Development and Validation of an MRI-Based Radiomics Signature for the Preoperative Prediction of Lymph Node Metastasis in Bladder Cancer

Abstract

BackgroundPreoperative lymph node (LN) status is important for the treatment of bladder cancer (BCa). However, a proportion of patients are at high risk for inaccurate clinical nodal staging by current methods. Here, we report an accurate magnetic resonance imaging (MRI)-based radiomics signature for the individual preoperative prediction of LN metastasis in BCa.MethodsIn total, 103 eligible BCa patients were divided into a training set (n = 69) and a validation set (n = 34). And 718 radiomics features were extracted from the cancerous volumes of interest (VOIs) on T2-weighted MRI images. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm in the training set, whose performance was assessed and then validated in the validation set. Stratified analyses were also performed. Based on the multivariable logistic regression analysis, a radiomics nomogram was developed incorporating the radiomics signature and selected clinical predictors. Discrimination, calibration and clinical usefulness of the nomogram were assessed.FindingsConsisting of 9 selected features, the radiomics signature showed a favorable discriminatory ability in the training set with an AUC of 0.9005, which was confirmed in the validation set with an AUC of 0.8447. Encouragingly, the radiomics signature also showed good discrimination in the MRI-reported LN negative (cN0) subgroup (AUC, 0.8406). The nomogram, consisting of the radiomics signature and the MRI-reported LN status, showed good calibration and discrimination in the training and validation sets (AUC, 0.9118 and 0.8902, respectively). The decision curve analysis indicated that the nomogram was clinically useful.InterpretationThe MRI-based radiomics nomogram has the potential to be used as a non-invasive tool for individualized preoperative prediction of LN metastasis in BCa. External validation is further required prior to clinical implementation.

Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach

“…Subsequently, analysis of the radiomics‐derived data can produce accurate, robust tools for disease detection, diagnosis, prognostic evaluation, etc. In recent years, radiomics has drawn increased attention in cancer research, including in BCa . In addition, literature studies also have indicated the possibility of applying radiomics to accessing the depth of bladder tumor involvement .…”

“…In recent years, radiomics has drawn increased attention in cancer research, including in BCa. [21][22][23] In addition, literature studies also have indicated the possibility of applying radiomics to accessing the depth of bladder tumor involvement. 25,26 Consequently, we sought to construct a more reliable radiomics tool for preoperatively differentiating MIBC from NMIBC.…”

“…Currently, radiomics is mainly used in oncology to facilitate improved clinical decision making . Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response . In addition, 2 MRI‐based radiomics signatures to differentiate MIBC from NMIBC have been developed .…”

“…[18][19][20] Indeed, radiomics can also be applied in patients with BCa, such as predicting lymph node metastasis and treatment response. [21][22][23][24] In addition, 2 MRI-based radiomics signatures to differentiate MIBC from NMIBC have been developed. 25,26 However, further studies are warranted because of limitations.…”

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

“…Recent studies have shown the value of radiomics features from primary lesion in predicting the lymph node metastasis for different cancer sites, e.g., CT radiomics features in colorectal cancer (33), MRI/CT radiomics features in bladder cancer (34,35) and esophageal cancer (36). For breast cancer, two recent studies have assessed the value of radiomics features extracted from the primary tumor region at DCE-MRI and diffusion-weighted MRI (DWI) in predicting sentinel lymph node metastasis, where the reported AUC, sensitivity and specificity ranging from 0.805 to 0.869, 0.700-0.778, and 0.747-861 respectively (9,10).…”