Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

Prata, Francesco; Anceschi, Umberto; Cordelli, Ermanno; Faiella, Eliodoro; Civitella, A.; Tuzzolo, P.; Iannuzzi, Andrea; Ragusa, Alberto; Esperto, Francesco; Prata, Salvatore Mario; Sicilia, Rosa; Muto, Giovanni; Grasso, Rosario Francesco; Scarpa, Roberto; Soda, Paolo; Simone, Giuseppe; Papalia, Rocco

doi:10.3390/curroncol30020157

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Jamshidi et al fused multiple models to detect csPCa, reaching an AUC of 1.000, however they only utilized images from a singleinstitution experience of 32 patients, greatly reducing generalizability [15]. Similarly, Prata et al introduced a novel radiomics tool that integrated MRI features with clinical metrics to predict csPCa, achieving an AUC of 0.804 in the cohort of 91 patients they investigated [16]. Rodrigues et al utilized MRI scans from 181 patients and experimented with fusing various classifiers [17].…”

Section: Radiomics and Ai Pioneering Detection Of Clinically-signific...mentioning

confidence: 99%

AI-powered radiomics: revolutionizing detection of urologic malignancies

Gelikman,

Rais-Bahrami,

Pinto

et al. 2023

Current Opinion in Urology

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Purpose of Review This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. Recent Findings As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. Summary Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.

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Section: Radiomics and Ai Pioneering Detection Of Clinically-signific...mentioning

confidence: 99%

AI-powered radiomics: revolutionizing detection of urologic malignancies

Gelikman,

Rais-Bahrami,

Pinto

et al. 2023

Current Opinion in Urology

View full text Add to dashboard Cite

show abstract

“…PCa shares biological similarity with luminal epithelial cells and is primarily driven by AR signaling [4,11,12]. PCa is dormant, and newly diagnosed cases only need active surveillance by periodically performed prostate specific antigen (PSA) testing, digital rectal examination (DRE) and biopsy [13,14].…”

Section: Introductionmentioning

confidence: 99%

The Molecular Biology of Prostate Cancer Stem Cells: From the Past to the Future

Zhou

Jia

et al. 2023

IJMS

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Prostate cancer (PCa) continues to rank as the second leading cause of cancer-related mortality in western countries, despite the golden treatment using androgen deprivation therapy (ADT) or anti-androgen therapy. With decades of research, scientists have gradually realized that the existence of prostate cancer stem cells (PCSCs) successfully explains tumor recurrence, metastasis and therapeutic failure of PCa. Theoretically, eradication of this small population may improve the efficacy of current therapeutic approaches and prolong PCa survival. However, several characteristics of PCSCs make their diminishment extremely challenging: inherent resistance to anti-androgen and chemotherapy treatment, over-activation of the survival pathway, adaptation to tumor micro-environments, escape from immune attack and being easier to metastasize. For this end, a better understanding of PCSC biology at the molecular level will definitely inspire us to develop PCSC targeted approaches. In this review, we comprehensively summarize signaling pathways responsible for homeostatic regulation of PCSCs and discuss how to eliminate these fractional cells in clinical practice. Overall, this study deeply pinpoints PCSC biology at the molecular level and provides us some research perspectives.

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T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

Duenweg,

Bobholz,

Barrett

et al. 2023

Cancers

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Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.

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Radiomic Machine-Learning Analysis of Multiparametric Magnetic Resonance Imaging in the Diagnosis of Clinically Significant Prostate Cancer: New Combination of Textural and Clinical Features

Cited by 5 publications

References 28 publications

AI-powered radiomics: revolutionizing detection of urologic malignancies

AI-powered radiomics: revolutionizing detection of urologic malignancies

The Molecular Biology of Prostate Cancer Stem Cells: From the Past to the Future

T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence

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