovich risk groups.The radiomics model can be used as a non-invasive and useful tool to predict the Leibovich risk groups for localized ccRCC patients.The triphasic CT-based radiomics model achieved favorable performance in preoperatively predicting the Leibovich risk groups in patients with localized ccRCC. Therefore, it can be used as a non-invasive and effective tool for preoperative risk stratification of patients with localized ccRCC.• The triphasic CT-based radiomics signature achieves better performance than the single-phase radiomics signature. • Radiomics holds prospects in preoperatively predicting the Leibovich risk groups for ccRCC. • This study provides a non-invasive method to stratify patients with localized ccRCC. 

Validity of a multiphase CT-based radiomics model in predicting the Leibovich risk groups for localized clear cell renal cell carcinoma: an exploratory study

ABSTRACT Immunotherapy has become a cornerstone for the treatment of renal cell carcinoma. Nevertheless, some patients are resistant to immune checkpoint inhibitors. The possibility to identify patients who cannot benefit from immunotherapy is a relevant clinical challenge. We analyzed the association between several radiomics features and response to immunotherapy in 53 patients treated with checkpoint inhibitors for advanced renal cell carcinoma. We found that the following features are associated with progression of disease as best tumor response: F_stat.range (p < .0004), F_stat.max (p < .0007), F_stat.var (p < .0016), F_stat.uniformity (p < .0020), F_stat.90thpercentile (p < .0050). Gross tumor volumes characterized by high values of F_stat.var and F_stat.max (greater than 60,000 and greater than 300, respectively) are most likely related to a high risk of progression. Further analyses are warranted to confirm these results. Radiomics, together with other potential predictive factors, such as gut microbiota, genetic features or circulating immune molecules, could allow a personalized treatment for patients with advanced renal cell carcinoma.

https://doi.org/10.1080/21645515.2023.2172926

Radiomics to predict immunotherapy efficacy in advanced renal cell carcinoma: A retrospective study

/pdf/state-of-the-art-of-nuclear-medicine-and-molecular-imaging-2pb9w960.pdf

State-of-the-art of nuclear medicine and molecular imaging in China: after the first 66 years (1956–2022)

The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study

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The radiomics-based tumor heterogeneity adds incremental value to the existing prognostic models for predicting outcome in localized clear cell renal cell carcinoma: a multicenter study