Pathomics in urology

Exploring the Use of Artificial Intelligence in the Management of Prostate Cancer

PURPOSE OF REVIEW

This review aims to explore the current state of research on the use of artificial intelligence (AI) in the management of prostate cancer. We examine the various applications of AI in prostate cancer, including image analysis, prediction of treatment outcomes, and patient stratification. Additionally, the review will evaluate the current limitations and challenges faced in the implementation of AI in prostate cancer management.

RECENT FINDINGS

Recent literature has focused particularly on the use of AI in radiomics, pathomics, the evaluation of surgical skills, and patient outcomes. AI has the potential to revolutionize the future of prostate cancer management by improving diagnostic accuracy, treatment planning, and patient outcomes. Studies have shown improved accuracy and efficiency of AI models in the detection and treatment of prostate cancer, but further research is needed to understand its full potential as well as limitations.

Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma

BACKGROUND

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of clear cell renal cell carcinoma (ccRCC).

METHODS

A total of 483 whole slide images (WSIs) data from three patient cohorts were retrospectively analyzed. We performed machine learning algorithm to identify optimal digital pathological features and constructed machine learning-based pathomics signature (MLPS) for ccRCC patients. Prognostic performance of the prognostic model was also verified in two independent validation cohorts.

RESULTS

MLPS could significantly distinguish ccRCC patients with high survival risk, with hazard ratio of 15.05, 4.49 and 1.65 in three independent cohorts, respectively. Cox regression analysis revealed that the MLPS could act as an independent prognostic factor for ccRCC patients. Integration nomogram based on MLPS, tumour stage system and tumour grade system improved the current survival prediction accuracy for ccRCC patients, with area under curve value of 89.5%, 90.0%, 88.5% and 85.9% for 1-, 3-, 5- and 10-year disease-free survival prediction.

DISCUSSION

The machine learning-based pathomics signature could act as a novel prognostic marker for patients with ccRCC. Nevertheless, prospective studies with multicentric patient cohorts are still needed for further verifications.

Radiogenomics in prostate cancer evaluation

PURPOSE OF REVIEW

Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management.

RECENT FINDINGS

Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice.

SUMMARY

In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.

Kidney cancer management 3.0: can artificial intelligence make us better?

PURPOSE OF REVIEW

Artificial intelligence holds tremendous potential for disrupting clinical medicine. Here we review the current role of artificial intelligence in the kidney cancer space.

RECENT FINDINGS

Machine learning and deep learning algorithms have been developed using information extracted from radiomic, histopathologic, and genomic datasets of patients with renal masses.

SUMMARY

Although artificial intelligence applications in medicine are still in their infancy, they already hold immediate promise to improve accuracy of renal mass characterization, grade, and prognostication. As algorithms become more robust and generalizable, artificial intelligence is poised to significantly disrupt kidney cancer care.

Novel transurethral resection technologies and training modalities in the management of nonmuscle invasive bladder cancer: a comprehensive review

PURPOSE OF REVIEW

Conventional transurethral resection (TURBT) with tumor fragmentation is the primary step in the surgical treatment of nonmuscle invasive bladder cancer. Recently, new surgical techniques and training modalities have emerged with the aim to overcome short-comings of TURBT and improve oncologic outcomes. In this review, we provide a comprehensive update of recent techniques/techniques that aim to improve upon conventional TURBT and beyond.

RECENT FINDINGS

A systemic approach during conventional TURBT that features the use of a surgical checklist has been shown to improve recurrence-free survival. Several simulators have been developed and validated to provide additional training opportunities. However, transfer of improved simulator performance into real world surgery still requires validation. While there is no convincing data that demonstrate superior outcomes with bipolar TURBT, en-bloc resection already promises to offer lower rates of complications as well as potentially lower recurrence probabilities in select patients.

SUMMARY

TURBT remains the quintessential procedure for the diagnosis and treatment of bladder cancer. Urologists need to be aware of the importance and challenges of this procedure. Aside of embracing new resection techniques and a conceptual-systematic approach, training opportunities should be expanded upon to improve patient outcomes.

A Novel Nomogram Based on Machine Learning-Pathomics Signature and Neutrophil to Lymphocyte Ratio for Survival Prediction of Bladder Cancer Patients

Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of bladder cancer (BCa). In addition, how neutrophil to lymphocyte ratio (NLR) could be used for prognosis prediction of BCa patients has not been fully understood. In this study, we collected 508 whole slide images (WSIs) of hematoxylin-eosin strained BCa slices and NLR value from the Shanghai General Hospital and The Cancer Genome Atlas (TCGA), which were further processed for nuclear segmentation. Cross-verified prediction models for predicting clinical prognosis were constructed based on machine learning methods. Six WSIs features were selected for the construction of pathomics-based prognosis model, which could automatically distinguish BCa patients with worse survival outcomes, with hazard ratio value of 2.19 in TCGA cohort (95% confidence interval: 1.63-2.94, p <0.0001) and 3.20 in General cohort (95% confidence interval: 1.75-5.87, p = 0.0014). Patients in TCGA cohort with high NLR exhibited significantly worse clinical survival outcome when compared with patients with low NLR (HR = 2.06, 95% CI: 1.29-3.27, p <0.0001). External validation in General cohort also revealed significantly poor prognosis in BCa patients with high NLR (HR = 3.69, 95% CI: 1.83-7.44 p <0.0001). Univariate and multivariate cox regression analysis proved that both the MLPS and the NLR could act as independent prognostic factor for overall survival of BCa patients. Finally, a novel nomogram based on MLPS and NLR was constructed to improve their clinical practicability, which had excellent agreement with actual observation in 1-, 3- and 5-year overall survival prediction. Decision curve analyses both in the TCGA cohort and General cohort revealed that the novel nomogram acted better than both the tumor grade system in prognosis prediction. Our novel nomogram based on MLPS and NLR could act as an excellent survival predictor and provide a scalable and cost-effective method for clinicians to facilitate individualized therapy. Nevertheless, prospective studies are still needed for further verifications.

Accuracy of Frozen Section Analysis of Urethral and Ureteral Margins During Radical Cystectomy for Bladder Cancer: A Systematic Review and Diagnostic Meta-Analysis

CONTEXT

The question of the ability of frozen section analysis (FSA) to accurately detect malignant pathology intraoperatively has been discussed for many decades.

OBJECTIVE

We aimed to conduct a systematic review and meta-analysis assessing the diagnostic estimates of FSA of the urethral and ureteral margins in patients treated with radical cystectomy (RC) for bladder cancer (BCa).

EVIDENCE ACQUISITION

The MEDLINE and EMBASE databases were searched in February 2021 for studies analyzing the association between FSA and the final urethral and ureteral margin status in patients treated with RC for BCa. The primary endpoint was the value of pathologic detection of urethral and ureteral malignant involvement with FSA during RC compared with the final margin status. We included studies that provided true positive, true negative, false positive, and false negative values for FSA, which allowed us to calculate the diagnostic estimates.

EVIDENCE SYNTHESIS

Fourteen studies, comprising 8208 patients, were included in the quantitative synthesis. Forest plots revealed that the pooled sensitivity and specificity for FSA of urethral margins during RC were 0.83 (95% confidence interval [CI] 0.38-0.97) and 0.95 (95% CI 0.91-0.97), respectively. While for the FSA of ureteral margins, the pooled sensitivity and specificity were 0.77 (95% CI 0.67-0.84) and 0.97 (95% CI 0.95-0.98), respectively. Calculated diagnostic odds ratios indicated high FSA effectiveness, and patients with a positive urethral or ureteral margin at final pathology are over 100 times more likely to have positive FSA than patients without margin involvement at final pathology. Area under the curves of 96.6% and 96.7% were reached for FSA detection of urethral and ureteral tumor involvement, respectively.

CONCLUSIONS

Intraoperative FSA demonstrated high diagnostic performance in detecting both urethral and ureteral malignant involvement at the time of RC for BCa. FSA of both urethral and ureteral margins during RC is accurate enough to be of great value in the routine management of BCa patients treated with RC. While its specificity was great to guide intraoperative decision-making, its sensitivity remains suboptimal yet.

PATIENT SUMMARY

We believe that the frozen section analysis of both urethral and ureteral margins during radical cystectomy should be considered more often in urologic practice, until quality of life-based cost-effectiveness studies can identify patients within each institution who are unlikely to benefit from it.

A panel of systemic inflammatory response biomarkers for outcome prediction in patients treated with radical cystectomy for urothelial carcinoma

Objectives

To determine the predictive and prognostic value of a panel of systemic inflammatory response (SIR) biomarkers relative to established clinicopathological variables in order to improve patient selection and facilitate more efficient delivery of peri‐operative systemic therapy.

Materials and Methods

The preoperative serum levels of a panel of SIR biomarkers, including albumin–globulin ratio, neutrophil–lymphocyte ratio, De Ritis ratio, monocyte–lymphocyte ratio and modified Glasgow prognostic score were assessed in 4199 patients treated with radical cystectomy for clinically non‐metastatic urothelial carcinoma of the bladder. Patients were randomly divided into a training and a testing cohort. A machine‐learning‐based variable selection approach (least absolute shrinkage and selection operator regression) was used for the fitting of several multivariable predictive and prognostic models. The outcomes of interest included prediction of upstaging to carcinoma invading bladder muscle (MIBC), lymph node involvement, pT3/4 disease, cancer‐specific survival (CSS) and recurrence‐free survival (RFS). The discriminatory ability of each model was either quantified by area under the receiver‐operating curves or by the C‐index. After validation and calibration of each model, a nomogram was created and decision‐curve analysis was used to evaluate the clinical net benefit.

Results

For all outcome variables, at least one SIR biomarker was selected by the machine‐learning process to be of high discriminative power during the fitting of the models. In the testing cohort, model performance evaluation for preoperative prediction of lymph node metastasis, ≥pT3 disease and upstaging to MIBC showed a 200‐fold bootstrap‐corrected area under the curve of 67.3%, 73% and 65.8%, respectively. For postoperative prognosis of CSS and RFS, a 200‐fold bootstrap corrected C‐index of 73.3% and 72.2%, respectively, was found. However, even the most predictive combinations of SIR biomarkers only marginally increased the discriminative ability of the respective model in comparison to established clinicopathological variables.

Conclusion

While our machine‐learning approach for fitting of the models with the highest discriminative ability incorporated several previously validated SIR biomarkers, these failed to improve the discriminative ability of the models to a clinically meaningful degree. While the prognostic and predictive value of such cheap and readily available biomarkers warrants further evaluation in the age of immunotherapy, additional novel biomarkers are still needed to improve risk stratification.