Automated Detection and Grading of Extraprostatic Extension of Prostate Cancer at MRI via Cascaded Deep Learning and Random Forest Classification

RATIONALE AND OBJECTIVES

Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.

MATERIAL AND METHODS

An expert genitourinary radiologist conducted prospective clinical assessments of MRI scans for 634 patients and assigned risk for EPE using a grading technique. The training set and held-out independent test set consisted of 507 patients and 127 patients, respectively. Existing deep-learning AI models for prostate organ and lesion segmentation were leveraged to extract area and distance features for random forest classification models. Model performance was evaluated using balanced accuracy, ROC AUCs for each EPE grade, as well as sensitivity, specificity, and accuracy compared to EPE on histopathology.

RESULTS

A balanced accuracy score of .390 ± 0.078 was achieved using a lesion detection probability threshold of 0.45 and distance features. Using the test set, ROC AUCs for AI-assigned EPE grades 0-3 were 0.70, 0.65, 0.68, and 0.55 respectively. When using EPE≥ 1 as the threshold for positive EPE, the model achieved a sensitivity of 0.67, specificity of 0.73, and accuracy of 0.72 compared to radiologist sensitivity of 0.81, specificity of 0.62, and accuracy of 0.66 using histopathology as the ground truth.

CONCLUSION

Our AI workflow for assigning imaging-based EPE grades achieves an accuracy for predicting histologic EPE approaching that of physicians. This automated workflow has the potential to enhance physician decision-making for assessing the risk of EPE in patients undergoing treatment for prostate cancer due to its consistency and automation.

Reducing False-Positives Due to Urinary Stagnation in the Prostatic Urethra on 18F-DCFPyL PSMA PET/CT With MRI

PURPOSE

Prostate-specific membrane antigen (PSMA)-targeting PET radiotracers reveal physiologic uptake in the urinary system, potentially misrepresenting activity in the prostatic urethra as an intraprostatic lesion. This study examined the correlation between midline 18F-DCFPyL activity in the prostate and hyperintensity on T2-weighted (T2W) MRI as an indication of retained urine in the prostatic urethra.

PATIENTS AND METHODS

Eighty-five patients who underwent both 18F-DCFPyL PSMA PET/CT and prostate MRI between July 2017 and September 2023 were retrospectively analyzed for midline radiotracer activity and retained urine on postvoid T2W MRIs. Fisher's exact tests and unpaired t tests were used to compare residual urine presence and prostatic urethra measurements between patients with and without midline radiotracer activity. The influence of anatomical factors including prostate volume and urethral curvature on urinary stagnation was also explored.

RESULTS

Midline activity on PSMA PET imaging was seen in 14 patients included in the case group, whereas the remaining 71 with no midline activity constituted the control group. A total of 71.4% (10/14) and 29.6% (21/71) of patients in the case and control groups had urethral hyperintensity on T2W MRI, respectively (P < 0.01). Patients in the case group had significantly larger mean urethral dimensions, larger prostate volumes, and higher incidence of severe urethral curvature compared with the controls.

CONCLUSIONS

Stagnated urine within the prostatic urethra is a potential confounding factor on PSMA PET scans. Integrating PET imaging with T2W MRI can mitigate false-positive calls, especially as PSMA PET/CT continues to gain traction in diagnosing localized prostate cancer.

Timing, incidence and management of delayed bleeding after partial nephrectomy in patients at risk for recurrent, bilateral, multifocal renal tumors

INTRODUCTION

Delayed bleeding is a potentially serious complication after partial nephrectomy (PN), with reported rates of 1%-2%. Patients with multiple renal tumors, including those with hereditary forms of kidney cancer, are often managed with resection of multiple tumors in a single kidney which may increase the risk of delayed bleeding, though outcomes have not previously been reported specifically in this population. The objective of this study was to evaluate the incidence and timing of delayed bleeding as well as the impact of intervention on renal functional outcomes in a cohort primarily made up of patients at risk for bilateral, multifocal renal tumors.

METHODS

A retrospective review of a prospectively maintained database of patients with known or suspected predisposition to bilateral, multifocal renal tumors who underwent PN from 2003 to 2023 was conducted. Patients who presented with delayed bleeding were identified. Patients with delayed bleeding were compared to those without. Comparative statistics and univariate logistic regression were used to determine potential risk factors for delayed bleeding.

RESULTS

A total of 1256 PN were performed during the study period. Angiographic evidence of pseudoaneurysm, AV fistula and/or extravasation occurred in 24 cases (1.9%). Of these, 21 were symptomatic presenting with gross hematuria in 13 (54.2%), decreasing hemoglobin in 4(16.7%), flank pain in 2(8.3%), and mental status change in 2 (8.3%), while 3 patients were asymptomatic. Median number of resected tumors was 5 (IQR 2-8). All patients underwent angiogram with super-selective embolization. Median time to bleed event was 13.5 days (IQR 7-22). Factors associated with delayed bleeding included open approach (OR 2.2, IQR(1.06-5.46), P = 0.04 and left-sided surgery (OR 4.93, IQR(1.67-14.5), P = 0.004. Selective embolization had little impact on ultimate renal functional outcomes, with a median change of 11% from the baseline eGFR after partial nephrectomy and embolization. One patient required total nephrectomy for refractory bleeding after embolization.

CONCLUSIONS

Delayed bleeding after PN in a cohort of patients with multifocal tumors is an infrequent event, with similar rates to single tumor series. Patients should be counseled regarding timing and symptoms of delayed bleeding and multidisciplinary management with interventional radiology is critical for timely diagnosis and treatment.

Image-guided transbronchial pulmonary cryoablation with a flexible cryoprobe in swine: performance and radiology-pathology correlation

PURPOSE

To evaluate the performance of a prototype flexible transbronchial cryoprobe compared to percutaneous transthoracic cryoablation and to define cone-beam CT (CBCT) imaging and pathology cryolesion features in an in vivo swine model.

METHODS

Transbronchial cryoablation was performed with a prototype flexible cryoprobe (3 central and 3 peripheral lung ablations in 3 swine) and compared to transthoracic cryoablation performed with a commercially available rigid cryoprobe (2 peripheral lung ablations in 1 swine). Procedural time and cryoablation success rates for endobronchial navigation and cryoneedle deployment were measured. Intraoperative CBCT imaging features of cryolesions were characterized and correlated with gross and H&E-stained sections of the explanted cryolesions.

RESULTS

The flexible cryoprobe was successfully navigated and delivered to each target through a steerable guiding sheath (6/6). At 4 min post ablation, 5/6 transbronchial and 2/2 transthoracic cryolesions were visible on CBCT. The volumes on CBCT images were 55.5±8.0 cm3 for central transbronchial ablations (n=2), 72.5±8.1 cm3 for peripheral transbronchial ablations (n=3), and 79.5±11.6 cm3 for peripheral transthoracic ablations (n=2). Pneumothorax developed in one animal post transbronchial ablation and during ablation in the transthoracic cryoablation. Images of cryoablation zones on CBCT correlated well with the matched gross and histopathology sections of the cryolesions.

CONCLUSIONS

Transbronchial cryoablation with a flexible cryoprobe, delivered through a steerable guiding sheath, is feasible. Transbronchial cryoablation zones are imageable with CBCT with gross and histopathology similar to transthoracic cryoablation.

Deep Learning-Based Interpretable AI for Prostate T2W MRI Quality Evaluation

RATIONALE AND OBJECTIVES

Prostate MRI quality is essential in guiding prostate biopsies. However, assessment of MRI quality is subjective with variation. Quality degradation sources exert varying impacts based on the sequence under consideration, such as T2W versus DWI. As a result, employing sequence-specific techniques for quality assessment could yield more advantageous outcomes. This study aims to develop an AI tool that offers a more consistent evaluation of T2W prostate MRI quality, efficiently identifying suboptimal scans while minimizing user bias.

MATERIALS AND METHODS

This retrospective study included 1046 patients from three cohorts (ProstateX [n = 347], All-comer in-house [n = 602], enriched bad-quality MRI in-house [n = 97]) scanned between January 2011 and May 2022. An expert reader assigned T2W MRIs a quality score. A train-validation-test split of 70:15:15 was applied, ensuring equal distribution of MRI scanners and protocols across all partitions. T2W quality AI classification model was based on 3D DenseNet121 architecture using MONAI framework. In addition to multiclassification, binary classification was utilized (Classes 0/1 vs. 2). A score of 0 was given to scans considered non-diagnostic or unusable, a score of 1 was given to those with acceptable diagnostic quality with some usability but with some quality distortions present, and a score of 2 was given to those considered optimal diagnostic quality and usability. Partial occlusion sensitivity maps were generated for anatomical correlation. Three body radiologists assessed reproducibility within a subgroup of 60 test cases using weighted Cohen Kappa.

RESULTS

The best validation multiclass accuracy of 77.1% (121/157) was achieved during training. In the test dataset, multiclassification accuracy was 73.9% (116/157), whereas binary accuracy was 84.7% (133/157). Sub-class sensitivity for binary quality distortion classification for class 0 was 100% (18/18), and sub-class specificity for T2W classification of absence/minimal quality distortions for class 2 was 90.5% (95/105). All three readers showed moderate to substantial agreement with ground truth (R1-R3 κ = 0.588, κ = 0.649, κ = 0.487, respectively), moderate to substantial agreement with each other (R1-R2 κ = 0.599, R1-R3 κ = 0.612, R2-R3 κ = 0.685), fair to moderate agreement with AI (R1-R3 κ = 0.445, κ = 0.410, κ = 0.292, respectively). AI showed substantial agreement with ground truth (κ = 0.704). 3D quality heatmap evaluation revealed that the most critical non-diagnostic quality imaging features from an AI perspective related to obscuration of the rectoprostatic space (94.4%, 17/18).

CONCLUSION

The 3D AI model can assess T2W prostate MRI quality with moderate accuracy and translate whole sequence-level classification labels into 3D voxel-level quality heatmaps for interpretation. Image quality has a significant downstream impact on ruling out clinically significant cancers. AI may be able to help with reproducible identification of MRI sequences requiring re-acquisition with explainability.

Evaluating Diagnostic Accuracy and Inter-reader Agreement of the Prostate Imaging After Focal Ablation Scoring System

Background and objective

Focal therapy (FT) is increasingly recognized as a promising approach for managing localized prostate cancer (PCa), notably reducing treatment-related morbidities. However, post-treatment anatomical changes present significant challenges for surveillance using current imaging techniques. This study aimed to evaluate the inter-reader agreement and efficacy of the Prostate Imaging after Focal Ablation (PI-FAB) scoring system in detecting clinically significant prostate cancer (csPCa) on post-FT multiparametric magnetic resonance imaging (mpMRI).

Methods

A retrospective cohort study was conducted involving patients who underwent primary FT for localized csPCa between 2013 and 2023, followed by post-FT mpMRI and a prostate biopsy. Two expert genitourinary radiologists retrospectively evaluated post-FT mpMRI using PI-FAB. The key measures included inter-reader agreement of PI-FAB scores, assessed by quadratic weighted Cohen's kappa (κ), and the system's efficacy in predicting in-field recurrence of csPCa, with a PI-FAB score cutoff of 3. Additional diagnostic metrics including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and overall accuracy were also evaluated.

Key findings and limitations

Scans from 38 patients were analyzed, revealing a moderate level of agreement in PI-FAB scoring (κ = 0.56). Both radiologists achieved sensitivity of 93% in detecting csPCa, although specificity, PPVs, NPVs, and accuracy varied.

Conclusions and clinical implications

The PI-FAB scoring system exhibited high sensitivity with moderate inter-reader agreement in detecting in-field recurrence of csPCa. Despite promising results, its low specificity and PPV necessitate further refinement. These findings underscore the need for larger studies to validate the clinical utility of PI-FAB, potentially aiding in standardizing post-treatment surveillance.

Patient summary

Focal therapy has emerged as a promising approach for managing localized prostate cancer, but limitations in current imaging techniques present significant challenges for post-treatment surveillance. The Prostate Imaging after Focal Ablation (PI-FAB) scoring system showed high sensitivity for detecting in-field recurrence of clinically significant prostate cancer. However, its low specificity and positive predictive value necessitate further refinement. Larger, more comprehensive studies are needed to fully validate its clinical utility.

Predicting 18F-DCFPyL-PET/CT Scan Positivity in Prostate Cancer Patients with Biochemical Recurrence

RATIONALE AND OBJECTIVES

To analyze variables that can predict the positivity of 18F-DCFPyL- positron emission tomography/computed tomography (PET/CT) and extent of disease in patients with biochemically recurrent (BCR) prostate cancer after primary local therapy with either radical prostatectomy or radiation therapy.

MATERIALS AND METHODS

This is a retrospective analysis of a prospective single institutional review board-approved study. We included 199 patients with biochemical recurrence and negative conventional imaging after primary local therapies (radical prostatectomy n = 127, radiation therapy n = 72). All patients underwent 18F-DCFPyL-PET/CT. Univariate and multivariate logistic regression analyses were used to determine predictors of a positive scan for both cohort of patients. Regression-based coefficients were used to develop nomograms predicting scan positivity and extra-pelvic disease. Decision curve analysis (DCA) was implemented to quantify nomogram's clinical benefit.

RESULTS

Of the 127 (63%) post-radical prostatectomy patients, 91 patients had positive scans - 61 of those with intrapelvic lesions and 30 with extra-pelvic lesions (i.e., retroperitoneal or distant nodes and/or bone/organ lesions). Of the 72 post-radiation therapy patients, 65 patients had positive scans - 39 of them had intrapelvic lesions and 26 extra-pelvic lesions. In the radical prostatectomy cohort, multivariate regression analysis revealed original International Society of Urological Pathology category, prostate-specific antigen (PSA), prostate-specific antigen doubling time (PSAdt), and time from BCR (mo) to scan were predictors for scan positivity and presence of extra-pelvic disease, with an area under the curve of 80% and 78%, respectively. Positive versus negative tumor margin after radical prostatectomy was not related to scan positivity or to the presence of positive extra-pelvic foci. In the radiation therapy cohort, multivariate regression analysis revealed that PSA, PSAdt, and time to BCR (mo) were predictors of extra-pelvic disease, with area under the curve of 82%. Because only seven patients in the radiation therapy cohort had negative scans, a prediction model for scan positivity could not be analyzed and only the presence of extra-pelvic disease was evaluated.

CONCLUSION

PSA and PSAdt are consistently significant predictors of 18F-DCFPyL PET/CT positivity and extra-pelvic disease in BCR prostate cancer patients. Stratifying the patient population into primary local treatment group enables the use of other variables as predictors, such as time since BCR. This nomogram may guide selection of the most suitable candidates for 18F-DCFPyL-PET/CT imaging.

Omicron detection with large language models and YouTube audio data

Publicly available audio data presents a unique opportunity for the development of digital health technologies with large language models (LLMs). In this study, YouTube was mined to collect audio data from individuals with self-declared positive COVID-19 tests as well as those with other upper respiratory infections (URI) and healthy subjects discussing a diverse range of topics. The resulting dataset was transcribed with the Whisper model and used to assess the capacity of LLMs for detecting self-reported COVID-19 cases and performing variant classification. Following prompt optimization, LLMs achieved accuracies of 0.89, 0.97, respectively, in the tasks of identifying self-reported COVID-19 cases and other respiratory illnesses. The model also obtained a mean accuracy of 0.77 at identifying the variant of self-reported COVID-19 cases using only symptoms and other health-related factors described in the YouTube videos. In comparison with past studies, which used scripted, standardized voice samples to capture biomarkers, this study focused on extracting meaningful information from public online audio data. This work introduced novel design paradigms for pandemic management tools, showing the potential of audio data in clinical and public health applications.

Automated prostate gland segmentation in challenging clinical cases: comparison of three artificial intelligence methods

OBJECTIVE

Automated methods for prostate segmentation on MRI are typically developed under ideal scanning and anatomical conditions. This study evaluates three different prostate segmentation AI algorithms in a challenging population of patients with prior treatments, variable anatomic characteristics, complex clinical history, or atypical MRI acquisition parameters.

MATERIALS AND METHODS

A single institution retrospective database was queried for the following conditions at prostate MRI: prior prostate-specific oncologic treatment, transurethral resection of the prostate (TURP), abdominal perineal resection (APR), hip prosthesis (HP), diversity of prostate volumes (large ≥ 150 cc, small ≤ 25 cc), whole gland tumor burden, magnet strength, noted poor quality, and various scanners (outside/vendors). Final inclusion criteria required availability of axial T2-weighted (T2W) sequence and corresponding prostate organ segmentation from an expert radiologist. Three previously developed algorithms were evaluated: (1) deep learning (DL)-based model, (2) commercially available shape-based model, and (3) federated DL-based model. Dice Similarity Coefficient (DSC) was calculated compared to expert. DSC by model and scan factors were evaluated with Wilcox signed-rank test and linear mixed effects (LMER) model.

RESULTS

683 scans (651 patients) met inclusion criteria (mean prostate volume 60.1 cc [9.05-329 cc]). Overall DSC scores for models 1, 2, and 3 were 0.916 (0.707-0.971), 0.873 (0-0.997), and 0.894 (0.025-0.961), respectively, with DL-based models demonstrating significantly higher performance (p < 0.01). In sub-group analysis by factors, Model 1 outperformed Model 2 (all p < 0.05) and Model 3 (all p < 0.001). Performance of all models was negatively impacted by prostate volume and poor signal quality (p < 0.01). Shape-based factors influenced DL models (p < 0.001) while signal factors influenced all (p < 0.001).

CONCLUSION

Factors affecting anatomical and signal conditions of the prostate gland can adversely impact both DL and non-deep learning-based segmentation models.

X-ray imageable, drug-loaded hydrogel that forms at body temperature for image-guided, needle- based locoregional drug delivery

Liver cancer ranks as the fifth leading cause of cancer-related death globally. Direct intratumoral injections of anti-cancer therapeutics may improve therapeutic efficacy and mitigate adverse effects compared to intravenous injections. Some challenges of intratumoral injections are that the liquid drug formulation may not remain localized and have unpredictable volumetric distribution. Thus, drug delivery varies widely, highly-dependent upon technique. An x-ray imageable poloxamer 407 (POL)-based drug delivery gel was developed and characterized, enabling real-time feedback. Utilizing three needle devices, POL or a control iodinated contrast solution were injected into an ex vivo bovine liver. The 3D distribution was assessed with cone beam computed tomography (CBCT). The 3D distribution of POL gels demonstrated localized spherical morphologies regardless of the injection rate. In addition, the gel 3D conformal distribution could be intentionally altered, depending on the injection technique. When doxorubicin (DOX) was loaded into the POL and injected, DOX distribution on optical imaging matched iodine distribution on CBCT suggesting spatial alignment of DOX and iodine localization in tissue. The controllability and localized deposition of this formulation may ultimately reduce the dependence on operator technique, reduce systemic side effects, and facilitate reproducibility across treatments, through more predictable standardized delivery.

Smartphone Augmented Reality Outperforms Conventional CT Guidance for Composite Ablation Margins in Phantom Models

PURPOSE

To develop and evaluate a smartphone augmented reality (AR) system for a large 50-mm liver tumor ablation with treatment planning for composite overlapping ablation zones.

MATERIALS AND METHODS

A smartphone AR application was developed to display tumor, probe, projected probe paths, ablated zones, and real-time percentage of the ablated target tumor volume. Fiducial markers were attached to phantoms and an ablation probe hub for tracking. The system was evaluated with tissue-mimicking thermochromic phantoms and gel phantoms. Four interventional radiologists performed 2 trials each of 3 probe insertions per trial using AR guidance versus computed tomography (CT) guidance approaches in 2 gel phantoms. Insertion points and optimal probe paths were predetermined. On Gel Phantom 2, serial ablated zones were saved and continuously displayed after each probe placement/adjustment, enabling feedback and iterative planning. The percentages of tumor ablated for AR guidance versus CT guidance, and with versus without display of recorded ablated zones, were compared among interventional radiologists with pairwise t-tests.

RESULTS

The means of percentages of tumor ablated for CT freehand and AR guidance were 36% ± 7 and 47% ± 4 (P = .004), respectively. The mean composite percentages of tumor ablated for AR guidance were 43% ± 1 (without) and 50% ± 2 (with display of ablation zone) (P = .033). There was no strong correlation between AR-guided percentage of ablation and years of experience (r < 0.5), whereas there was a strong correlation between CT-guided percentage of ablation and years of experience (r > 0.9).

CONCLUSIONS

A smartphone AR guidance system for dynamic iterative large liver tumor ablation was accurate, performed better than conventional CT guidance, especially for less experienced interventional radiologists, and enhanced more standardized performance across experience levels for ablation of a 50-mm tumor.

Understanding barriers to reducing antimicrobials on Australian dairy farms: A qualitative analysis

INTRODUCTION

Reducing antibiotic use in production animal systems is one strategy which may help to limit the development of antimicrobial resistance. To reduce antimicrobial use in food-producing animals, it is important to first understand how antibiotics are used on farm and what barriers exist to decreasing their use. In dairy production systems, mastitis is one of the most common reasons for administering antimicrobials. Therefore, it is important to understand the motivations and behaviours of dairy farmers in relation to the diagnosis, treatment and prevention of mastitis.

MATERIALS AND METHODS

In this study, we interviewed a sample of dairy farmers and dairy industry professionals from the major dairying regions of eastern Australia regarding their current practices used to diagnose, treat, and control subclinical and clinical mastitis. Inductive thematic analysis was used to code interview transcripts and identify the recurrent themes.

RESULTS

Four overarching themes were identified: (1) the challenges associated with the detection and diagnosis of clinical mastitis, including with laboratory culture, (2) the motivations behind treatment decisions for different cases, (3) decisions around dry cow therapy and the role of herd recording, and (4) concerns regarding the development of antimicrobial resistance.

DISCUSSION

This study identifies several challenges which may limit the ability of Australian dairy farmers to reduce antimicrobial use on farm, such as the need for rapid and reliable diagnostic tests capable of identifying the pathogenic causes of mastitis and the difficulties associated with conducting herd recording for the implementation of selective dry cow therapy. Industry professionals were concerned that farmers were not using individual cow records to aid in treatment decisions, which could result in unnecessary antimicrobial use. The results of this study can act as the basis for future research aimed at assessing these issues across the broader Australian dairy industry.

Combined immune checkpoint inhibition with durvalumab and tremelimumab with and without radiofrequency ablation in patients with advanced biliary tract carcinoma

BACKGROUND

Current standard of care for advanced biliary tract cancer (BTC) is gemcitabine, cisplatin plus anti-PD1/PD-L1, but response rates are modest. The purpose of this study was to explore the efficacy and safety of durvalumab (anti-PD-L1) and tremelimumab (anti-CTLA-4), with and without an interventional radiology (IR) procedure in advanced BTC.

METHODS

Eligible patients with advanced BTC who had received or refused at least one prior line of systemic therapy were treated with tremelimumab and durvalumab for four combined doses followed by monthly durvalumab alone with and without an IR procedure until the progression of disease or unacceptable toxicity. Objective response was assessed through CT or MRI by Response Evaluation Criteria in Solid Tumors (RECIST, version 1.1) every 8 weeks. Adverse events (AEs) were recorded and managed. The primary endpoint was 6-month progression-free survival (PFS).

RESULTS

Twenty-three patients with advanced BTC were enrolled; 17 patients were assigned to treatment with durvalumab and tremelimumab (Durva/Treme); and 6 patients were treated with the combination of durvalumab, tremelimumab plus IR procedure (Durva/Treme + IR). The best clinical responses in the Durva/Treme arm were partial response (n = 1), stable disease (n = 5), progressive disease (n = 5), and in the Durva/Treme + IR arm: partial response (n = 0), stable disease (n = 3), progressive disease (n = 3). The median PFS was 2.2 months (95% CI: 1.3-3.1 months) in the Durva/Treme arm and 2.9 months (95% CI: 1.9-4.7 months) in the Durva/Treme + IR arm (p = 0.27). The median OS was 5.1 months (95% CI: 2.5-6.9 months) in the Durva/Treme arm and 5.8 months (95% CI: 2.9-40.1 months) in the Durva/Treme + IR arm (p = 0.31). The majority of AEs were grades 1-2.

CONCLUSION

Durva/Treme and Durva/Treme + IR showed similar efficacy. With a manageable safety profile. Larger studies are needed to fully characterize the efficacy of Durva/Treme ± IR in advanced BTC.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study

RATIONALE AND OBJECTIVES

Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs. metastatic lesion classification on staging CTs and to compare its performance with radiologists.

MATERIALS AND METHODS

This retrospective study developed two AI models using 297 staging CT scans (81 metastatic) with 4601 benign and 1911 metastatic lesions in PCa patients. Metastases were validated by follow-up scans, bone biopsy, or PET/CT. Segmentation AI (3DAISeg) was developed using the lesion contours delineated by a radiologist. 3DAISeg performance was evaluated with the Dice similarity coefficient, and classification AI (3DAIClass) performance on AI and radiologist contours was assessed with F1-score and accuracy. Training/validation/testing data partitions of 70:15:15 were used. A multi-reader study was performed with two junior and two senior radiologists within a subset of the testing dataset (n = 36).

RESULTS

In 45 unseen staging CT scans (12 metastatic PCa) with 669 benign and 364 metastatic lesions, 3DAISeg detected 73.1% of metastatic (266/364) and 72.4% of benign lesions (484/669). Each scan averaged 12 extra segmentations (range: 1-31). All metastatic scans had at least one detected metastatic lesion, achieving a 100% patient-level detection. The mean Dice score for 3DAISeg was 0.53 (median: 0.59, range: 0-0.87). The F1 for 3DAIClass was 94.8% (radiologist contours) and 92.4% (3DAISeg contours), with a median false positive of 0 (range: 0-3). Using radiologist contours, 3DAIClass had PPV and NPV rates comparable to junior and senior radiologists: PPV (semi-automated approach AI 40.0% vs. Juniors 32.0% vs. Seniors 50.0%) and NPV (AI 96.2% vs. Juniors 95.7% vs. Seniors 91.9%). When using 3DAISeg, 3DAIClass mimicked junior radiologists in PPV (pure-AI 20.0% vs. Juniors 32.0% vs. Seniors 50.0%) but surpassed seniors in NPV (pure-AI 93.8% vs. Juniors 95.7% vs. Seniors 91.9%).

CONCLUSION

Our lesion detection and classification AI model performs on par with junior and senior radiologists in discerning benign and metastatic lesions on staging CTs obtained for PCa.

VB-111 (ofranergene obadenovec) in combination with nivolumab in patients with microsatellite stable colorectal liver metastases: a single center, single arm, phase II trial

BACKGROUND

Microsatellite stable colorectal liver metastases (MSS CLM) maintain an immunosuppressive tumor microenvironment (TME). Historically, immune-based approaches have been ineffective. VB-111 (ofranergene obadenovec) is a genetically-modified adenoviral vector targeting the TME; its unique dual mechanism induces an immune response and disrupts neovascularization. Checkpoint inhibition may synergize the immune response induced by viral-mediated anti-angiogenic gene therapy. We aimed to examine the safety and antitumor activity of VB-111 and nivolumab in patients with refractory MSS CLM and to characterize immunological treatment-response.

METHODS

This was a phase II study of adult patients with histologically-confirmed MSS CLM who progressed on prior therapy. A priming dose of VB-111 1×1013 viral particles was given intravenously 2 weeks prior to starting biweekly nivolumab 240 mg and continued every 6 weeks. The combination continued until disease progression or unacceptable toxicity. The primary objectives were overall response rate and safety/tolerability. Secondary objectives included median overall survival and progression-free survival. Correlative studies were performed on paired tumor biopsies and blood.

RESULTS

Between August 2020 and December 2021, 14 patients were enrolled with median age 50.5 years (40-75), and 14% were women. Median follow-up was 5.5 months. Of the 10 evaluable patients, the combination of VB-111 and nivolumab failed to demonstrate radiographic responses; at best, 2 patients had stable disease. Median overall survival was 5.5 months (95% CI: 2.3 to 10.8), and median progression-free survival was 1.8 months (95% CI: 1.4 to 1.9). The most common grade 3-4 treatment-related adverse events were fever/chills, influenza-like symptoms, and lymphopenia. No treatment-related deaths were reported. Qualitative analysis of immunohistochemical staining of paired tumor biopsies did not demonstrate significant immune infiltration after treatment, except for one patient who had exceptional survival (26.0 months). Immune analysis of peripheral blood mononuclear cells showed an increase of PD-1highKi67highCD8+ T cells and HLA-DRhigh T cells after VB-111 priming dose. Plasma cytokines interleukin-10 and tumor necrosis factor-α increased after treatment with both drugs.

CONCLUSION

In patients with MSS CLM, VB-111 and nivolumab did not improve overall response rate or survival but were tolerated with minimal toxicities. While challenging to distinguish between antiviral or antitumor, correlative studies demonstrated an immune response with activation and proliferation of CD8+ T cells systemically that was poorly sustained.

TRIAL REGISTRATION NUMBER

NCT04166383.

PI-RADS Version 2.0 Versus Version 2.1: Comparison of Prostate Cancer Gleason Grade Upgrade and Downgrade Rates From MRI-Targeted Biopsy to Radical Prostatectomy

BACKGROUND. Precise risk stratification through MRI/ultrasound (US) fusion-guided targeted biopsy (TBx) can guide optimal prostate cancer (PCa) management. OBJECTIVE. The purpose of this study was to compare PI-RADS version 2.0 (v2.0) and PI-RADS version 2.1 (v2.1) in terms of the rates of International Society of Urological Pathology (ISUP) grade group (GG) upgrade and downgrade from TBx to radical prostatectomy (RP). METHODS. This study entailed a retrospective post hoc analysis of patients who underwent 3-T prostate MRI at a single institution from May 2015 to March 2023 as part of three prospective clinical trials. Trial participants who underwent MRI followed by MRI/US fusion-guided TBx and RP within a 1-year interval were identified. A single genitourinary radiologist performed clinical interpretations of the MRI examinations using PI-RADS v2.0 from May 2015 to March 2019 and PI-RADS v2.1 from April 2019 to March 2023. Upgrade and downgrade rates from TBx to RP were compared using chi-square tests. Clinically significant cancer was defined as ISUP GG2 or greater. RESULTS. The final analysis included 308 patients (median age, 65 years; median PSA density, 0.16 ng/mL2). The v2.0 group (n = 177) and v2.1 group (n = 131) showed no significant difference in terms of upgrade rate (29% vs 22%, respectively; p = .15), downgrade rate (19% vs 21%, p = .76), clinically significant upgrade rate (14% vs 10%, p = .27), or clinically significant downgrade rate (1% vs 1%, p > .99). The upgrade rate and downgrade rate were also not significantly different between the v2.0 and v2.1 groups when stratifying by index lesion PI-RADS category or index lesion zone, as well as when assessed only in patients without a prior PCa diagnosis (all p > .01). Among patients with GG2 or GG3 at RP (n = 121 for v2.0; n = 103 for v2.1), the concordance rate between TBx and RP was not significantly different between the v2.0 and v2.1 groups (53% vs 57%, p = .51). CONCLUSION. Upgrade and downgrade rates from TBx to RP were not significantly different between patients whose MRI examinations were clinically interpreted using v2.0 or v2.1. CLINICAL IMPACT. Implementation of the most recent PI-RADS update did not improve the incongruence in PCa grade assessment between TBx and surgery.

C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation

Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual annotation of the vessel masks is challenging and resource-intensive due to subtle branches and complex structures. To overcome this issue, this paper presents a self-supervised vessel segmentation method, dubbed the contrastive diffusion adversarial representation learning (C-DARL) model. Our model is composed of a diffusion module and a generation module that learns the distribution of multi-domain blood vessel data by generating synthetic vessel images from diffusion latent. Moreover, we employ contrastive learning through a mask-based contrastive loss so that the model can learn more realistic vessel representations. To validate the efficacy, C-DARL is trained using various vessel datasets, including coronary angiograms, abdominal digital subtraction angiograms, and retinal imaging. Experimental results confirm that our model achieves performance improvement over baseline methods with noise robustness, suggesting the effectiveness of C-DARL for vessel segmentation.Our source code is available at https://github.com/boahK/MEDIA_CDARL.2.

A Review of Ultrasound-Mediated Checkpoint Inhibitor Immunotherapy

Over the past decade, immunotherapy has emerged as a major modality in cancer medicine. However, despite its unprecedented success, immunotherapy currently benefits only a subgroup of patients, may induce responses of limited duration and is associated with potentially treatment-limiting side effects. In addition, responses to immunotherapeutics are sometimes diminished by the emergence of a complex array of resistance mechanisms. The efficacy of immunotherapy depends on dynamic interactions between tumour cells and the immune landscape in the tumour microenvironment. Ultrasound, especially in conjunction with cavitation-promoting agents such as microbubbles, can assist in the uptake and/or local release of immunotherapeutic agents at specific target sites, thereby increasing treatment efficacy and reducing systemic toxicity. There is also increasing evidence that ultrasound and/or cavitation may themselves directly stimulate a beneficial immune response. In this review, we summarize the latest developments in the use of ultrasound and cavitation agents to promote checkpoint inhibitor immunotherapy.

Quality of T2-weighted MRI re-acquisition versus deep learning GAN image reconstruction: A multi-reader study

PURPOSE

To evaluate CycleGAN's ability to enhance T2-weighted image (T2WI) quality.

METHOD

A CycleGAN algorithm was used to enhance T2WI quality. 96 patients (192 scans) were identified from patients who underwent multiple axial T2WI due to poor quality on the first attempt (RAD1) and improved quality on re-acquisition (RAD2). CycleGAN algorithm gave DL classifier scores (0-1) for quality quantification and produced enhanced versions of QI1 and QI2 from RAD1 and RAD2, respectively. A subset (n = 20 patients) was selected for a blinded, multi-reader study, where four radiologists rated T2WI on a scale of 1-4 for quality. The multi-reader study presented readers with 60 image pairs (RAD1 vs RAD2, RAD1 vs QI1, and RAD2 vs QI2), allowing for selecting sequence preferences and quantifying the quality changes.

RESULTS

The DL classifier correctly discerned 71.9 % of quality classes, with 90.6 % (96/106) as poor quality and 48.8 % (42/86) as diagnostic in original sequences (RAD1, RAD2). CycleGAN images (QI1, QI2) demonstrated quantitative improvements, with consistently higher DL classifier scores than original scans (p < 0.001). In the multi-reader analysis, CycleGAN demonstrated no qualitative improvements, with diminished overall quality and motion in QI2 in most patients compared to RAD2, with noise levels remaining similar (8/20). No readers preferred QI2 to RAD2 for diagnosis.

CONCLUSION

Despite quantitative enhancements with CycleGAN, there was no qualitative boost in T2WI diagnostic quality, noise, or motion. Expert radiologists didn't favor CycleGAN images over standard scans, highlighting the divide between quantitative and qualitative metrics.

Comparison of MRI-Based Staging and Pathologic Staging for Predicting Biochemical Recurrence of Prostate Cancer After Radical Prostatectomy

BACKGROUND. Currently most clinical models for predicting biochemical recurrence (BCR) of prostate cancer (PCa) after radical prostatectomy (RP) incorporate staging information from RP specimens, creating a gap in preoperative risk assessment. OBJECTIVE. The purpose of our study was to compare the utility of presurgical staging information from MRI and postsurgical staging information from RP pathology in predicting BCR in patients with PCa. METHODS. This retrospective study included 604 patients (median age, 60 years) with PCa who underwent prostate MRI before RP from June 2007 to December 2018. A single genitourinary radiologist assessed MRI examinations for extraprostatic extension (EPE) and seminal vesicle invasion (SVI) during clinical interpretations. The utility of EPE and SVI on MRI and RP pathology for BCR prediction was assessed through Kaplan-Meier and Cox proportional hazards analyses. Established clinical BCR prediction models, including the University of California San Francisco Cancer of the Prostate Risk Assessment (UCSF-CAPRA) model and the Cancer of the Prostate Risk Assessment Postsurgical (CAPRA-S) model, were evaluated in a subset of 374 patients with available Gleason grade groups from biopsy and RP pathology; two CAPRA-MRI models (CAPRA-S model with modifications to replace RP pathologic staging features with MRI staging features) were also assessed. RESULTS. Univariable predictors of BCR included EPE on MRI (HR = 3.6), SVI on MRI (HR = 4.4), EPE on RP pathology (HR = 5.0), and SVI on RP pathology (HR = 4.6) (all p < .001). Three-year BCR-free survival (RFS) rates for patients without versus with EPE were 84% versus 59% for MRI and 89% versus 58% for RP pathology, and 3-year RFS rates for patients without versus with SVI were 82% versus 50% for MRI and 83% versus 54% for RP histology (all p < .001). For patients with T3 disease on RP pathology, 3-year RFS rates were 67% and 41% for patients without and with T3 disease on MRI. AUCs of CAPRA models, including CAPRA-MRI models, ranged from 0.743 to 0.778. AUCs were not significantly different between CAPRA-S and CAPRA-MRI models (p > .05). RFS rates were significantly different between low- and intermediate-risk groups for only CAPRA-MRI models (80% vs 51% and 74% vs 44%; both p < .001). CONCLUSION. Presurgical MRI-based staging features perform comparably to postsurgical pathologic staging features for predicting BCR. CLINICAL IMPACT. MRI staging can preoperatively identify patients at high BCR risk, helping to inform early clinical decision-making. TRIAL REGISTRATION. ClinicalTrials.gov NCT00026884 and NCT02594202.

Is prostatic adenocarcinoma with cribriform architecture more difficult to detect on prostate MRI?

BACKGROUND

Cribriform (CBFM) pattern on prostate biopsy has been implicated as a predictor for high-risk features, potentially leading to adverse outcomes after definitive treatment. This study aims to investigate whether the CBFM pattern containing prostate cancers (PCa) were associated with false negative magnetic resonance imaging (MRI) and determine the association between MRI and histopathological disease burden.

METHODS

Patients who underwent multiparametric magnetic resonance imaging (mpMRI), combined 12-core transrectal ultrasound (TRUS) guided systematic (SB) and MRI/US fusion-guided biopsy were retrospectively queried for the presence of CBFM pattern at biopsy. Biopsy cores and lesions were categorized as follows: C0 = benign, C1 = PCa with no CBFM pattern, C2 = PCa with CBFM pattern. Correlation between cancer core length (CCL) and measured MRI lesion dimension were assessed using a modified Pearson correlation test for clustered data. Differences between the biopsy core groups were assessed with the Wilcoxon-signed rank test with clustering.

RESULTS

Between 2015 and 2022, a total of 131 consecutive patients with CBFM pattern on prostate biopsy and pre-biopsy mpMRI were included. Clinical feature analysis included 1572 systematic biopsy cores (1149 C0, 272 C1, 151 C2) and 736 MRI-targeted biopsy cores (253 C0, 272 C1, 211 C2). Of the 131 patients with confirmed CBFM pathology, targeted biopsy (TBx) alone identified CBFM in 76.3% (100/131) of patients and detected PCa in 97.7% (128/131) patients. SBx biopsy alone detected CBFM in 61.1% (80/131) of patients and PCa in 90.8% (119/131) patients. TBx and SBx had equivalent detection in patients with smaller prostates (p = 0.045). For both PCa lesion groups there was a positive and significant correlation between maximum MRI lesion dimension and CCL (C1 lesions: p < 0.01, C2 lesions: p < 0.001). There was a significant difference in CCL between C1 and C2 lesions for T2 scores of 3 and 5 (p ≤ 0.01, p ≤ 0.01, respectively) and PI-RADS 5 lesions (p ≤ 0.01), with C2 lesions having larger CCL, despite no significant difference in MRI lesion dimension.

CONCLUSIONS

The extent of disease for CBFM-containing tumors is difficult to capture on mpMRI. When comparing MRI lesions of similar dimensions and PIRADS scores, CBFM-containing tumors appear to have larger cancer yield on biopsy. Proper staging and planning of therapeutic interventions is reliant on accurate mpMRI estimation. Special considerations should be taken for patients with CBFM pattern on prostate biopsy.

Immune Effects of Cryoablation in Woodchuck Hepatocellular Carcinoma

Objectives

Local and systemic immune responses evoked by locoregional therapies such as cryoablation are incompletely understood. The aim of this study was to characterize cryoablation-related immune response and the capacity of immune drugs to augment immunity upon cryoablation for the treatment of hepatocellular carcinoma (HCC) using a woodchuck hepatocellular carcinoma model.

Materials and Methods

Twelve woodchucks chronically infected with woodchuck hepatitis virus and with hepatocellular carcinoma underwent imaging with contrast-enhanced CT. Partial cryoablation of tumors in three woodchucks was performed. Fourteen days after cryoablation, liver tissues were harvested and stained with H&E and TUNEL, and immune infiltrates were quantified. Peripheral blood mononuclear cells (PBMC) were collected from ablated and nonablated woodchucks, labeled with carboxyfluorescein succinimidyl ester (CFSE) and cultured with immune-modulating drugs, including a small PD-L1 antagonist molecule (BMS-202) and three TLR7/8 agonists (DSR 6434, GS-9620, gardiquimod). After incubation, cell replication and immune cell populations were analyzed by flow cytometry.

Results

Local immune response in tumors was characterized by an increased number of CD3+ T lymphocytes and natural killer cells in the cryolesion margin compared to other tumor regions. T regulatory cells were found in higher numbers in distant tumors within the liver compared to untreated or control tumors. Cryoablation also augmented the systemic immune response as demonstrated by higher numbers of PBMC responses upon immune drug stimulation in the cryoablation group.

Conclusions

Partial cryoablation augmented immune effects in both treated and remote untreated tumor microenvironments, as well as systemically, in woodchucks with HCC. Characterization of these mechanisms may enhance development of novel drug-device combinations for treatment of HCC.

A Novel Magnetic Resonance Imaging/Ultrasound Fusion Prostate Biopsy Technique Using Transperineal Ultrasound: An Initial Experience

OBJECTIVE

To report an initial experience with a novel, "fully" transperineal (TP) prostate fusion biopsy using an unconstrained ultrasound transducer placed on the perineal skin to guide biopsy needles inserted via a TP approach.

METHODS

Conventional TP prostate biopsies for detection of prostate cancer have been performed with transrectal ultrasound, requiring specialized hardware, imposing limitations on needle trajectory, and contributing to patient discomfort. Seventy-six patients with known or suspected prostate cancer underwent 78 TP biopsy sessions in an academic center between June 2018 and April 2022 and were included in this study. These patients underwent TP prostate fusion biopsy using a grid or freehand device with transrectal ultrasound as well as TP prostate fusion biopsy using TP ultrasound in the same session. Per-session and per-lesion cancer detection rates were compared for conventional and fully TP biopsies using Fisher exact and McNemar's tests.

RESULTS

After a refinement period in 30 patients, 92 MRI-visible prostate lesions were sampled in 46 subsequent patients, along with repeat biopsies in 2 of the 30 patients from the refinement period. Grade group ≥2 cancer was diagnosed in 24/92 lesions (26%) on conventional TP biopsy (17 lesions with grid, 7 with freehand device), and in 25/92 lesions (27%) on fully TP biopsy (P = 1.00), with a 73/92 (79%) rate of agreement for grade group ≥2 cancer between the two methods.

CONCLUSION

Fully TP biopsy is feasible and may detect prostate cancer with detection rates comparable to conventional TP biopsy.

Evaluation of a Deep Learning-based Algorithm for Post-Radiotherapy Prostate Cancer Local Recurrence Detection Using Biparametric MRI

OBJECTIVE

To evaluate a biparametric MRI (bpMRI)-based artificial intelligence (AI) model for the detection of local prostate cancer (PCa) recurrence in patients with radiotherapy history.

MATERIALS AND METHODS

This study included post-radiotherapy patients undergoing multiparametric MRI and subsequent MRI/US fusion-guided and/or systematic biopsy. Histopathology results were used as ground truth. The recurrent cancer detection sensitivity of a bpMRI-based AI model, which was developed on a large dataset to primarily identify lesions in treatment-naïve patients, was compared to a prospective radiologist assessment using the Wald test. Subanalysis was conducted on patients stratified by the treatment modality (external beam radiation treatment [EBRT] and brachytherapy) and the prostate volume quartiles.

RESULTS

Of the 62 patients included (median age = 70 years; median PSA = 3.51 ng/ml; median prostate volume = 27.55 ml), 56 recurrent PCa foci were identified within 46 patients. The AI model detected 40 lesions in 35 patients. The AI model performance was lower than the prospective radiology interpretation (Rad) on a patient-(AI: 76.1% vs. Rad: 91.3%, p = 0.02) and lesion-level (AI: 71.4% vs. Rad: 87.5%, p = 0.01). The mean number of false positives per patient was 0.35 (range: 0-2). The AI model performance was higher in EBRT group both on patient-level (EBRT: 81.5% [22/27] vs. brachytherapy: 68.4% [13/19]) and lesion-level (EBRT: 79.4% [27/34] vs. brachytherapy: 59.1% [13/22]). In patients with gland volumes >34 ml (n = 25), detection sensitivities were 100% (11/11) and 94.1% (16/17) on patient- and lesion-level, respectively.

CONCLUSION

The reported bpMRI-based AI model detected the majority of locally recurrent prostate cancer after radiotherapy. Further testing including external validation of this model is warranted prior to clinical implementation.

Hydrogel drug delivery systems for minimally invasive local immunotherapy of cancer

Although systemic immunotherapy has achieved durable responses and improved survival for certain patients and cancer types, low response rates and immune system-related systemic toxicities limit its overall impact. Intratumoral (intralesional) delivery of immunotherapy is a promising technique to combat mechanisms of tumor immune suppression within the tumor microenvironment and reduce systemic drug exposure and associated side effects. However, intratumoral injections are prone to variable tumor drug distribution and leakage into surrounding tissues, which can compromise efficacy and contribute to toxicity. Controlled release drug delivery systems such as in situ-forming hydrogels are promising vehicles for addressing these challenges by providing improved spatio-temporal control of locally administered immunotherapies with the goal of promoting systemic tumor-specific immune responses and abscopal effects. In this review we will discuss concepts, applications, and challenges in local delivery of immunotherapy using controlled release drug delivery systems with a focus on intratumorally injected hydrogel-based drug carriers.

Deep Learning-Based T2-weighted MR Image Quality Assessment and Its Impact on Prostate Cancer Detection Rates

BACKGROUND

Image quality evaluation of prostate MRI is important for successful implementation of MRI into localized prostate cancer diagnosis.

PURPOSE

To examine the impact of image quality on prostate cancer detection using an in-house previously developed artificial intelligence (AI) algorithm.

STUDY TYPE

Retrospective.

SUBJECTS

615 consecutive patients (median age 67 [interquartile range [IQR]: 61-71] years) with elevated serum PSA (median PSA 6.6 [IQR: 4.6-9.8] ng/mL) prior to prostate biopsy.

FIELD STRENGTH/SEQUENCE

3.0T/T2-weighted turbo-spin-echo MRI, high b-value echo-planar diffusion-weighted imaging, and gradient recalled echo dynamic contrast-enhanced.

ASSESSMENTS

Scans were prospectively evaluated during clinical readout using PI-RADSv2.1 by one genitourinary radiologist with 17 years of experience. For each patient, T2-weighted images (T2WIs) were classified as high-quality or low-quality based on evaluation of both general distortions (eg, motion, distortion, noise, and aliasing) and perceptual distortions (eg, obscured delineation of prostatic capsule, prostatic zones, and excess rectal gas) by a previously developed in-house AI algorithm. Patients with PI-RADS category 1 underwent 12-core ultrasound-guided systematic biopsy while those with PI-RADS category 2-5 underwent combined systematic and targeted biopsies. Patient-level cancer detection rates (CDRs) were calculated for clinically significant prostate cancer (csPCa, International Society of Urological Pathology Grade Group ≥2) by each biopsy method and compared between high- and low-quality images in each PI-RADS category.

STATISTICAL TESTS

Fisher's exact test. Bootstrap 95% confidence intervals (CI). A P value <0.05 was considered statistically significant.

RESULTS

385 (63%) T2WIs were classified as high-quality and 230 (37%) as low-quality by AI. Targeted biopsy with high-quality T2WIs resulted in significantly higher clinically significant CDR than low-quality images for PI-RADS category 4 lesions (52% [95% CI: 43-61] vs. 32% [95% CI: 22-42]). For combined biopsy, there was no significant difference in patient-level CDRs for PI-RADS 4 between high- and low-quality T2WIs (56% [95% CI: 47-64] vs. 44% [95% CI: 34-55]; P = 0.09).

DATA CONCLUSION

Higher quality T2WIs were associated with better targeted biopsy clinically significant cancer detection performance for PI-RADS 4 lesions. Combined biopsy might be needed when T2WI is lower quality.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Artificial Intelligence and Infectious Disease Imaging

The mass production of the graphics processing unit and the coronavirus disease 2019 (COVID-19) pandemic have provided the means and the motivation, respectively, for rapid developments in artificial intelligence (AI) and medical imaging techniques. This has led to new opportunities to improve patient care but also new challenges that must be overcome before these techniques are put into practice. In particular, early AI models reported high performances but failed to perform as well on new data. However, these mistakes motivated further innovation focused on developing models that were not only accurate but also stable and generalizable to new data. The recent developments in AI in response to the COVID-19 pandemic will reap future dividends by facilitating, expediting, and informing other medical AI applications and educating the broad academic audience on the topic. Furthermore, AI research on imaging animal models of infectious diseases offers a unique problem space that can fill in evidence gaps that exist in clinical infectious disease research. Here, we aim to provide a focused assessment of the AI techniques leveraged in the infectious disease imaging research space, highlight the unique challenges, and discuss burgeoning solutions.

Ultrasound Frame-to-Volume Registration via Deep Learning for Interventional Guidance

Fusing intraoperative 2-D ultrasound (US) frames with preoperative 3-D magnetic resonance (MR) images for guiding interventions has become the clinical gold standard in image-guided prostate cancer biopsy. However, developing an automatic image registration system for this application is challenging because of the modality gap between US/MR and the dimensionality gap between 2-D/3-D data. To overcome these challenges, we propose a novel US frame-to-volume registration (FVReg) pipeline to bridge the dimensionality gap between 2-D US frames and 3-D US volume. The developed pipeline is implemented using deep neural networks, which are fully automatic without requiring external tracking devices. The framework consists of three major components, including one) a frame-to-frame registration network (Frame2Frame) that estimates the current frame's 3-D spatial position based on previous video context, two) a frame-to-slice correction network (Frame2Slice) adjusting the estimated frame position using the 3-D US volumetric information, and three) a similarity filtering (SF) mechanism selecting the frame with the highest image similarity with the query frame. We validated our method on a clinical dataset with 618 subjects and tested its potential on real-time 2-D-US to 3-D-MR fusion navigation tasks. The proposed FVReg achieved an average target navigation error of 1.93 mm at 5-14 fps. Our source code is publicly available at https://github.com/DIAL-RPI/Frame-to-Volume-Registration.

Deep-Learning-Based Whole-Lung and Lung-Lesion Quantification Despite Inconsistent Ground Truth: Application to Computerized Tomography in SARS-CoV-2 Nonhuman Primate Models

RATIONALE AND OBJECTIVES

Animal modeling of infectious diseases such as coronavirus disease 2019 (COVID-19) is important for exploration of natural history, understanding of pathogenesis, and evaluation of countermeasures. Preclinical studies enable rigorous control of experimental conditions as well as pre-exposure baseline and longitudinal measurements, including medical imaging, that are often unavailable in the clinical research setting. Computerized tomography (CT) imaging provides important diagnostic, prognostic, and disease characterization to clinicians and clinical researchers. In that context, automated deep-learning systems for the analysis of CT imaging have been broadly proposed, but their practical utility has been limited. Manual outlining of the ground truth (i.e., lung-lesions) requires accurate distinctions between abnormal and normal tissues that often have vague boundaries and is subject to reader heterogeneity in interpretation. Indeed, this subjectivity is demonstrated as wide inconsistency in manual outlines among experts and from the same expert. The application of deep-learning data-science tools has been less well-evaluated in the preclinical setting, including in nonhuman primate (NHP) models of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection/COVID-19, in which the translation of human-derived deep-learning tools is challenging. The automated segmentation of the whole lung and lung lesions provides a potentially standardized and automated method to detect and quantify disease.

MATERIALS AND METHODS

We used deep-learning-based quantification of the whole lung and lung lesions on CT scans of NHPs exposed to SARS-CoV-2. We proposed a novel multi-model ensemble technique to address the inconsistency in the ground truths for deep-learning-based automated segmentation of the whole lung and lung lesions. Multiple models were obtained by training the convolutional neural network (CNN) on different subsets of the training data instead of having a single model using the entire training dataset. Moreover, we employed a feature pyramid network (FPN), a CNN that provides predictions at different resolution levels, enabling the network to predict objects with wide size variations.

RESULTS

We achieved an average of 99.4 and 60.2% Dice coefficients for whole-lung and lung-lesion segmentation, respectively. The proposed multi-model FPN outperformed well-accepted methods U-Net (50.5%), V-Net (54.5%), and Inception (53.4%) for the challenging lesion-segmentation task. We show the application of segmentation outputs for longitudinal quantification of lung disease in SARS-CoV-2-exposed and mock-exposed NHPs.

CONCLUSION

Deep-learning methods should be optimally characterized for and targeted specifically to preclinical research needs in terms of impact, automation, and dynamic quantification independently from purely clinical applications.

Morphometric characterization and temporal temperature measurements during hepatic microwave ablation in swine

PURPOSE

Heat-induced destruction of cancer cells via microwave ablation (MWA) is emerging as a viable treatment of primary and metastatic liver cancer. Prediction of the impacted zone where cell death occurs, especially in the presence of vasculature, is challenging but may be achieved via biophysical modeling. To advance and characterize thermal MWA for focal cancer treatment, an in vivo method and experimental dataset were created for assessment of biophysical models designed to dynamically predict ablation zone parameters, given the delivery device, power, location, and proximity to vessels.

MATERIALS AND METHODS

MWA zone size, shape, and temperature were characterized and monitored in the absence of perfusion in ex vivo liver and a tissue-mimicking thermochromic phantom (TMTCP) at two power settings. Temperature was monitored over time using implanted thermocouples with their locations defined by CT. TMTCPs were used to identify the location of the ablation zone relative to the probe. In 6 swine, contrast-enhanced CTs were additionally acquired to visualize vasculature and absence of perfusion along with corresponding post-mortem gross pathology.

RESULTS

Bench studies demonstrated average ablation zone sizes of 4.13±1.56cm2 and 8.51±3.92cm2, solidity of 0.96±0.06 and 0.99±0.01, ablations centered 3.75cm and 3.5cm proximal to the probe tip, and temperatures of 50 ºC at 14.5±13.4s and 2.5±2.1s for 40W and 90W ablations, respectively. In vivo imaging showed average volumes of 9.8±4.8cm3 and 33.2±28.4cm3 and 3D solidity of 0.87±0.02 and 0.75±0.15, and gross pathology showed a hemorrhagic halo area of 3.1±1.2cm2 and 9.1±3.0cm2 for 40W and 90W ablations, respectfully. Temperatures reached 50ºC at 19.5±9.2s and 13.0±8.3s for 40W and 90W ablations, respectively.

CONCLUSION

MWA results are challenging to predict and are more variable than manufacturer-provided and bench predictions due to vascular stasis, heat-induced tissue changes, and probe operating conditions. Accurate prediction of MWA zones and temperature in vivo requires comprehensive thermal validation sets.

Locoregional Thermal and Chemical Tumor Ablation: Review of Clinical Applications and Potential Opportunities for Use in Low- and Middle-Income Countries

This review highlights opportunities to develop accessible ablative therapies to reduce the cancer burden in LMICs.

C-DARL: Contrastive diffusion adversarial representation learning for label-free blood vessel segmentation

Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine. Unfortunately, manual annotation of the vessel masks is challenging and resource-intensive due to subtle branches and complex structures. To overcome this issue, this paper presents a self-supervised vessel segmentation method, dubbed the contrastive diffusion adversarial representation learning (C-DARL) model. Our model is composed of a diffusion module and a generation module that learns the distribution of multi-domain blood vessel data by generating synthetic vessel images from diffusion latent. Moreover, we employ contrastive learning through a mask-based contrastive loss so that the model can learn more realistic vessel representations. To validate the efficacy, C-DARL is trained using various vessel datasets, including coronary angiograms, abdominal digital subtraction angiograms, and retinal imaging. Experimental results confirm that our model achieves performance improvement over baseline methods with noise robustness, suggesting the effectiveness of C-DARL for vessel segmentation.

Prospective Evaluation of PI-RADS Version 2.1 for Prostate Cancer Detection and Investigation of Multiparametric MRI-derived Markers

Background Data regarding the prospective performance of Prostate Imaging Reporting and Data System (PI-RADS) version 2.1 alone and in combination with quantitative MRI features for prostate cancer detection is limited. Purpose To assess lesion-based clinically significant prostate cancer (csPCa) rates in different PI-RADS version 2.1 categories and to identify MRI features that could improve csPCa detection. Materials and Methods This single-center prospective study included men with suspected or known prostate cancer who underwent multiparametric MRI and MRI/US-guided biopsy from April 2019 to December 2021. MRI scans were prospectively evaluated using PI-RADS version 2.1. Atypical transition zone (TZ) nodules were upgraded to category 3 if marked diffusion restriction was present. Lesions with an International Society of Urological Pathology (ISUP) grade of 2 or higher (range, 1-5) were considered csPCa. MRI features, including three-dimensional diameter, relative lesion volume (lesion volume divided by prostate volume), sphericity, and surface to volume ratio (SVR), were obtained from lesion contours delineated by the radiologist. Univariable and multivariable analyses were conducted at the lesion and participant levels to determine features associated with csPCa. Results In total, 454 men (median age, 67 years [IQR, 62-73 years]) with 838 lesions were included. The csPCa rates for lesions categorized as PI-RADS 1 (n = 3), 2 (n = 170), 3 (n = 197), 4 (n = 319), and 5 (n = 149) were 0%, 9%, 14%, 37%, and 77%, respectively. csPCa rates of PI-RADS 4 lesions were lower than PI-RADS 5 lesions (P < .001) but higher than PI-RADS 3 lesions (P < .001). Upgraded PI-RADS 3 TZ lesions were less likely to harbor csPCa compared with their nonupgraded counterparts (4% [one of 26] vs 20% [20 of 99], P = .02). Predictors of csPCa included relative lesion volume (odds ratio [OR], 1.6; P < .001), SVR (OR, 6.2; P = .02), and extraprostatic extension (EPE) scores of 2 (OR, 9.3; P < .001) and 3 (OR, 4.1; P = .02). Conclusion The rates of csPCa differed between consecutive PI-RADS categories of 3 and higher. MRI features, including lesion volume, shape, and EPE scores of 2 and 3, predicted csPCa. Upgrading of PI-RADS category 3 TZ lesions may result in unnecessary biopsies. ClinicalTrials.gov registration no. NCT03354416 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Goh in this issue.

Radiogenomic profiling of prostate tumors prior to external beam radiotherapy converges on a transcriptomic signature of TGF-β activity driving tumor recurrence

Background

Patients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies.

Methods

Twenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset.

Findings

Baseline MRI lesion volume and PTEN/TP53 status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort.

Interpretation

TGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria.

Funding

This research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

Imaging and Pathologic Evaluation of Cryoablation of Woodchuck (Marmota monax) Hepatocellular Carcinoma

We characterized cryoablation as a mode of clinical intervention in adult woodchucks with hepatocellular carcinoma (HCC). Woodchucks (n = 4) were infected with woodchuck hepatitis virus at birth and developed LI-RADS-5 hypervascular HCC. At 21 mo of age, they underwent ultrasound (US), contrast-enhanced CT (CECT) imaging, and US-guided subtotal cryoablation (IcePearl 2.1 CX, Galil, BTG) of their largest tumor (Mean HCC volume of 49 ± 9 cm³). Cryoablation was performed using two 10-min freeze cycles, each followed by an 8-min thaw cycle. The first woodchuck developed significant hemorrhage after the procedure and was euthanized. In the other 3 woodchucks, the probe track was cauterized and all 3 completed the study. Fourteen days after ablation, CECT was performed, and woodchucks were euthanized. Explanted tumors were sectioned using subject-specific, 3D-printed cutting molds. Initial tumor volume, the size of the cryoablation ice ball, gross pathology and hematoxylin and eosin-stained tissue sections were evaluated. On US, the edges of the solid ice balls were echogenic with dense acoustic shadowing and average dimensions of 3.1 ± 0.5 × 2.1 ± 0.4 cm and cross-sectional area of 4.7 ± 1.0 cm². On day 14 after cryoablation, CECT of the 3 woodchucks showed devascularized hypo-attenuating cryolesions with dimensions of 2.8 ± 0.3 × 2.6 ± 0.4 × 2.93 ± 0.7 cm and a cross sectional area of 5.8 ± 1.2 cm². Histopathologic evaluation showed hemorrhagic necrosis with a central amorphous region of coagulative necrosis surrounded by a rim of karyorrhectic debris. A rim of approximately 2.5 mm of coagulative necrosis and fibrous connective tissue clearly demarcated the cryolesion from adjacent HCC. Partial cryoablation of tumors produced coagulative necrosis with well-defined ablation margins at 14 d. Cauterization appeared to prevent hemorrhage after cryoablation of hypervascular tumors. Our findings indicate that woodchucks with HCC may provide a predictive preclinical model for investigating ablative modalities and developing new combination therapies.

HoloLens augmented reality system for transperineal free-hand prostate procedures

Purpose

An augmented reality (AR) system was developed to facilitate free-hand real-time needle guidance for transperineal prostate (TP) procedures and to overcome the limitations of a traditional guidance grid.

Approach

The HoloLens AR system enables the superimposition of annotated anatomy derived from preprocedural volumetric images onto a patient and addresses the most challenging part of free-hand TP procedures by providing real-time needle tip localization and needle depth visualization during insertion. The AR system accuracy, or the image overlay accuracy ( n = 56 ), and needle targeting accuracy ( n = 24 ) were evaluated within a 3D-printed phantom. Three operators each used a planned-path guidance method ( n = 4 ) and free-hand guidance ( n = 4 ) to guide needles into targets in a gel phantom. Placement error was recorded. The feasibility of the system was further evaluated by delivering soft tissue markers into tumors of an anthropomorphic pelvic phantom via the perineum.

Results

The image overlay error was 1.29 ± 0.57    mm , and needle targeting error was 2.13 ± 0.52    mm . The planned-path guidance placements showed similar error compared to the free-hand guidance ( 4.14 ± 1.08    mm versus 4.20 ± 1.08    mm , p = 0.90 ). The markers were successfully implanted either into or in close proximity to the target lesion.

Conclusions

The HoloLens AR system can provide accurate needle guidance for TP interventions. AR support for free-hand lesion targeting is feasible and may provide more flexibility than grid-based methods, due to the real-time 3D and immersive experience during free-hand TP procedures.

Predicting Outcomes of Indeterminate Bone Lesions on 18F-DCFPyL PSMA PET/CT Scans in the Setting of High-Risk Primary or Recurrent Prostate Cancer

Indeterminate bone lesions (IBLs) on prostate-specific membrane antigen (PSMA) PET/CT are common. This study aimed to define variables that predict whether such lesions are likely malignant or benign using features on PSMA PET/CT. Methods: ¹⁸F-DCFPyL PET/CT imaging was performed on 243 consecutive patients with high-risk primary or biochemically recurrent prostate cancer. IBLs identified on PSMA PET/CT could not definitively be interpreted as benign or malignant. Medical records of patients with IBLs were reviewed to determine the ultimate status of each lesion. IBLs were deemed malignant or benign on the basis of evidence of progression or stability at follow-up, respectively, or by biopsy results; IBLs were deemed equivocal when insufficient or unclear evidence existed. Post hoc patient, lesion, and scan variables accounting for clustered data were evaluated using Wilcoxon rank-sum and χ² tests to determine features that favored benign or malignant interpretation. Results: Overall, 98 IBLs within 267 bone lesions (36.7%) were identified in 48 of 243 patients (19.8%). Thirty-seven of 98 IBLs were deemed benign, and 42 were deemed malignant, of which 8 had histologic verification; 19 remained equivocal. Location and SUV_max categorical variables were predictive of IBL interpretation (P = 0.0201 and P = 0.0230, respectively). For IBLs with new interpretations, 34 of 37 (91.9%) considered benign showed an SUV_max of less than 5 or exhibited focal uptake without coexisting bone metastases; 37 of 42 (88.1%) deemed malignant demonstrated an SUV_max of at least 5 or were present with coexisting bone metastases. Logistic regression predicted IBLs with a high SUV_max (univariable: odds ratio [OR], 9.29 [P = 0.0016]; multivariable: OR, 13.87 [P = 0.0089]) or present with other bone metastases (univariable: OR, 9.87 [P = 0.0112]; multivariable: OR, 11.35 [P = 0.003]) to be malignant. Conclusion: IBLs on PSMA PET/CT are concerning; however, characterizing their location, SUV, and additional scan findings can aid interpretation. IBLs displaying an SUV_max of at least 5 or present with other bone metastases favor malignancy. IBLs without accompanying bone metastases that exhibit an SUV_max of less than 5 and are observed only in atypical locations favor benign processes. These guidelines may assist in the interpretation of IBLs on PSMA PET/CT.

Ultrasound Volume Reconstruction From Freehand Scans Without Tracking

Transrectal ultrasound is commonly used for guiding prostate cancer biopsy, where 3D ultrasound volume reconstruction is often desired. Current methods for 3D reconstruction from freehand ultrasound scans require external tracking devices to provide spatial information of an ultrasound transducer. This paper presents a novel deep learning approach for sensorless ultrasound volume reconstruction, which efficiently exploits content correspondence between ultrasound frames to reconstruct 3D volumes without external tracking. The underlying deep learning model, deep contextual-contrastive network (DC 2-Net), utilizes self-attention to focus on the speckle-rich areas to estimate spatial movement and then minimizes a margin ranking loss for contrastive feature learning. A case-wise correlation loss over the entire input video helps further smooth the estimated trajectory. We train and validate DC 2-Net on two independent datasets, one containing 619 transrectal scans and the other having 100 transperineal scans. Our proposed approach attained superior performance compared with other methods, with a drift rate of 9.64 % and a prostate Dice of 0.89. The promising results demonstrate the capability of deep neural networks for universal ultrasound volume reconstruction from freehand 2D ultrasound scans without tracking information.

Patient-specific needle guidance templates drilled intraprocedurally for image guided intervention: feasibility study in swine

PURPOSE

Thermal ablation of large tumors may benefit from simultaneous placement of multiple needles, but accurate placement becomes challenging as the number of needles increases. The aim of this work was to evaluate use of personalized needle guidance grid templates based on intraprocedural CT and fabricated at the point of care to implement ablation treatment plans with multiple needles in vivo.

METHODS

A plastic frame was designed to hold two parallel plastic guide plates in a rigid relationship, fixed over the abdomen by a mounting arm. Steel ball targets (1.5 mm) were implanted under ultrasound in the livers of two domestic swine under general anesthesia. Following breath-hold CT of the subject and frame, the targets and frame were identified using customized 3D Slicer-based planning software. Multiple needle trajectories targeting the balls were planned, including complex off-plane trajectories. A machining program for drilling the hole pattern corresponding to the planned needle trajectories was generated. The pattern was drilled in the two plates with a numerical-controlled milling machine in the suite. The plates were attached to the frame and needles passed through the paired holes to the calculated depth. Placement accuracy was defined as needle tip-to-target distance on post-placement CT.

RESULTS

The planning process and manufacturing required approximately 6 and 15 min, respectively. Needles were rapidly inserted (n = 11) to the target points without complications or traversing nontarget anatomy. The mean needle tip-to-target distance error was 3.4 ± 2.2, range 0-7 mm.

CONCLUSION

Rapid and accurate needle placement was feasible using a subject-specific, custom-drilled, needle guidance grid template fabricated intraprocedurally. Targeting accuracy and performance were similar to more complex and expensive tracking systems which may enable accurate intraprocedural implementation of treatment plans in the liver or other organs. This may be of value in complex ablation cases or in areas where more advanced guidance systems are not available.

Ipsilateral hemigland prostate biopsy may underestimate cancer burden in patients with unilateral mpMRI-visible lesions

PURPOSE

To evaluate the cancer detection rates of reduced-core biopsy schemes in patients with unilateral mpMRI-visible intraprostatic lesions and to analyze the contribution of systematic biopsy cores in clinically significant prostate cancer (csPCa) detection.

METHODS

212 patients with mpMRI-visible unilateral intraprostatic lesions undergoing MRI/TRUS fusion-guided targeted biopsy (TBx) and systematic biopsy (SBx) were included. Cancer detection rates of TBx + SBx, as determined by highest Gleason Grade Group (GG), were compared to 3 reduced-core biopsy schemes: TBx alone, TBx + ipsilateral systematic biopsy (IBx; MRI-positive hemigland), and TBx + contralateral systematic biopsy (CBx; MRI-negative hemigland). Patient-level and biopsy core-level data were analyzed using descriptive statistics with confidence intervals. Univariable and multivariable logistic regression analysis was conducted to identify predictors of csPCa (≥ GG2) detected in MRI-negative hemiglands at p < 0.05.

RESULTS

Overall, 43.4% (92/212) of patients had csPCa and 66.0% (140/212) of patients had any PCa detected by TBx + SBx. Of patients with csPCa, 81.5% had exclusively ipsilateral involvement (MRI-positive), 7.6% had only contralateral involvement (MRI-negative), and 10.9% had bilateral involvement. The csPCa detection rates of reduced-core biopsy schemes were 35.4% (75/212), 40.1% (85/212), and 39.6% (84/212) for TBx alone, TBx + IBx, and TBx + CBx, respectively, with detection sensitivities of 81.5%, 92.4%, and 91.3% compared to TBx + SBx.

CONCLUSION

Reduced-core prostate biopsy strategies confined to the ipsilateral hemigland underestimate csPCa burden by at least 8% in patients with unilateral mpMRI-visible intraprostatic lesions. The combined TBx + SBx strategy maximizes csPCa detection.

Phase I/II study of PexaVec in combination with immune checkpoint inhibition in refractory metastatic colorectal cancer

BACKGROUND

Oncolytic immunotherapy represents a unique therapeutic platform for the treatment of cancer. Here, we evaluated the safety and efficacy of the combination of pexastimogene devacirepvec (PexaVec) plus durvalumab (anti-programmed death ligand 1) with and without tremelimumab (anti-cytotoxic T-lymphocyte associated protein 4) in patients with standard chemotherapy refractory mismatch repair proficient (pMMR) metastatic colorectal cancer (mCRC) in a phase I/II trial.

METHODS

Adult patients with histologically confirmed advanced pMMR mCRC, who had progressed on at least two prior lines of systemic chemotherapy were studied in four cohorts. Patients received four doses of PexaVec IV at a dose of 3×10⁸ plaque forming units (pfu) (dose level 1) or 1×10⁹ pfu (dose level 2) every 2 weeks. Twelve days after the first PexaVec administration, patients received either 1500 mg of durvalumab every 28 days alone or an additional single dose of 300 mg tremelimumab on day 1. Responses were assessed every 8 weeks by CT or MRI. AEs were recorded. The primary endpoints were safety and feasibility. Secondary endpoints included progression-free survival (PFS) and overall survival. Paired tumor samples and peripheral blood were collected to perform immune monitoring.

RESULTS

Thirty-four patients with mCRC enrolled on to the study: 16 patients in the PexaVec/durvalumab cohorts and 18 patients in the PexaVec/durvalumab/tremelimumab cohorts. Overall, the combination of PexaVec plus immune checkpoint inhibitors did not result in any unexpected toxicities. Most common toxicities observed were fever and chills after PexaVec infusion. Two cases of grade 3 colitis, one case of a grade 2 myositis and one case of grade 3 hypotension resulted in discontinuation of immune checkpoint inhibitor and PexaVec treatment, respectively. The median PFS in the PexaVec/durvalumab/tremelimumab cohorts was 2.3 months (95% CI: 2.2 to 3.2 months) vs 2.1 months (95% CI: 1.7 to 2.8 months; p=0.57) in the PexaVec/durvalumab cohorts. Flow cytometry analysis of peripheral blood mononuclear cells revealed an increase in Ki67⁺CD8⁺ T cells on treatment.

CONCLUSION

PexaVec in combination with durvalumab and tremelimumab is safe and tolerable. No unexpected toxicities were observed. The combination of PexaVec/durvalumab/tremelimumab demonstrated potential clinical activity in patients with pMMR mCRC, but further studies are needed to identify the predictive biomarkers.

TRIAL REGISTRATION NUMBER

NCT03206073.

In vitro characterization of immune modulating drug-eluting immunobeads towards transarterial embolization in cancer

Hepatocellular carcinoma (HCC) is an aggressive liver cancer with limited effective treatment options. In this study, we selected TLR agonists imiquimod (IMQ), gardiquimod (GARD), GS-9620 and DSR 6434, and a small molecule checkpoint inhibitor, BMS-202, for characterization of drug loading and release from radiopaque embolic beads (DC Bead LUMI) for potential use in image-guided transarterial embolization (TACE) of HCC. The maximum drug loading capacity and amount of drug released over time were determined by high performance liquid chromatography and compared with the commonly used anthracycline, doxorubicin hydrochloride (Dox). Maximum drug loading was 204.54 ± 3.87, 65.28 ± 3.09, 65.95 ± 6.96, 65.97 ± 1.54, and 148.05 ± 2.24 mg of drug per milliliter of DC Bead LUMI for Dox, GARD, DSR 6434, IMQ, and BMS-202, respectively. Fast loading and subsequent rapid release in saline were observed for IMQ, GARD, and DSR 6434. These drugs could also be partially removed from the beads by repeated washing with de-ionized water suggesting weak interaction with the beads. Aggregation of IMQ was observed in water and saline. GS-9620 partially decomposed in the solubilizing solution, so loading and release were not characterized. Compared to TLR agonists, slower loading and release were observed for Dox and BMS-202. Potential factors influencing drug loading into and release from DC Bead LUMI including steric hinderance, hydrophobicity, drug pKa, and the electrostatic nature of the beads are discussed. The maximum loading capacity of BMS-202 and Dox in DC Bead LUMI exceeded the maximum theoretical loading capacity of the beads expected from ionic interaction alone suggesting additional drug-bead or drug-drug interactions may play a role. Slightly more release was observed for BMS-202 at early time points followed by a slower release compared to Dox. Further study of these drug-bead combinations is warranted in search of new tools for locoregional delivery of immune-modulating agents for treatment of HCC via drug-eluting bead chemoembolization.

Cross-modal attention for multi-modal image registration

In the past few years, convolutional neural networks (CNNs) have been proven powerful in extracting image features crucial for medical image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are limited in their ability to understand the spatial correspondence between features, which is at the core of image registration. The issue is further exaggerated when it comes to multi-modal image registration, where the appearances of input images can differ significantly. This paper presents a novel cross-modal attention mechanism for correlating features extracted from the multi-modal input images and mapping such correlation to image registration transformation. To efficiently train the developed network, a contrastive learning-based pre-training method is also proposed to aid the network in extracting high-level features across the input modalities for the following cross-modal attention learning. We validated the proposed method on transrectal ultrasound (TRUS) to magnetic resonance (MR) registration, a clinically important procedure that benefits prostate cancer biopsy. Our experimental results demonstrate that for MR-TRUS registration, a deep neural network embedded with the cross-modal attention block outperforms other advanced CNN-based networks with ten times its size. We also incorporated visualization techniques to improve the interpretability of our network, which helps bring insights into the deep learning based image registration methods. The source code of our work is available at https://github.com/DIAL-RPI/Attention-Reg.

In-line miniature 3D-printed pressure-cycled ventilator maintains respiratory homeostasis in swine with induced acute pulmonary injury

The COVID-19 pandemic demonstrated the need for inexpensive, easy-to-use, rapidly mass-produced resuscitation devices that could be quickly distributed in areas of critical need. In-line miniature ventilators based on principles of fluidics ventilate patients by automatically oscillating between forced inspiration and assisted expiration as airway pressure changes, requiring only a continuous supply of pressurized oxygen. Here, we designed three miniature ventilator models to operate in specific pressure ranges along a continuum of clinical lung injury (mild, moderate, and severe injury). Three-dimensional (3D)-printed prototype devices evaluated in a lung simulator generated airway pressures, tidal volumes, and minute ventilation within the targeted range for the state of lung disease each was designed to support. In testing in domestic swine before and after induction of pulmonary injury, the ventilators for mild and moderate injury met the design criteria when matched with the appropriate degree of lung injury. Although the ventilator for severe injury provided the specified design pressures, respiratory rate was elevated with reduced minute ventilation, a result of lung compliance below design parameters. Respiratory rate reflected how well each ventilator matched the injury state of the lungs and could guide selection of ventilator models in clinical use. This simple device could help mitigate shortages of conventional ventilators during pandemics and other disasters requiring rapid access to advanced airway management, or in transport applications for hands-free ventilation.

Development of a 3D CNN-based AI Model for Automated Segmentation of the Prostatic Urethra

RATIONALE AND OBJECTIVE

The combined use of prostate cancer radiotherapy and MRI planning is increasingly being used in the treatment of clinically significant prostate cancers. The radiotherapy dosage quantity is limited by toxicity in organs with de-novo genitourinary toxicity occurrence remaining unperturbed. Estimation of the urethral radiation dose via anatomical contouring may improve our understanding of genitourinary toxicity and its related symptoms. Yet, urethral delineation remains an expert-dependent and time-consuming procedure. In this study, we aim to develop a fully automated segmentation tool for the prostatic urethra.

MATERIALS AND METHODS

This study incorporated 939 patients' T2-weighted MRI scans (train/validation/test/excluded: 657/141/140/1 patients), including in-house and public PROSTATE-x datasets, and their corresponding ground truth urethral contours from an expert genitourinary radiologist. The AI model was developed using MONAI framework and was based on a 3D-UNet. AI model performance was determined by Dice score (volume-based) and the Centerline Distance (CLD) between the prediction and ground truth centers (slice-based). All predictions were compared to ground truth in a systematic failure analysis to elucidate the model's strengths and weaknesses. The Wilcoxon-rank sum test was used for pair-wise comparison of group differences.

RESULTS

The overall organ-adjusted Dice score for this model was 0.61 and overall CLD was 2.56 mm. When comparing prostates with symmetrical (n = 117) and asymmetrical (n = 23) benign prostate hyperplasia (BPH), the AI model performed better on symmetrical prostates compared to asymmetrical in both Dice score (0.64 vs. 0.51 respectively, p < 0.05) and mean CLD (2.3 mm vs. 3.8 mm respectively, p < 0.05). When calculating location-specific performance, the performance was highest at the apex and lowest at the base location of the prostate for Dice and CLD. Dice location dependence: symmetrical (Apex, Mid, Base: 0.69 vs. 0.67 vs. 0.54 respectively, p < 0.05) and asymmetrical (Apex, Mid, Base: 0.68 vs. 0.52 vs. 0.39 respectively, p < 0.05). CLD location dependence: symmetrical (Apex, Mid, Base: 1.43 mm vs. 2.15 mm vs. 3.28 mm, p < 0.05) and asymmetrical (Apex, Mid, Base: 1.83 mm vs. 3.1 mm vs. 6.24 mm, p < 0.05).

CONCLUSION

We developed a fully automated prostatic urethra segmentation AI tool yielding its best performance in prostate glands with symmetric BPH features. This system can potentially be used to assist treatment planning in patients who can undergo whole gland radiation therapy or ablative focal therapy.

Imaging and biopsy of HIV-infected individuals undergoing analytic treatment interruption

Background

HIV persistence during antiretroviral therapy (ART) is the principal obstacle to cure. Lymphoid tissue is a compartment for HIV, but mechanisms of persistence during ART and viral rebound when ART is interrupted are inadequately understood. Metabolic activity in lymphoid tissue of patients on long-term ART is relatively low, and increases when ART is stopped. Increases in metabolic activity can be detected by ¹⁸F-fluorodeoxyglucose Positron Emission Tomography (FDG-PET) and may represent sites of HIV replication or immune activation in response to HIV replication.

Methods

FDG-PET imaging will be used to identify areas of high and low metabolic uptake in lymphoid tissue of individuals undergoing long-term ART. Baseline tissue samples will be collected. Participants will then be randomized 1:1 to continue or interrupt ART via analytic treatment interruption (ATI). Image-guided biopsy will be repeated 10 days after ATI initiation. After ART restart criteria are met, image-guided biopsy will be repeated once viral suppression is re-achieved. Participants who continued ART will have a second FDG-PET and biopsies 12–16 weeks after the first. Genetic characteristics of HIV populations in areas of high and low FDG uptake will be assesed. Optional assessments of non-lymphoid anatomic compartments may be performed to evaluate HIV populations in distinct anatomic compartments.

Anticipated results

We anticipate that PET standardized uptake values (SUV) will correlate with HIV viral RNA in biopsies of those regions and that lymph nodes with high SUV will have more viral RNA than those with low SUV within a patient. Individuals who undergo ATI are expected to have diverse viral populations upon viral rebound in lymphoid tissue. HIV populations in tissues may initially be phylogenetically diverse after ATI, with emergence of dominant viral species (clone) over time in plasma. Dominant viral species may represent the same HIV population seen before ATI.

Discussion

This study will allow us to explore utility of PET for identification of HIV infected cells and determine whether high FDG uptake respresents areas of HIV replication, immune activation or both. We will also characterize HIV infected cell populations in different anatomic locations. The protocol will represent a platform to investigate persistence and agents that may target HIV populations.

Study protocol registration

Identifier: NCT05419024.

Predictors of 18F-DCFPyL PET/CT Positivity in Patients with Biochemical Recurrence of Prostate Cancer After Local Therapy

Our objective was to investigate the factors predicting scan positivity and disease location in patients with biochemical recurrence (BCR) of prostate cancer (PCa) after primary local therapy using prostate-specific membrane antigen-targeted ¹⁸F-DCFPyL PET/CT. Methods: This was a 2-institution study including 245 BCR PCa patients after primary local therapy and negative results on conventional imaging. The patients underwent ¹⁸F-DCFPyL PET/CT. We tested for correlations of lesion detection rate and disease location with tumor characteristics, time from initial therapy, prostate-specific antigen (PSA) level, and PSA doubling time (PSAdt). Multivariate logistic regression analyses were used to determine predictors of a positive scan. Regression-based coefficients were used to develop nomograms predicting scan positivity and extrapelvic disease. Results: Overall, 79.2% (194/245) of patients had a positive ¹⁸F-DCFPyL PET/CT result, with detection rates of 48.2% (27/56), 74.3% (26/35), 84% (37/44), 96.7% (59/61), and 91.8% (45/49) for PSAs of <0.5, 0.5 to <1.0, 1.0 to <2.0, 2.0 to <5.0, and ≥5.0 ng/mL, respectively. Patients with lesions confined to the pelvis had lower PSAs than those with distant sites (1.6 ± 3.5 vs. 3.0 ± 6.3 ng/mL, P < 0.001). In patients treated with prostatectomy (n = 195), 24.1% (47/195) had a negative scan result, 46.1% (90/195) showed intrapelvic disease, and 29.7% (58/195) showed extrapelvic disease. In the postradiation subgroup (n = 50), ¹⁸F-DCFPyL PET/CT was always negative at a PSA lower than 1.0 ng/mL and extrapelvic disease was seen only when PSA was greater than 2.0 ng/mL. At multivariate analysis, PSA and PSAdt were independent predictive factors of scan positivity and the presence of extrapelvic disease in postsurgical patients, with area under the curve of 78% and 76%, respectively. PSA and PSAdt were independent predictors of the presence of extrapelvic disease in the postradiation cohort, with area under the curve of 85%. Time from treatment to scan was significantly longer for prostatectomy-bed-only recurrences than for those with bone or visceral disease (6.2 ± 6.4 vs. 2.4 ± 1.3 y, P < 0.001). Conclusion:¹⁸F-DCFPyL PET/CT offers high detection rates in BCR PCa patients. PSA and PSAdt are able to predict scan positivity and disease location. Furthermore, the presence of bone or visceral lesions is associated with shorter intervals from treatment than are prostate-bed-only recurrences. These tools might guide clinicians to select the most suitable candidates for ¹⁸F-DCFPyL PET/CT imaging.

A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging

RATIONALE AND OBJECTIVES

Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and classification of prostatic lesions. Herein, we aimed to develop and test a cascaded deep learning detection and classification system trained on biparametric prostate MRI using PI-RADS for assisting radiologists during prostate MRI read out.

MATERIALS AND METHODS

T2-weighted, diffusion-weighted (ADC maps, high b value DWI) MRI scans obtained at 3 Tesla from two institutions (n = 1043 in-house and n = 347 Prostate-X, respectively) acquired between 2015 to 2019 were used for model training, validation, testing. All scans were retrospectively reevaluated by one radiologist. Suspicious lesions were contoured and assigned a PI-RADS category. A 3D U-Net-based deep neural network was used to train an algorithm for automated detection and segmentation of prostate MRI lesions. Two 3D residual neural network were used for a 4-class classification task to predict PI-RADS categories 2 to 5 and BPH. Training and validation used 89% (n = 1290 scans) of the data using 5 fold cross-validation, the remaining 11% (n = 150 scans) were used for independent testing. Algorithm performance at lesion level was assessed using sensitivities, positive predictive values (PPV), false discovery rates (FDR), classification accuracy, Dice similarity coefficient (DSC). Additional analysis was conducted to compare AI algorithm's lesion detection performance with targeted biopsy results.

RESULTS

Median age was 66 years (IQR = 60-71), PSA 6.7 ng/ml (IQR = 4.7-9.9) from in-house cohort. In the independent test set, algorithm correctly detected 111 of 198 lesions leading to 56.1% (49.3%-62.6%) sensitivity. PPV was 62.7% (95% CI 54.7%-70.7%) with FDR of 37.3% (95% CI 29.3%-45.3%). Of 79 true positive lesions, 82.3% were tumor positive at targeted biopsy, whereas of 57 false negative lesions, 50.9% were benign at targeted biopsy. Median DSC for lesion segmentation was 0.359. Overall PI-RADS classification accuracy was 30.8% (95% CI 24.6%-37.8%).

CONCLUSION

Our cascaded U-Net, residual network architecture can detect, classify cancer suspicious lesions at prostate MRI with good detection, reasonable classification performance metrics.

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation

Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL.

Detection of failure patterns using advanced imaging in patients with biochemical recurrence following low-dose-rate brachytherapy for prostate cancer

PURPOSE/OBJECTIVE(S)

This study describes the pattern of failure in patients with biochemical (BCR) recurrence after low-dose-rate (LDR) brachytherapy as a component of definitive treatment for prostate cancer.

METHODS

Patients with BCR after LDR brachytherapy ± external beam radiation therapy (EBRT) were enrolled on prospective IRB approved advanced imaging protocols. Patients underwent 3T multiparametric MRI (mpMRI); a subset underwent prostate specific membrane antigen (PSMA)-based PET/CT. Pathologic confirmation was obtained unless contraindicated.

RESULTS

Between January 2011 and April 2021, 51 patients with BCR after brachytherapy (n = 36) or brachytherapy + EBRT (n = 15) underwent mpMRI and were included in this analysis. Of 38 patients with available dosimetry, only two had D90<90%. The prostate and seminal vesicles were a site of failure in 66.7% (n = 34) and 39.2% (n = 20), respectively. PET/CT (n = 32 patients) more often identified lesions pelvic lymph nodes (50%; n = 16) and distant metastases (18.8%; n = 6), than mpMRI. Isolated nodal disease (9.8%; n = 5) and distant metastases (n = 1) without local recurrence were uncommon. Recurrence within the prostate was located in the transition zone in 48.5%, central or midline in 45.5%, and anterior in 36.4% of patients.

CONCLUSION

In this cohort of patients with BCR after LDR brachytherapy ± EBRT, the predominant recurrence pattern was local (prostate ± seminal vesicles) with frequent occurrence in the anterior prostate and transition zone. mpMRI and PSMA PET/CT provided complementary information to localize sites of recurrence, with PSMA PET/CT often confirming mpMRI findings and identifying occult nodal or distant metastases.