MRI-based automated machine learning model for preoperative identification of variant histology in muscle-invasive bladder carcinoma

Tumor necrosis in magnetic resonance imaging predicts urothelial carcinoma with squamous differentiation in muscle-invasive bladder carcinoma

PURPOSE

This study investigated radiologic features on preoperative MRI to differentiate urothelial carcinoma with squamous differentiation (UCSD) from conventional urothelial carcinoma (UC) in muscle-invasive bladder carcinoma.

METHODS

Ninety-nine patients who underwent radical cystectomy and a preoperative bladder MRI scan within three months before surgery were retrospectively enrolled. Various MRI features, including tumor length, location, multiplicity, long-to-short axis ratio, morphology, radiologic stage, and degree of severe necrosis, were analyzed. Univariable and multivariable logistic regression analyses were performed to identify MRI features predictive of UCSD. The diagnostic performance of a significant MRI feature was assessed using 5-fold cross-validation.

RESULTS

Among the MRI features, significant radiologic findings associated with UCSD in the univariable analysis included heterogeneous tumor signal intensity in T2-weighted images (odds ratio [OR], 3.365; 95% confidence interval [CI], 1.213-9.986; P = 0.022) and contrast-enhanced T1-weighted images (OR, 4.428; 95% CI, 1.519-12.730; P = 0.007), as well as marked (≥ 50%) severe necrosis (OR, 17.100; 95% CI, 4.699-73.563; P < 0.001). In the multivariable analysis, marked (≥ 50%) severe necrosis (odds ratio [OR], 13.755; 95% confidence interval [CI], 2.796-89.118; P = 0.004) was a significant predictor of UCSD. Marked (≥ 50%) severe necrosis showed a high specificity of 95.0% with a precision of 65.0% for diagnosing UCSD based on 5-fold cross-validation.

CONCLUSION

Preoperative bladder MRI revealing marked severe necrosis may be indicative of UCSD and can assist in distinguishing it from conventional UC.

The role of MRI in muscle-invasive bladder cancer: an update from the last two years

PURPOSE OF REVIEW

Muscle invasive bladder cancer (MIBC) is aggressive and requires radical cystectomy and neoadjuvant therapy, yet over 40% of patients face recurrence. The loss of the bladder also significantly reduces quality of life. Accurate staging, crucial for treatment decisions, is typically done through transurethral resection (TURBT), but inconsistencies in pathology affect diagnosis in 25% of cases. MRI is the most precise imaging method for evaluating local tumor invasiveness. This review discusses recent advances in MRI for staging MIBC and predicting responses to neoadjuvant therapy.

RECENT FINDINGS

Vesical imaging - reporting and data system (VI-RADS) accuracy may improve if combined with ADC maps and tumor contact length, while a bi-parametric MRI approach without contrast could reduce side effects without losing diagnostic precision, though evidence is mixed. VI-RADS shows promise in predicting neoadjuvant therapy responses, and the new nacVI-RADS score is in development. Non-Gaussian diffusion-weighted imaging techniques and machine learning could enhance accuracy but need more integration with mpMRI. VI-RADS may assist in evaluating responses in bladder-sparing regimens. Urodrill, an MRI-guided biopsy, aims to replace diagnostic TURBT but needs more accuracy data.

SUMMARY

MRI in MIBC is evolving, offering potential for accurate local staging and reduced side effects by avoiding TURBT. Predicting neoadjuvant treatment response could guide personalized treatment and bladder preservation. Larger trials are needed to validate these findings.

Progress of Multiparameter Magnetic Resonance Imaging in Bladder Cancer: A Comprehensive Literature Review

Magnetic resonance imaging (MRI) has been proven to be an indispensable imaging method in bladder cancer, and it can accurately identify muscular invasion of bladder cancer. Multiparameter MRI is a promising tool widely used for preoperative staging evaluation of bladder cancer. Vesical Imaging-Reporting and Data System (VI-RADS) scoring has proven to be a reliable tool for local staging of bladder cancer with high accuracy in preoperative staging, but VI-RADS still faces challenges and needs further improvement. Artificial intelligence (AI) holds great promise in improving the accuracy of diagnosis and predicting the prognosis of bladder cancer. Automated machine learning techniques based on radiomics features derived from MRI have been utilized in bladder cancer diagnosis and have demonstrated promising potential for practical implementation. Future work should focus on conducting more prospective, multicenter studies to validate the additional value of quantitative studies and optimize prediction models by combining other biomarkers, such as urine and serum biomarkers. This review assesses the value of multiparameter MRI in the accurate evaluation of muscular invasion of bladder cancer, as well as the current status and progress of its application in the evaluation of efficacy and prognosis.