A nomogram to predict the absence of clinically significant prostate cancer in males with negative MRI

PURPOSE: To create a nomogram to predict the absence of clinically significant prostate cancer (CSPCa) in males with non-suspicion multiparametric magnetic resonance imaging (mpMRI) undergoing prostate biopsy (PBx). MATERIALS AND METHODS: We identified consecutive patients who underwent 3T mpMRI followed by PBx for suspicion of PCa or surveillance follow-up. All patients had Prostate Imaging Reporting and Data System score 1-2 (negative mpMRI). CSPCa was defined as Grade Group ≥2. Multivariate logistic regression analysis was performed via backward elimination. Discrimination was evaluated with area under the receiver operating characteristic (AUROC). Internal validation with 1,000x bootstrapping for estimating the optimism corrected AUROC. RESULTS: Total 327 patients met inclusion criteria. The median (IQR) age and PSA density (PSAD) were 64 years (58-70) and 0.10 ng/mL2 (0.07-0.15), respectively. Biopsy history was as follows: 117 (36%) males were PBx-naive, 130 (40%) had previous negative PBx and 80 (24%) had previous positive PBx. The majority were White (65%); 6% of males self-reported Black. Overall, 44 (13%) patients were diagnosed with CSPCa on PBx. Black race, history of previous negative PBx and PSAD ≥0.15ng/mL2 were independent predictors for CSPCa on PBx and were included in the nomogram. The AUROC of the nomogram was 0.78 and the optimism corrected AUROC was 0.75. CONCLUSIONS: Our nomogram facilitates evaluating individual probability of CSPCa on PBx in males with PIRADS 1-2 mpMRI and may be used to identify those in whom PBx may be safely avoided. Black males have increased risk of CSPCa on PBx, even in the setting of PIRADS 1-2 mpMRI.

Artificial intelligence trained with integration of multiparametric MR-US imaging data and fusion biopsy trajectory-proven pathology data for 3D prediction of prostate cancer: A proof-of-concept study.

BACKGROUND: We aimed to develop an artificial intelligence (AI) algorithm that predicts the volume and location of clinically significant cancer (CSCa) using convolutional neural network (CNN) trained with integration of multiparametric MR-US image data and MRI-US fusion prostate biopsy (MRI-US PBx) trajectory-proven pathology data. METHODS: Twenty consecutive patients prospectively underwent MRI-US PBx, followed by robot-assisted radical prostatectomy (RARP). The AI algorithm was trained with the integration of MR-US image data with a MRI-US PBx trajectory-proven pathology. The relationship with the 3D-cancer-mapping of RARP specimens was compared between AI system-suggested 3D-CSCa mapping and an experienced radiologist's suggested 3D-CSCa mapping on MRI alone according to the Prostate Imaging Reporting and Data System (PI-RADS) version 2. The characteristics of detected and undetected tumors at AI were compared in 22,968 image data. The relationships between CSCa volumes and volumes predicted by AI as well as the radiologist's reading based on PI-RADS were analyzed. RESULTS: The concordance of the CSCa center with that in RARP specimens was significantly higher in the AI prediction than the radiologist' reading (83% vs. 54%, p = 0.036). CSCa volumes predicted with AI were more accurate (r = 0.90, p < 0.001) than the radiologist's reading. The limitations include that the elastic fusion technology has its own registration error. CONCLUSIONS: We presented a novel pilot AI algorithm for 3D prediction of PCa. AI was trained by integration of multiparametric MR-US image data and fusion biopsy trajectory-proven pathology data. This deep learning AI model may more precisely predict the 3D mapping of CSCa in its volume and center location than a radiologist's reading based on PI-RADS version 2, and has potential in the planning of focal therapy.

Urine cell image recognition using a deep-learning model for an automated slide evaluation system.

OBJECTIVES: To develop a classification system for urine cytology with artificial intelligence (AI) using a convolutional neural network algorithm that classifies urine cell images as negative (benign) or positive (atypical or malignant). PATIENTS AND METHODS: We collected 195 urine cytology slides from consecutive patients with a histologically confirmed diagnosis of urothelial cancer (between January 2016 and December 2017). Two certified cytotechnologists independently evaluated and labelled each slide; 4637 cell images with concordant diagnoses were selected, including 3128 benign cells (negative), 398 atypical cells, and 1111 cells that were malignant or suspicious for malignancy (positive). This pathologically confirmed labelled dataset was used to represent the ground truth for AI training/validation/testing. Customized CutMix (CircleCut) and Refined Data Augmentation were used for image processing. The model architecture included EfficientNet B6 and Arcface. We used 80% of the data for training and validation (4:1 ratio) and 20% for testing. Model performance was evaluated with fivefold cross-validation. A receiver-operating characteristic (ROC) analysis was used to evaluate the binary classification model. Bayesian posterior probabilities for the AI performance measure (Y) and cytotechnologist performance measure (X) were compared. RESULTS: The area under the ROC curve was 0.99 (95% confidence interval [CI] 0.98-0.99), the highest accuracy was 95% (95% CI 94-97), sensitivity was 97% (95% CI 95-99), and specificity was 95% (95% CI 93-97). The accuracy of AI surpassed the highest level of cytotechnologists for the binary classification [Pr(Y > X) = 0.95]. AI achieved >90% accuracy for all cell subtypes. In the subgroup analysis based on the clinicopathological characteristics of patients who provided the test cells, the accuracy of AI ranged between 89% and 97%. CONCLUSION: Our novel AI classification system for urine cytology successfully classified all cell subtypes with an accuracy of higher than 90%, and achieved diagnostic accuracy of malignancy superior to the highest level achieved by cytotechnologists.

Microwave focal therapy of prostate cancer: a non-clinical study and exploratory clinical trial.

OBJECTIVE: To examine the safety and efficacy of microwave tissue coagulation (MTC) for prostate cancer and assess its use in lesion-targeted focal therapy in a non-clinical study and a clinical phase II trial. METHODS: In the non-clinical study using Microtaze® -AFM-712 (Alfresa Pharma Corporation, Osaka, Japan) with an MTC needle, MTC was performed using a transperineal approach to targeted canine prostatic tissue under real-time ultrasonography guidance. Using various MTC output and irradiation time combinations, the targeted and surrounding tissues (rectum, bladder and fat) were examined to confirm the extent of coagulative necrosis or potential cell death, and to compare intra-operative ultrasonography and pathology findings. The exploratory clinical trial was conducted to examine the safety and efficacy of MTC. Five selected patients underwent transperineal MTC to clinically single lesion magnetic resonance imaging (MRI)-visible lesions with Gleason score 3 + 4 or 4 + 4. Prostate-specific antigen (PSA), MRI and Expanded Prostate Cancer Index Composite questionnaire findings were compared before and 6 months after surgery. RESULTS: The region of coagulative necrosis was predictable by monitoring of ultrasonically visible vaporization; thus, by placing the MTC needle at a certain distance, we were able to perform a safe procedure without adverse events affecting the surrounding organs. Based on the non-clinical study, which used various combinations of output and irradiation time, MTC with 30-W output for 60-s irradiation was selected for the prostate. Based on the predictable necrosis, the therapeutic plan (where to place the MTC needle to achieve complete ablation of the target and how many sessions) was strictly determined per patient. There were no serious adverse events in any patient and only temporary urinary symptoms related to MTC therapy were observed. Furthermore, post-treatment satisfaction was very high. All preoperative MRI-visible lesions disappeared, and PSA decreased by 55% 6 months after surgery. CONCLUSION: Microwave tissue coagulation may be an option for lesion-targeted focal therapy for prostate cancer.

Systematic Biopsy of the Prostate can Be Omitted in Men with PI-RADS™ 5 and Prostate Specific Antigen Density Greater than 15.

PURPOSE: We evaluated the prostate cancer and clinically significant prostate cancer detection on systematic biopsy (SB), target biopsy (TB) alone and combined SB and TB in men with Prostate Imaging Reporting and Data System™ (PI-RADS™) 5 lesion. MATERIALS AND METHODS: From a prospectively maintained prostate biopsy database, we identified consecutive patients with PI-RADS 5 lesion on multiparametric magnetic resonance imaging. The patients underwent multiparametric magnetic resonance imaging followed by transrectal TB of PI-RADS 5 lesion and 12-core SB. The prostate cancer and clinically significant prostate cancer (Grade Group, GG ≥2) detection on SB, TB and SB+TB were determined for all men and accordingly to prostate specific antigen density. Statistic significant was set a p <0.05. RESULTS: Overall, 112 patients met inclusion criteria. The detection rate of prostate cancer for SB, TB and SB+TB was 89%, 93% and 95%, respectively, and for clinically significant prostate cancer it was 72%, 81% and 85%, respectively. SB added 2% prostate cancer and 4% clinically significant prostate cancer detection to TB. A total of 78 patients had prostate specific antigen density >0.15 ng/ml2, and the detection rate of PCa for SB, TB and SB+TB was 92%, 97% and 97%, respectively, and for clinically significant prostate cancer it was 79%, 91% and 95%, respectively. In this population, if SB was omitted, 0 prostate cancer and only 4% (3) of clinically significant prostate cancer would be missed. The clinically significant prostate cancer detection rate improved with increased prostate specific antigen density for SB (p=0.01), TB (p <0.0001) and combined SB+TB (p=0.002). CONCLUSIONS: In patients with PI-RADS 5 on multiparametric magnetic resonance imaging and prostate specific antigen density >0.15 ng/ml2, SB marginally increases clinically significant prostate cancer detection, but not overall prostate cancer detection in comparison to TB alone. Systematic biopsy did not affect patients' management and can be omitted on this population.

Moving away from systematic biopsies: image-guided prostate biopsy (in-bore biopsy, cognitive fusion biopsy, MRUS fusion biopsy) -literature review.

OBJECTIVE: To compare the detection rate of clinically significant cancer (CSCa) by magnetic resonance imaging-targeted biopsy (MRI-TB) with that by standard systematic biopsy (SB) and to evaluate the role of MRI-TB as a replacement from SB in men at clinical risk of prostate cancer. METHODS: The non-systematic literature was searched for peer-reviewed English-language articles using PubMed, including the prospective paired studies, where the index test was MRI-TB and the comparator text was SB. Also the randomized clinical trials (RCTs) are included if one arm was MRI-TB and another arm was SB. RESULTS: Eighteen prospective studies used both MRI-TB and TRUS-SB, and eight RCT received one of the tests for prostate cancer detection. In most prospective trials to compare MRI-TB vs. SB, there was no significant difference in any cancer detection rate; however, MRI-TB detected more men with CSCa and fewer men with CISCa than SB. CONCLUSION: MRI-TB is superior to SB in detection of CSCa. Since some significant cancer was detected by SB only, a combination of SB with the TB technique would avoid the underdiagnosis of CSCa.

Techniques and Outcomes of MRI-TRUS Fusion Prostate Biopsy.

PURPOSE OF REVIEW: The goal of this study is to review recent findings and evaluate the utility of MRI transrectal ultrasound fusion biopsy (FBx) techniques and discuss future directions. RECENT FINDINGS: FBx detects significantly higher rates of clinically significant prostate cancer (csPCa) than ultrasound-guided systematic prostate biopsy (SBx), particularly in repeat biopsy settings. FBx has also been shown to detect significantly lower rates of clinically insignificant prostate cancer. In addition, a dedicated prostate MRI can assist in more accurately predicting the Gleason score and provide further information regarding the index cancer location, prostate volume, and clinical stage. The ability to accurately evaluate specific lesions is vital to both focal therapy and active surveillance, for treatment selection, planning, and adequate follow-up. FBx has been demonstrated in multiple high-quality studies to have improved performance in diagnosis of csPCa compared to SBx. The combination of FBx with novel technologies including radiomics, prostate-specific membrane antigen positron emission tomography (PSMA PET), and high-resolution micro-ultrasound may have the potential to further enhance this performance.

High Intensity Focused Ultrasound Hemigland Ablation for Prostate Cancer: Initial Outcomes of a United States Series.

PURPOSE: We report outcomes of hemigland high intensity focused ultrasound ablation as primary treatment for localized prostate cancer in the United States. MATERIALS AND METHODS: A total of 100 consecutive men underwent hemigland high intensity focused ultrasound (December 2015 to December 2019). Primary end point was treatment failure, defined as Grade Group 2 or greater on followup prostate biopsy, radical treatment, systemic therapy, metastases or prostate cancer specific mortality. IIEF (International Index of Erectile Function), I-PSS (International Prostate Symptom Score) and 90-day complications were reported. RESULTS: At study entry patients had very low (8%), low (20%), intermediate favorable (50%), intermediate unfavorable (17%) and high (5%) risk prostate cancer. Median followup was 20 months. The 2-year survival free from treatment failure, Grade Group 2 or greater recurrence, repeat focal high intensity focused ultrasound and radical treatment was 73%, 76%, 90% and 91%, respectively. Bilateral prostate cancer at diagnosis was the sole predictor for Grade Group 2 or greater recurrence (p=0.03). Of men who underwent posttreatment biopsy (58), 10 had in-field and 8 out-of-field Grade Group 2 or greater positive biopsy. Continence (zero pad) was maintained in 100% of patients. Median IIEF-5 and I-PSS scores before vs after hemigland high intensity focused ultrasound were 22 vs 21 (p=0.99) and 9 vs 6 (p=0.005), respectively. Minor and major complications occurred in 13% and 0% of patients. No patient had rectal fistula or died. CONCLUSIONS: Short-term results of focal high intensity focused ultrasound indicate safety, excellent potency and continence preservation, and adequate short-term prostate cancer control. Radical treatment was avoided in 91% of men at 2 years. Men with bilateral prostate cancer at diagnosis have increased risk for Grade Group 2 or greater recurrence. To our knowledge, this is the initial and largest United States series of focal high intensity focused ultrasound as primary treatment for prostate cancer.

Magnetic resonance imaging/transrectal ultrasound fusion-targeted prostate biopsy using three-dimensional ultrasound-based organ-tracking technology: Initial experience in Japan.

OBJECTIVE: To evaluate the impact of magnetic resonance imaging/transrectal ultrasound fusion-targeted prostate biopsy on the diagnosis of clinically significant prostate cancer using real-time three-dimensional ultrasound-based organ-tracking technology. METHODS: The present study was a retrospective review of 262 consecutive patients with prostate-specific antigen of 7.1 ng/mL (interquartile range 4.0-19.8). All patients received pre-biopsy magnetic resonance imaging and had a suspicious lesion for clinically significant prostate cancer. All patients underwent a combination of systematic biopsy (6 cores) and three-dimensional ultrasound-based magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (2 cores). The positive rate of any cancer, positive rate of clinically significant prostate cancer, Gleason score and maximum cancer core length were compared between systematic biopsy versus magnetic resonance imaging/transrectal ultrasound fusion-targeted prostate biopsy. RESULTS: Overall, the positive rate of any cancer per patient was 61% (160/262) in systematic biopsy versus 79% (207/262) in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P < 0.0001); and that of clinically significant prostate cancer per patient was 46% (120/262) in systematic biopsy versus 70% (181/262) in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P < 0.0001). The positive rate of any cancer per core was 21.7% (330/1523) in systematic biopsy versus 68.6% (406/592) in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P < 0.0001), and that of clinically significant prostate cancer per core was 12.7% (193/1423) in systematic biopsy versus 60.3% (357/592) in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P < 0.0001). Adding systematic biopsy leads to 13 more cancer cases (5%). The distribution of Gleason score (6/7/8/9/10) was 59/71/23/6/1 in systematic biopsy versus 48/105/36/15/2 in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P = 0.005). The maximum cancer core length was 5 mm (0.5-16) in systematic biopsy versus 8 mm (1-19 mm) in magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy (P < 0.0001). CONCLUSIONS: Three-dimensional ultrasound-based magnetic resonance imaging/transrectal ultrasound fusion-targeted biopsy seems to be associated with a higher detection rate of clinically significant prostate cancer, with fewer cores than systematic random biopsy. However, significant cancer can still be detected by the systematic technique only. A combination of systematic biopsy with the targeted biopsy technique would avoid the underdiagnosis of clinically significant prostate cancer.

MALIGNANT MESOTHELIOMA OF THE TUNICA VAGINALIS TESTIS: A CASE REPORT.

We report here a case of malignant mesothelioma of the tunica vaginalis testis. A 93-year-old man with no history of asbestos exposure complained of increase of right scrotum size with pain. Ultrasonography and magnetic resonance imaging revealed a right hydrocele testis. A cytologic examination of the hydrocele fluid demonstrated mesothelial cells but show less atypicality and lack of obvious malignant features (class IIIa). We performed right hydrocelectomy for hydrocele testis. The pathological diagnosis was epithelial type of malignant mesothelioma of the tunica vaginalis testis, therefore we performed radical orchidectomy with wide excision of hemi-scrotal wall. There is no evidence of recurrence after 6 months of follow up. Malignant mesothelioma of the tunica vaginalis is rare, and accurate preoperative diagnosis is difficult. When a rapid increasing hemorrhagic hydrocele testis or nodular masses of the tunica vaginalis was observed, malignant mesothelioma should be considered. Malignant mesothelioma is highly fatal disease. Even two stage operation, radical orchidectomy should be performed.

[An Undeniable Case of Optic Neuropathy Due to Cabazitaxel].

Cabazitaxelis a taxane-type antineoplastic agent used for treating prostate cancer. Although typical side effects include neutropenia and fatigue, no studies have investigated eye disorders as a possible side effect, and the details are not clear. Herein, we report our experience of an undeniable case of optic neuropathy caused by cabazitaxel. A 78-year-old man had been diagnosed with prostate cancer (cT3aN1M1b, stage IV) 3 years previously, with a treatment history of bicalutamide, leuprorelin, flutamide, docetaxel, abiraterone, and enzalutamide. Because of a decline in vision during the second and third administration cycles of cabazitaxel, the patient visited an ophthalmologist. He was found to have reduced visual acuity, reduced central critical flicker frequency, narrowed field of vision, and impaired color vision, and was diagnosed with optic neuropathy. Although cabazitaxel administration was continued through 6 cycles, the symptoms were unchanged, and no drastic exacerbation was seen. This patient undeniably developed optic neuropathy due to cabazitaxel. Optic neuropathy due to taxane-type antineoplastic agents has also been reported with paclitaxel or docetaxel, and all precautions should be taken when administering such drugs. Detailed studies that include data from a larger number of facilities should be conducted in the future.

A Fully Automated Artificial Intelligence System to Assist Pathologists' Diagnosis to Predict Histologically High-grade Urothelial Carcinoma from Digitized Urine Cytology Slides Using Deep Learning.

BACKGROUND: Urine cytology, although a useful screening method for urothelial carcinoma, lacks sensitivity. As an emerging technology, artificial intelligence (AI) improved image analysis accuracy significantly. OBJECTIVE: To develop a fully automated AI system to assist pathologists in the histological prediction of high-grade urothelial carcinoma (HGUC) from digitized urine cytology slides. DESIGN, SETTING, AND PARTICIPANTS: We digitized 535 consecutive urine cytology slides for AI use. Among these slides, 181 were used for AI development, 39 were used as AI test data to identify HGUC by cell-level classification, and 315 were used as AI test data for slide-level classification. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Out of the 315 slides, 171 were collected immediately prior to bladder biopsy or transurethral resection of bladder tumor, and then outcomes were compared with the histological presence of HGUC in the surgical specimen. The primary aim was to compare AI prediction of the histological presence of HGUC with the pathologist's histological diagnosis of HGUC. Secondary aims were to compare the time required for AI evaluation and concordance between the AI's classification and pathologist's cytology diagnosis. RESULTS AND LIMITATIONS: The AI capability for predicting the histological presence of HGUC was 0.78 for the area under the curve. Comparing the AI predictive performance with pathologists' diagnosis, the AI sensitivity of 63% for histological HGUC prediction was superior to a pathologists' cytology sensitivity of 46% (p = 0.0037). On the contrary, there was no significant difference between the AI specificity of 83% and pathologists' specificity of 89% (p = 0.13), and AI accuracy of 74% and pathologists' accuracy of 68% (p = 0.08). The time required for AI evaluation was 139 s. With respect to the concordance between the AI prediction and pathologist's cytology diagnosis, the accuracy was 86%. Agreements with positive and negative findings were 92% and 84%, respectively. CONCLUSIONS: We developed a fully automated AI system to assist pathologists' histological diagnosis of HGUC using digitized slides. This AI system showed significantly higher sensitivity than a board-certified cytopathologist and may assist pathologists in making urine cytology diagnoses, reducing their workload. PATIENT SUMMARY: In this study, we present a deep learning-based artificial intelligence (AI) system that classifies urine cytology slides according to the Paris system. An automated AI system was developed and validated with 535 consecutive urine cytology slides. The AI predicted histological high-grade urothelial carcinoma from digitized urine cytology slides with superior sensitivity than pathologists, while maintaining comparable specificity and accuracy.

PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation.

Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.