The impact of the Ontario quality-based procedures funding model on radical prostatectomy outcomes.

INTRODUCTION: In 2015, radical prostatectomy (RP) in Ontario transitioned to the quality-based procedures (QBP) funding model, which assigns disbursement from surgical quality indicator (QI) outcome performance. The objective of this study was to assess the QBP QI outcomes before and after implementation of the QBP funding model for RP, and to determine whether changes seen were attributable to the QBP model. METHODS: We conducted a population-based, retrospective cohort study including all men who underwent RP for prostate cancer in Ontario from 2010-2019. We used administrative data from Ontario's health databases to gather surgical and QI outcome data. Our primary outcomes were the five measurable QBP QIs outlined by the province. We performed a pre- and post-intervention comparison, in addition to an interrupted-time series (ITS) analysis. RESULTS: Two of the five QIs improved after implementation of the QBP model (complication rate: 11.89% vs. 9.96%, p<0.001; proportion meeting length of stay target: 78.11% vs. 86.84%, p<0.001). ITS analysis revealed that there was no difference in trend in either outcome between pre- and post-implementation periods (p=0.913 and p=0.249, respectively). Two QIs were worse in the post-implementation period (unplanned visit rate: 23.45% vs. 25%, p=0.015; proportion meeting Wait 2 target: 94.39% vs. 92.88%, p<0.001). ITS revealed no significant trend changes post-implementation (p=0.260 and p=0.272, respectively). There was no difference in re-operation rate (2.84% vs. 2.45%, p=0.107). CONCLUSIONS: The QBP model for RP corresponds with mixed QI changes, but further analysis suggests that these changes were pre-existing trends and not attributable to the model.

Hospital volume-outcome relationships for robot-assisted surgeries: a population-based analysis.

BACKGROUND: Associations between procedure volumes and outcomes can inform minimum volume standards and the regionalization of health services. Robot-assisted surgery continues to expand globally; however, data are limited regarding which hospitals should be using the technology. STUDY DESIGN: Using administrative health data for all residents of Ontario, Canada, this retrospective cohort study included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using 4 arms (RPL-4) between January 2010 and September 2021. Associations between yearly hospital volumes and 90-day major complications were evaluated using multivariable logistic regression models adjusted for patient characteristics and clustering at the level of the hospital. RESULTS: A total of 10,879 patients were included, with 7567, 1776, 724, and 812 undergoing a RARP, TRH, RAPN, and RPL-4, respectively. Yearly hospital volume was not associated with 90-day complications for any procedure. Doubling of yearly volume was associated with a 17-min decrease in operative time for RARP (95% confidence interval [CI] - 23 to - 10), 8-min decrease for RAPN (95% CI - 14 to - 2), 24-min decrease for RPL-4 (95% CI - 29 to - 19), and no significant change for TRH (- 7 min; 95% CI - 17 to 3). CONCLUSION: The risk of 90-day major complications does not appear to be higher in low volume hospitals; however, they may not be as efficient with operating room utilization. Careful case selection may have contributed to the lack of an observed association between volumes and complications.

Hospital learning curves for robot-assisted surgeries: a population-based analysis.

BACKGROUND: Robot-assisted surgery has been rapidly adopted. It is important to define the learning curve to inform credentialling requirements, training programs, identify fast and slow learners, and protect patients. This study aimed to characterize the hospital learning curve for common robot-assisted procedures. STUDY DESIGN: This cohort study, using administrative health data for Ontario, Canada, included adult patients who underwent a robot-assisted radical prostatectomy (RARP), total robotic hysterectomy (TRH), robot-assisted partial nephrectomy (RAPN), or robotic portal lobectomy using four arms (RPL-4) between 2010 and 2021. The association between cumulative hospital volume of a robot-assisted procedure and major complications was evaluated using multivariable logistic models adjusted for patient characteristics and clustering at the hospital level. RESULTS: A total of 6814 patients were included, with 5230, 543, 465, and 576 patients in the RARP, TRH, RAPN, and RPL-4 cohorts, respectively. There was no association between cumulative hospital volume and major complications. Visual inspection of learning curves demonstrated a transient worsening of outcomes followed by subsequent improvements with experience. Operative time decreased for all procedures with increasing volume and reached plateaus after approximately 300 RARPs, 75 TRHs, and 150 RPL-4s. The odds of a prolonged length of stay decreased with increasing volume for patients undergoing a RARP (OR 0.87; 95% CI 0.82-0.92) or RPL-4 (OR 0.77; 95% CI 0.68-0.87). CONCLUSION: Hospitals may adopt robot-assisted surgery without significantly increasing the risk of major complications for patients early in the learning curve and with an expectation of increasing efficiency.

The evolving use of robotic surgery: a population-based analysis.

INTRODUCTION: Robotic surgery has integrated into the healthcare system despite limited evidence demonstrating its clinical benefit. Our objectives were (i) to describe secular trends and (ii) patient- and system-level determinants of the receipt of robotic as compared to open or laparoscopic surgery. METHODS: This population-based retrospective cohort study included adult patients who, between 2009 and 2018 in Ontario, Canada, underwent one of four commonly performed robotic procedures: radical prostatectomy, total hysterectomy, thoracic lobectomy, partial nephrectomy. Patients were categorized based on the surgical approach as robotic, open, or laparoscopic for each procedure. Multivariable regression models were used to estimate the temporal trend in robotic surgery use and associations of patient and system characteristics with the surgical approach. RESULTS: The cohort included 24,741 radical prostatectomy, 75,473 total hysterectomy, 18,252 thoracic lobectomy, and 4608 partial nephrectomy patients, of which 6.21% were robotic. After adjusting for patient and system characteristics, the rate of robotic surgery increased by 24% annually (RR 1.24, 95%CI 1.13-1.35): 13% (RR 1.13, 95%CI 1.11-1.16) for robotic radical prostatectomy, 9% (RR 1.09, 95%CI 1.05-1.13) for robotic total hysterectomy, 26% (RR 1.26, 95%CI 1.06-1.50) for thoracic lobectomy and 26% (RR 1.26, 95%CI 1.13-1.40) for partial nephrectomy. Lower comorbidity burden, earlier disease stage (among cancer cases), and early career surgeons with high case volume at a teaching hospital were consistently associated with the receipt of robotic surgery. CONCLUSION: The use of robotic surgery has increased. The study of the real-world clinical outcomes and associated costs is needed before further expanding use among additional providers and hospitals.

Prostate cancer classification using radiomics and machine learning on mp-MRI validated using co-registered histology.

BACKGROUND: Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for prostate cancer (PCa) detection but currently has unaddressed limitations. Computer aided diagnosis (CAD) systems have been developed to address these needs, but many approaches used to generate and validate the models have inherent biases. METHOD: All clinically significant PCa on histology was mapped to mp-MRI using a previously validated registration algorithm. Shape and size matched non-PCa regions were selected using a proposed sampling algorithm to eliminate biases towards shape and size. Further analysis was performed to assess biases regarding inter-zonal variability. RESULTS: A 5-feature Naïve-Bayes classifier produced an area under the receiver operating characteristic curve (AUC) of 0.80 validated using leave-one-patient-out cross-validation. As mean inter-class area mismatch increased, median AUC trended towards positively biasing classifiers to producing higher AUCs. Classifiers were invariant to differences in shape between PCa and non-PCa lesions (AUC: 0.82 vs 0.82). Performance for models trained and tested only in the peripheral zone was found to be lower than in the central gland (AUC: 0.75 vs 0.95). CONCLUSION: We developed a radiomics based machine learning system to classify PCa vs non-PCa tissue on mp-MRI validated on accurately co-registered mid-gland histology with a measured target registration error. Potential biases involved in model development were interrogated to provide considerations for future work in this area.

Adverse events following robotic surgery: population-based analysis.

BACKGROUND: Robotic surgery was integrated into some healthcare systems despite there being few well designed, real-world studies on safety or benefit. This study compared the safety of robotic with laparoscopic, thoracoscopic, and open approaches in common robotic procedures. METHODS: This was a population-based, retrospective study of all adults who underwent prostatectomy, hysterectomy, pulmonary lobectomy, or partial nephrectomy in Ontario, Canada, between 2008 and 2018. The primary outcome was 90-day total adverse events using propensity score overlap weights, and secondary outcomes were minor or major morbidity/adverse events. RESULTS: Data on 24 741 prostatectomy, 75 473 hysterectomy, 18 252 pulmonary lobectomy, and 6608 partial nephrectomy operations were included. Relative risks for total adverse events in robotic compared with open surgery were 0.80 (95 per cent c.i. 0.74 to 0.87) for radical prostatectomy, 0.44 (0.37 to 0.52) for hysterectomy, 0.53 (0.44 to 0.65) for pulmonary lobectomy, and 0.72 (0.54 to 0.97) for partial nephrectomy. Relative risks for total adverse events in robotic surgery compared with a laparoscopic/thoracoscopic approach were 0.94 (0.77 to 1.15), 1.00 (0.82 to 1.23), 1.01 (0.84 to 1.21), and 1.23 (0.82 to 1.84) respectively. CONCLUSION: The robotic approach is associated with fewer adverse events than an open approach but similar to a laparoscopic/thoracoscopic approach. The benefit of the robotic approach is related to the minimally-invasive approach rather than the platform itself.

Targeting prostate lesions on multiparametric MRI with HDR brachytherapy: Optimal planning margins determined using whole-mount digital histology.

PURPOSE: Multiparametric magnetic resonance imaging (mpMRI) has demonstrated the ability to localize intraprostatic lesions. It is our goal to determine how to optimally target the underlying histopathological cancer within the setting of high-dose-rate brachytherapy (HDR-BT). METHODS AND MATERIALS: Ten prostatectomy patients had pathologist-annotated mid-gland histology registered to pre-procedural mpMRI, which were interpreted by four different observers. Simulated HDR-BT plans with realistic catheter placements were generated by registering the mpMRI lesions and corresponding histology annotations to previously performed clinical HDR-BT implants. Inverse treatment planning was used to generate treatment plans that treated the entire gland to a single dose of 15 Gy, as well as focally targeted plans that aimed to escalate dose to the mpMRI lesions to 20.25 Gy. Three margins to the lesion were explored: 0 mm, 1 mm, and 2 mm. The analysis compared the dose that would have been delivered to the corresponding histologically-defined cancer with the different treatment planning techniques. RESULTS: mpMRI-targeted plans delivered a significantly higher dose to the histologically-defined cancer (p < 0.001), in comparison to the standard treatment plans. Additionally, adding a 1 mm margin resulted in significantly higher D98, and D90 to the histologically-defined cancer in comparison to the 0 mm margin targeted plans (p = 0.019 & p = 0.0026). There was no significant difference between plans using 1 mm and 2 mm margins. CONCLUSIONS: Adding a 1 mm margin to intraprostatic mpMRI lesions significantly increased the dose to histologically-defined cancer, in comparison applying no margin. No significant effect was observed by further expanding the margins.

A Robust Edge-Preserving Stereo Matching Method for Laparoscopic Images.

Stereo matching has become an active area of research in the field of computer vision. In minimally invasive surgery, stereo matching provides depth information to surgeons, with the potential to increase the safety of surgical procedures, particularly those performed laparoscopically. Many stereo matching methods have been reported to perform well for natural images, but for images acquired during a laparoscopic procedure, they are limited by image characteristics including illumination differences, weak texture content, specular highlights, and occlusions. To overcome these limitations, we propose a robust edge-preserving stereo matching method for laparoscopic images, comprising an efficient sparse-dense feature matching step, left and right image illumination equalization, and refined disparity optimization. We validated the proposed method using both benchmark biological phantoms and surgical stereoscopic data. Experimental results illustrated that, in the presence of heavy illumination differences between image pairs, texture and textureless surfaces, specular highlights and occlusions, our proposed approach consistently obtains a more accurate estimate of the disparity map than state-of-the-art stereo matching methods in terms of robustness and boundary preservation.

Simple prostatectomy using the open and robotic approaches for lower urinary tract symptoms: A retrospective, case-control series.

INTRODUCTION: We aimed to assess the outcome of our series of simple prostatectomy at our institution using the open simple prostatectomy (OSP) and robotic-assisted simple prostatectomy (RASP) approaches. METHODS: We conducted a retrospective chart review of men who underwent OSP and RASP at Western University, in London, ON. Preoperative, intraoperative, and postoperative data were collected and analyzed. RESULTS: From 2012-2020, 29 men underwent a simple prostatectomy at our institution. Eight patients underwent an OSP and 21 patients underwent a RASP. The median age was 69 years. Preoperative median prostate volume was 153 cm³ (range 80-432). The surgical indications were failed medical treatment, urinary retention, hydronephrosis, cystolithiasis, and recurrent hematuria. The median operative time was 137.5 minutes in OSP and 185 minutes in RASP (p=0.04). Median estimated blood loss was 2300 ml (range 600-4000) and 100 ml (range 50-400) in the open and robotic procedures, respectively (p=0.4). The mean length of hospital stay was shorter in the RASP group, one day vs. three days (z=4.152, p&lt;0.005). Perioperative complication rates were significantly lower in the group undergoing RASP, with no complications recorded in this group (p=0.004). Both groups demonstrated excellent functional results, with most patients reporting complete urinary continence (p=0.8). CONCLUSIONS: We report very good perioperative outcomes, with a minimal risk profile and excellent functional results, leading to marked improvement in patients' symptoms at followup after both the OSP and RASP approaches. RASP was associated with a shorter length of hospital stay, decreased blood loss, and a lower complication rate.

Short-duration dynamic [18F]DCFPyL PET and CT perfusion imaging to localize dominant intraprostatic lesions in prostate cancer: validation against digital histopathology and comparison to [18F]DCFPyL PET/MR at 120 minutes.

PURPOSE: Localized prostate cancer (PCa) in patients is characterized by a dominant focus in the gland (dominant intraprostatic lesion, DIL). Accurate DIL identification may enable more accurate diagnosis and therapy through more precise targeting of biopsy, radiotherapy and focal ablative therapies. The goal of this study is to validate the performance of [18F]DCFPyL PET and CT perfusion (CTP) for detecting and localizing DIL against digital histopathological images. METHODS: Multi-modality image sets: in vivo T2-weighted (T2w)-MRI, 22-min dynamic [18F]DCFPyL PET/CT, CTP, and 2-h post-injection PET/MR were acquired in patients prior to radical prostatectomy. The explanted gland with implanted fiducial markers was imaged with T2w-MRI. All images were co-registered to the pathologist-annotated digital images of whole-mount mid-gland histology sections using fiducial markers and anatomical landmarks. Regions of interest encompassing DIL and non-DIL tissue were drawn on the digital histopathological images and superimposed on PET and CTP parametric maps. Logistic regression with backward elimination of parameters was used to select the most sensitive parameter set to distinguish DIL from non-DIL voxels. Leave-one-patient-out cross-validation was performed to determine diagnostic performance. RESULTS: [18F]DCFPyL PET and CTP parametric maps of 15 patients were analyzed. SUVLate and a model combining Ki and k4 of [18F]DCFPyL achieved the most accurate performance distinguishing DIL from non-DIL voxels. Both detection models achieved an AUC of 0.90 and an error rate of < 10%. Compared to digital histopathology, the detected DILs had a mean dice similarity coefficient of 0.8 for the Ki and k4 model and 0.7 for SUVLate. CONCLUSIONS: We have validated using co-registered digital histopathological images that parameters from kinetic analysis of 22-min dynamic [18F]DCFPyL PET can accurately localize DILs in PCa for targeting of biopsy, radiotherapy, and focal ablative therapies. Short-duration dynamic [18F]DCFPyL PET was not inferior to SUVLate in this diagnostic task. CLINICAL TRIAL REGISTRATION NUMBER: NCT04009174 (ClinicalTrials.gov).

Prostate specific membrane antigen positron emission tomography for lesion-directed high-dose-rate brachytherapy dose escalation.

BACKGROUND AND PURPOSE: Prostate specific membrane antigen positron emission tomography imaging (PSMA-PET) has demonstrated potential for intra-prostatic lesion localization. We leveraged our existing database of co-registered PSMA-PET imaging with cross sectional digitized pathology to model dose coverage of histologically-defined prostate cancer when tailoring brachytherapy dose escalation based on PSMA-PET imaging. MATERIALS AND METHODS: Using a previously-developed automated approach, we created segmentation volumes delineating underlying dominant intraprostatic lesions for ten men with co-registered pathology-imaging datasets. To simulate realistic high-dose-rate brachytherapy (HDR-BT) treatments, we registered the PSMA-PET-defined segmentation volumes and underlying cancer to 3D trans-rectal ultrasound images of HDR-BT cases where 15 Gray (Gy) was delivered. We applied dose/volume optimization to focally target the dominant intraprostatic lesion identified on PSMA-PET. We then compared histopathology dose for all high-grade cancer within whole-gland treatment plans versus PSMA-PET-targeted plans. Histopathology dose was analyzed for all clinically significant cancer with a Gleason score of 7or greater. RESULTS: The standard whole-gland plans achieved a median [interquartile range] D98 of 15.2 [13.8-16.4] Gy to the histologically-defined cancer, while the targeted plans achieved a significantly higher D98 of 16.5 [15.0-19.0] Gy (p = 0.007). CONCLUSION: This study is the first to use digital histology to confirm the effectiveness of PSMA-PET HDR-BT dose escalation using automatically generated contours. Based on the findings of this study, PSMA-PET lesion dose escalation can lead to increased dose to the ground truth histologically defined cancer.

Potential Pitfalls in Pre-implantation Genetic Diagnosis in a Patient with Tuberous Sclerosis and Isolated Mosaicism for a TSC2 Variant in Renal Tissue.

Tuberous sclerosis complex (TSC) is an autosomal dominant disorder that displays a wide spectrum of clinical manifestations, often affecting multiple organs including the kidneys, brain, lungs, and skin. A pathogenic mutation in either the TSC1 or TSC2 gene can be detected in almost 85% of the cases, with mosaicism accounting for about half of the remaining cases. We report a case of TSC diagnosed clinically, requesting genetic counselling regarding reproductive risks. No mutation was identified on initial testing of peripheral blood; however, mosaicism for a likely pathogenic frameshift variant in TSC2 was detected at a level of 15% in renal angiomyolipoma tissue. Despite widespread clinical manifestations of TCS, this variant was not detected in skin fibroblasts or saliva, raising the possibility this is an isolated somatic mutation in renal tissue with the underlying germline mutation not yet identified. This case highlights the difficulties when counselling patients with mosaicism regarding their reproductive risks and prenatal diagnostic options.

A multiobserver study investigating the effectiveness of prostatic multiparametric magnetic resonance imaging to dose escalate corresponding histologic lesions using high-dose-rate brachytherapy.

PURPOSE: Using multiparametric MRI data and the pathologic data from radical prostatectomy specimens, we simulated the treatment planning of dose-escalated high-dose-rate brachytherapy (HDR-BT) to the Multiparametric MRI dominant intraprostatic lesion (mpMRI-DIL) to compare the dose potentially delivered to the pathologically confirmed locations of the high-grade component of the cancer. METHODS AND MATERIALS: Pathologist-annotated prostatectomy midgland histology sections from 12 patients were registered to preprostatectomy mpMRI scans that were interpreted by four radiologists. To simulate realistic HDR-BT, we registered each observer's mpMRI-DILs and corresponding histology to two transrectal ultrasound images of other HDR-BT patients with a 15-Gy whole-gland prescription. We used clinical inverse planning to escalate the mpMRI-DILs to 20.25 Gy. We compared the dose that the histopathology would have received if treated with standard treatment plans to the dose mpMRI-targeting would have achieved. The histopathology was grouped as high-grade cancer (any Gleason Grade 4 or 5) and low-grade cancer (only Gleason Grade 3). RESULTS: 212 mpMRI-targeted HDR-BT plans were analyzed. For high-grade histology, the mpMRI-targeted plans achieved significantly higher median [IQR] D98 and D90 values of 18.2 [16.7-19.5] Gy and 19.4 [17.8-20.9] Gy, respectively, in comparison with the standard plans (p = 0.01 and p = 0.003). For low-grade histology, the targeted treatment plans would have resulted in a significantly higher median D90 of 17.0 [16.1-18.4] Gy in comparison with standard plans (p = 0.015); the median D98 was not significantly higher (p = 0.2). CONCLUSIONS: In this retrospective pilot study of 12 patients, mpMRI-based dose escalation led to increased dose to high-grade, but not low-grade, cancer. In our data set, different observers and mpMRI sequences had no substantial effect on dose to histologic cancer.

Kinetic analysis of dominant intraprostatic lesion of prostate cancer using quantitative dynamic [18F]DCFPyL-PET: comparison to [18F]fluorocholine-PET.

PURPOSE: Identification of the dominant intraprostatic lesion(s) (DILs) can facilitate diagnosis and treatment by targeting biologically significant intra-prostatic foci. A PSMA ligand, [18F]DCFPyL (2-(3-{1-carboxy-5-[(6-[18F]fluoro-pyridine-3-carbonyl)-amino]-pentyl}-ureido)-pentanedioic acid), is better than choline-based [18F]FCH (fluorocholine) in detecting and localizing DIL because of higher tumour contrast, particularly when imaging is delayed to 1 h post-injection. The goal of this study was to investigate whether the different imaging performance of [18F]FCH and [18F]DCFPyL can be explained by their kinetic behaviour in prostate cancer (PCa) and to evaluate whether DIL can be accurately detected and localized using a short duration dynamic positron emission tomography (PET). METHODS: 19 and 23 PCa patients were evaluated with dynamic [18F]DCFPyL and [18F]FCH PET, respectively. The dynamic imaging protocol with each tracer had a total imaging time of 22 min and consisted of multiple frames with acquisition times from 10 to 180 s. Tumour and benign tissue regions identified by sextant biopsy were compared using standardized uptake value (SUV) and tracer kinetic parameters from kinetic analysis of time-activity curves. RESULTS: For [18F]DCFPyL, logistic regression identified Ki and k4 as the optimal model to discriminate tumour from benign tissue (84.2% sensitivity and 94.7% specificity), while only SUV was predictive for [18F]FCH (82.6% sensitivity and 87.0% specificity). The higher k3 (binding) of [18F]FCH than [18F]DCFPyL explains why [18F]FCH SUV can differentiate tumour from benign tissue within minutes of injection. Superior [18F]DCFPyL tumour contrast was due to the higher k4/k3 (more rapid washout) in benign tissue compared to tumour tissue. CONCLUSIONS: DIL was detected with good sensitivity and specificity using 22-min dynamic [18F]DCFPyL PET and avoids the need for delayed post-injection imaging timepoints. The dissimilar in vivo kinetic behaviour of [18F]DCFPyL and [18F]FCH could explain their different SUV images. Clinical Trial Registration NCT04009174 (ClinicalTrials.gov).

The value of 99mTc-sestamibi single-photon emission computed tomography-computed tomography in the evaluation and risk stratification of renal masses.

INTRODUCTION: Differentiation of renal cell carcinoma (RCC) from oncocytoma is a common diagnostic dilemma. A few studies have shown that 99mTc-sestamibi (MIBI) imaging has the potential to characterize indeterminate renal masses. This comparative study evaluated the utility of MIBI single-photon emission computed tomography-computed tomography (SPECT-CT) in the assessment and risk stratification of renal masses. METHODS: A total of 29 patients with 31 renal masses who had cross-sectional imaging and MIBI SPECT-CT were included. Lesions were categorized as either MIBI-positive or -negative on SPECT-CT. Individual lesion density ranged from 22-56 Hounsfield units (HU) on the non-contrast CT part of SPECT-CT. Quantitative relative MIBI uptake was calculated by measuring tumor to ipsilateral renal parenchymal uptake. The imaging results were correlated with histopathology. RESULTS: All oncocytic lesions, including seven oncocytomas and one hybrid oncocytic chromophobe tumor (100%), were positive on MIBI. One chromophobe RCC showed low-grade MIBI uptake. The remaining RCC subtypes, including 15 clear-cell, four papillary, two mixed clear-cell and papillary, and one chromophobe, were MIBI-negative. The quantitative relative tumor uptake showed statistically significant higher uptake in the low-risk/oncocytic lesions compared to RCCs. CONCLUSIONS: This study demonstrates that MIBI SPECT-CT is valuable in the characterization of indeterminate renal masses. The combination of MIBI uptake on SPECT and lesion density on non-contrast CT can be used for risk stratification of renal masses. This technique may reduce the need for further imaging (multiphasic CT or magnetic resonance imaging), renal mass biopsy, or surgical resection of low-risk renal masses. Subsequently, more patients could be followed with active surveillance.

Histologic validation of auto-contoured dominant intraprostatic lesions on [18F] DCFPyL PSMA-PET imaging.

BACKGROUND: PSMA-PET1 has shown good concordance with histology, but there is a need to investigate the ability of PSMA-PET to delineate DIL2 boundaries for guided biopsy and focal therapy planning. OBJECTIVE: To determine threshold and margin combinations that satisfy the following criteria: ≥95% sensitivity with max specificity and ≥95% specificity with max sensitivity. DESIGN, SETTING AND PARTICIPANTS: We registered pathologist-annotated whole-mount mid-gland prostatectomy histology sections cut in 4.4 mm intervals from 12 patients to pre-surgical PSMA-PET/MRI by mapping histology to ex-vivo imaging to in-vivo imaging. We generated PET-derived tumor volumes using boundaries defined by thresholded PET volumes from 1-100% of SUV3max in 1% intervals. At each interval, we applied margins of 0-30 voxels in one voxel increments, giving 3000 volumes/patient. OUTCOME MEASUREMENTS: Mean and standard deviation of sensitivity and specificity for cancer detection within the 2D oblique histologic planes that intersected with the 3D PET volume for each patient. RESULTS AND LIMITATIONS: A threshold of 67% SUV max with an 8.4 mm margin achieved a (mean ± std.) sensitivity of 95.0 ± 7.8% and specificity of 76.4 ± 14.7%. A threshold of 81% SUV max with a 5.1 mm margin achieved sensitivity of 65.1 ± 28.4% and specificity of 95.1 ± 5.2%. CONCLUSIONS: Preliminary evidence of thresholding and margin expansion of PSMA-PET images targeted at DILs validated with histopathology demonstrated excellent mean sensitivity and specificity in the setting of focal therapy/boosting and guided biopsy. These parameters can be used in a larger validation study supporting clinical translation.

Small Renal Mass Surveillance: Histology-specific Growth Rates in a Biopsy-characterized Cohort.

BACKGROUND: Most reports of active surveillance (AS) of small renal masses (SRMs) lack biopsy confirmation, and therefore include benign tumors and different subtypes of renal cell carcinoma (RCC). OBJECTIVE: We compared the growth rates and progression of different histologic subtypes of RCC SRMs (SRMRCC) in the largest cohort of patients with biopsy-characterized SRMs on AS. DESIGN, SETTING, AND PARTICIPANTS: Data from patients in a multicenter Canadian trial and a Princess Margaret cohort were combined to include 136 biopsy-proven SRMRCC lesions managed by AS, with treatment deferred until progression or patient/surgeon decision. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Growth curves were estimated from serial tumor size measures. Tumor progression was defined by sustained size ≥4 cm or volume doubling within 1 yr. RESULTS AND LIMITATIONS: Median follow-up for patients who remained on AS was 5.8 yr (interquartile range 3.4-7.5 yr). Clear cell RCC SRMs (SRMccRCC) grew faster than papillary type 1 SRMs (0.25 and 0.02 cm/yr on average, respectively, p = 0.0003). Overall, 60 SRMRCC lesions progressed: 49 (82%) by rapid growth (volume doubling), seven (12%) increasing to ≥4 cm, and four (6.7%) by both criteria. Six patients developed metastases, and all were of clear cell RCC histology. Limitations include the use of different imaging modalities and a lack of central imaging review. CONCLUSIONS: Tumor growth varies between histologic subtypes of SRMRCC and among SRMccRCC, which likely reflects individual host and tumor biology. Without validated biomarkers that predict this variation, initial follow-up of histologically characterized SRMs can inform personalized treatment for patients on AS. PATIENT SUMMARY: Many small kidney cancers are suitable for surveillance and can be monitored over time for change. We demonstrate that different types of kidney cancers grow at different rates and are at different risks of progression. These results may guide better personalized treatment.

Automatic cancer detection on digital histopathology images of mid-gland radical prostatectomy specimens.

Purpose: Automatic cancer detection on radical prostatectomy (RP) sections facilitates graphical and quantitative surgical pathology reporting, which can potentially benefit postsurgery follow-up care and treatment planning. It can also support imaging validation studies using a histologic reference standard and pathology research studies. This problem is challenging due to the large sizes of digital histopathology whole-mount whole-slide images (WSIs) of RP sections and staining variability across different WSIs. Approach: We proposed a calibration-free adaptive thresholding algorithm, which compensates for staining variability and yields consistent tissue component maps (TCMs) of the nuclei, lumina, and other tissues. We used and compared three machine learning methods for classifying each cancer versus noncancer region of interest (ROI) throughout each WSI: (1) conventional machine learning methods and 14 texture features extracted from TCMs, (2) transfer learning with pretrained AlexNet fine-tuned by TCM ROIs, and (3) transfer learning with pretrained AlexNet fine-tuned with raw image ROIs. Results: The three methods yielded areas under the receiver operating characteristic curve of 0.96, 0.98, and 0.98, respectively, in leave-one-patient-out cross validation using 1.3 million ROIs from 286 mid-gland whole-mount WSIs from 68 patients. Conclusion: Transfer learning with the use of TCMs demonstrated state-of-the-art overall performance and is more stable with respect to sample size across different tissue types. For the tissue types involving Gleason 5 (most aggressive) cancer, it achieved the best performance compared to the other tested methods. This tool can be translated to clinical workflow to assist graphical and quantitative pathology reporting for surgical specimens upon further multicenter validation.

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.

Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning after RP. Promising results for detecting and grading prostate cancer on digital histopathology images have been reported using machine learning techniques. However, the importance and applicability of those methods have not been fully investigated. We computed three-class tissue component maps (TCMs) from the images, where each pixel was labeled as nuclei, lumina, or other. We applied seven different machine learning approaches: three non-deep learning classifiers with features extracted from TCMs, and four deep learning, using transfer learning with the 1) TCMs, 2) nuclei maps, 3) lumina maps, and 4) raw images for cancer detection and grading on whole-mount RP tissue sections. We performed leave-one-patient-out cross-validation against expert annotations using 286 whole-slide images from 68 patients. For both cancer detection and grading, transfer learning using TCMs performed best. Transfer learning using nuclei maps yielded slightly inferior overall performance, but the best performance for classifying higher-grade cancer. This suggests that 3-class TCMs provide the major cues for cancer detection and grading primarily using nucleus features, which are the most important information for identifying higher-grade cancer.