Eye tracking in digital pathology: A comprehensive literature review.

Eye tracking has been used for decades in attempt to understand the cognitive processes of individuals. From memory access to problem-solving to decision-making, such insight has the potential to improve workflows and the education of students to become experts in relevant fields. Until recently, the traditional use of microscopes in pathology made eye tracking exceptionally difficult. However, the digital revolution of pathology from conventional microscopes to digital whole slide images allows for new research to be conducted and information to be learned with regards to pathologist visual search patterns and learning experiences. This has the promise to make pathology education more efficient and engaging, ultimately creating stronger and more proficient generations of pathologists to come. The goal of this review on eye tracking in pathology is to characterize and compare the visual search patterns of pathologists. The PubMed and Web of Science databases were searched using 'pathology' AND 'eye tracking' synonyms. A total of 22 relevant full-text articles published up to and including 2023 were identified and included in this review. Thematic analysis was conducted to organize each study into one or more of the 10 themes identified to characterize the visual search patterns of pathologists: (1) effect of experience, (2) fixations, (3) zooming, (4) panning, (5) saccades, (6) pupil diameter, (7) interpretation time, (8) strategies, (9) machine learning, and (10) education. Expert pathologists were found to have higher diagnostic accuracy, fewer fixations, and shorter interpretation times than pathologists with less experience. Further, literature on eye tracking in pathology indicates that there are several visual strategies for diagnostic interpretation of digital pathology images, but no evidence of a superior strategy exists. The educational implications of eye tracking in pathology have also been explored but the effect of teaching novices how to search as an expert remains unclear. In this article, the main challenges and prospects of eye tracking in pathology are briefly discussed along with their implications to the field.

Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics.

Stereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion's longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.

A Visual Aid Tool for Detection of Pancreatic Tumour-Vessel Contact on Staging CT: A Retrospective Cohort Study.

Purpose: In pancreatic adenocarcinoma, the difficult distinction between normal and affected pancreas on CT studies may lead to discordance between the pre-surgical assessment of vessel involvement and intraoperative findings. We hypothesize that a visual aid tool could improve the performance of radiology residents when detecting vascular invasion in pancreatic adenocarcinoma patients. Methods: This study consisted of 94 pancreatic adenocarcinoma patient CTs. The visual aid compared the estimated body fat density of each patient with the densities surrounding the superior mesenteric artery and mapped them onto the CT scan. Four radiology residents annotated the locations of perceived vascular invasion on each scan with the visual aid overlaid on alternating scans. Using 3 expert radiologists as the reference standard, we quantified the area under the receiver operating characteristic curve to determine the performance of the tool. We then used sensitivity, specificity, balanced accuracy ((sensitivity + specificity)/2), and spatial metrics to determine the performance of the residents with and without the tool. Results: The mean area under the curve was 0.80. Radiology residents' sensitivity/specificity/balanced accuracy for predicting vascular invasion were 50%/85%/68% without the tool and 81%/79%/80% with it compared to expert radiologists, and 58%/85%/72% without the tool and 78%/77%/77% with it compared to the surgical pathology. The tool was not found to impact the spatial metrics calculated on the resident annotations of vascular invasion. Conclusion: The improvements provided by the visual aid were predominantly reflected by increased sensitivity and accuracy, indicating the potential of this tool as a learning aid for trainees.

Predicting stereotactic radiosurgery outcomes with multi-observer qualitative appearance labelling versus MRI radiomics.

Qualitative observer-based and quantitative radiomics-based analyses of T1w contrast-enhanced magnetic resonance imaging (T1w-CE MRI) have both been shown to predict the outcomes of brain metastasis (BM) stereotactic radiosurgery (SRS). Comparison of these methods and interpretation of radiomics-based machine learning (ML) models remains limited. To address this need, we collected a dataset of n = 123 BMs from 99 patients including 12 clinical features, 107 pre-treatment T1w-CE MRI radiomic features, and BM post-SRS progression scores. A previously published outcome model using SRS dose prescription and five-way BM qualitative appearance scoring was evaluated. We found high qualitative scoring interobserver variability across five observers that negatively impacted the model's risk stratification. Radiomics-based ML models trained to replicate the qualitative scoring did so with high accuracy (bootstrap-corrected AUC = 0.84-0.94), but risk stratification using these replicated qualitative scores remained poor. Radiomics-based ML models trained to directly predict post-SRS progression offered enhanced risk stratification (Kaplan-Meier rank-sum p = 0.0003) compared to using qualitative appearance. The qualitative appearance scoring enabled interpretation of the progression radiomics-based ML model, with necrotic BMs and a subset of heterogeneous BMs predicted as being at high-risk of post-SRS progression, in agreement with current radiobiological understanding. Our study's results show that while radiomics-based SRS outcome models out-perform qualitative appearance analysis, qualitative appearance still provides critical insight into ML model operation.

An oral glucose tolerance test does not affect cerebral blood flow: role of NOS.

Animal data indicate that insulin triggers a robust nitric oxide synthase (NOS)-mediated dilation in cerebral arteries similar to the peripheral tissue vasodilation observed in healthy adults. Insulin's role in regulating cerebral blood flow (CBF) in humans remains unclear but may be important for understanding the links between insulin resistance, diminished CBF, and poor brain health outcomes. We tested the hypothesis that an oral glucose challenge (oral glucose tolerance test, OGTT), which increases systemic insulin and glucose, would acutely increase CBF in healthy adults due to NOS-mediated vasodilation, and that changes in CBF would be greater in anterior regions where NOS expression or activity may be greater. In a randomized, single-blind approach, 18 young healthy adults (24 ± 5 yr) underwent magnetic resonance imaging (MRI) with a placebo before and after an OGTT (75 g glucose), and 11 of these adults also completed an NG-monomethyl-l-arginine (l-NMMA) visit. Four-dimensional (4-D) flow MRI quantified macrovascular CBF and arterial spin labeling (ASL) quantified microvascular perfusion. Subjects completed baseline imaging with a placebo (or l-NMMA), then consumed an OGTT followed by MRI scans and blood sampling every 10-15 min for 90 min. Contrary to our hypothesis, total CBF (P = 0.17) and global perfusion (P > 0.05) did not change at any time point up to 60 min after the OGTT, and no regional changes were detected. l-NMMA did not mediate any effect of OGTT on CBF. These data suggest that insulin-glucose challenge does not acutely alter CBF in healthy adults.

Regulation of Skeletal Muscle Resistance Arteriolar Tone: Integration of Multiple Mechanisms.

INTRODUCTION: Physiological system complexity represents an imposing challenge to gaining insight into how arteriolar behavior emerges. Further, mechanistic complexity in arteriolar tone regulation requires that a systematic determination of how these processes interact to alter vascular diameter be undertaken. METHODS: The present study evaluated the reactivity of ex vivo proximal and in situ distal resistance arterioles in skeletal muscle with challenges across the full range of multiple physiologically relevant stimuli and determined the stability of responses over progressive alterations to each other parameter. The five parameters chosen for examination were (1) metabolism (adenosine concentration), (2) adrenergic activation (norepinephrine concentration), (3) myogenic activation (intravascular pressure), (4) oxygen (superfusate PO2), and (5) wall shear rate (altered intraluminal flow). Vasomotor tone of both arteriole groups following challenge with individual parameters was determined; subsequently, responses were determined following all two- and three-parameter combinations to gain deeper insight into how stimuli integrate to change arteriolar tone. A hierarchical ranking of stimulus significance for establishing arteriolar tone was performed using mathematical and statistical analyses in conjunction with machine learning methods. RESULTS: Results were consistent across methods and indicated that metabolic and adrenergic influences were most robust and stable across all conditions. While the other parameters individually impact arteriolar tone, their impact can be readily overridden by the two dominant contributors. CONCLUSION: These data suggest that mechanisms regulating arteriolar tone are strongly affected by acute changes to the local environment and that ongoing investigation into how microvessels integrate stimuli regulating tone will provide a more thorough understanding of arteriolar behavior emergence across physiological and pathological states.

Dual-center validation of using magnetic resonance imaging radiomics to predict stereotactic radiosurgery outcomes.

Background: MRI radiomic features and machine learning have been used to predict brain metastasis (BM) stereotactic radiosurgery (SRS) outcomes. Previous studies used only single-center datasets, representing a significant barrier to clinical translation and further research. This study, therefore, presents the first dual-center validation of these techniques. Methods: SRS datasets were acquired from 2 centers (n = 123 BMs and n = 117 BMs). Each dataset contained 8 clinical features, 107 pretreatment T1w contrast-enhanced MRI radiomic features, and post-SRS BM progression endpoints determined from follow-up MRI. Random decision forest models were used with clinical and/or radiomic features to predict progression. 250 bootstrap repetitions were used for single-center experiments. Results: Training a model with one center's dataset and testing it with the other center's dataset required using a set of features important for outcome prediction at both centers, and achieved area under the receiver operating characteristic curve (AUC) values up to 0.70. A model training methodology developed using the first center's dataset was locked and externally validated with the second center's dataset, achieving a bootstrap-corrected AUC of 0.80. Lastly, models trained on pooled data from both centers offered balanced accuracy across centers with an overall bootstrap-corrected AUC of 0.78. Conclusions: Using the presented validated methodology, radiomic models trained at a single center can be used externally, though they must utilize features important across all centers. These models' accuracies are inferior to those of models trained using each individual center's data. Pooling data across centers shows accurate and balanced performance, though further validation is required.

Reduced basal macrovascular and microvascular cerebral blood flow in young adults with metabolic syndrome: potential mechanisms.

Ninety-million Americans suffer metabolic syndrome (MetSyn), increasing the risk of diabetes and poor brain outcomes, including neuropathology linked to lower cerebral blood flow (CBF), predominantly in anterior regions. We tested the hypothesis that total and regional CBF is lower in MetSyn more so in the anterior brain and explored three potential mechanisms. Thirty-four controls (25 ± 5 yr) and 19 MetSyn (30 ± 9 yr), with no history of cardiovascular disease/medications, underwent four-dimensional flow magnetic resonance imaging (MRI) to quantify macrovascular CBF, whereas arterial spin labeling quantified brain perfusion in a subset (n = 38/53). Contributions of cyclooxygenase (COX; n = 14), nitric oxide synthase (NOS, n = 17), or endothelin receptor A signaling (n = 13) were tested with indomethacin, NG-monomethyl-L-arginine (L-NMMA), and Ambrisentan, respectively. Total CBF was 20 ± 16% lower in MetSyn (725 ± 116 vs. 582 ± 119 mL/min, P < 0.001). Anterior and posterior brain regions were 17 ± 18% and 30 ± 24% lower in MetSyn; reductions were not different between regions (P = 0.112). Global perfusion was 16 ± 14% lower in MetSyn (44 ± 7 vs. 36 ± 5 mL/100 g/min, P = 0.002) and regionally in frontal, occipital, parietal, and temporal lobes (range 15-22%). The decrease in CBF with L-NMMA (P = 0.004) was not different between groups (P = 0.244, n = 14, 3), and Ambrisentan had no effect on either group (P = 0.165, n = 9, 4). Interestingly, indomethacin reduced CBF more in Controls in the anterior brain (P = 0.041), but CBF decrease in posterior was not different between groups (P = 0.151, n = 8, 6). These data indicate that adults with MetSyn exhibit substantially reduced brain perfusion without regional differences. Moreover, this reduction is not due to loss of NOS or gain of ET-1 signaling but rather a loss of COX vasodilation.NEW & NOTEWORTHY We tested the impact of insulin resistance (IR) on resting cerebral blood flow (CBF) in adults with metabolic syndrome (MetSyn). Using MRI and research pharmaceuticals to study the role of NOS, ET-1, or COX signaling, we found that adults with MetSyn exhibit substantially lower CBF that is not explained by changes in NOS or ET-1 signaling. Interestingly, adults with MetSyn show a loss of COX-mediated vasodilation in the anterior but not posterior circulation.

Using deep learning to predict tumor mutational burden from scans of H&E-stained multicenter slides of lung squamous cell carcinoma.

Purpose: A high tumor mutational burden (TMB) is a promising biomarker for identifying lung squamous cell carcinoma (SqCC) patients who are more likely to benefit from risky but potentially highly beneficial immunotherapy, but it is not available in most clinics. It has been shown that it is possible to predict TMB from standard-of-care cancer histology slides using deep learning for various cancer sites. Our goal is to build a model that can do this specifically for lung SqCC and to validate it on a held-out test set from centers on which the model was not trained. Approach: We obtained scans of diagnostic slides from 50 lung SqCC patients, with one slide per-patient, from 35 different centers. We held out 20 slides from 15 centers for testing and used the rest for training and validation, ensuring that no center was represented in more than one set. Using transfer learning, we explored several neural network architectures and training parameters to choose an optimal model. Results: Using the training and validation sets, we found the optimal model to be VGG16. The per-patient AUC for this model on the held-out test set was 0.65, with an accuracy of 65%, true positive rate of 77%, and true negative rate of 43%. Conclusions: A deep learning model can predict TMB from scans of H&E-stained slides of lung SqCC resections on an independent test set containing images only from centers on which the model was not trained. With further development and external validation, such a system can act as an alternative to traditional genetic sequencing for patients with SqCC; this will help physicians determine, with more accuracy, whether patients should be given immunotherapy. This will more effectively give access to immunotherapy drugs to those who need them and help spare others the toxicities associated with them.

Application of a novel index for understanding vascular health following pharmacological intervention in a pre-clinical model of metabolic disease.

While a thorough understanding of microvascular function in health and how it becomes compromised with progression of disease risk is critical for developing effective therapeutic interventions, our ability to accurately assess the beneficial impact of pharmacological interventions to improve outcomes is vital. Here we introduce a novel Vascular Health Index (VHI) that allows for simultaneous assessment of changes to vascular reactivity/endothelial function, vascular wall mechanics and microvessel density within cerebral and skeletal muscle vascular networks with progression of metabolic disease in obese Zucker rats (OZR); under control conditions and following pharmacological interventions of clinical relevance. Outcomes are compared to "healthy" conditions in lean Zucker rats. We detail the calculation of vascular health index, full assessments of validity, and describe progressive changes to vascular health index over the development of metabolic disease in obese Zucker rats. Further, we detail the improvement to cerebral and skeletal muscle vascular health index following chronic treatment of obese Zucker rats with anti-hypertensive (15%-52% for skeletal muscle vascular health index; 12%-48% for cerebral vascular health index; p < 0.05 for both), anti-dyslipidemic (13%-48% for skeletal muscle vascular health index; p < 0.05), anti-diabetic (12%-32% for cerebral vascular health index; p < 0.05) and anti-oxidant/inflammation (41%-64% for skeletal muscle vascular health index; 29%-42% for cerebral vascular health index; p < 0.05 for both) drugs. The results present the effectiveness of mechanistically diverse interventions to improve cerebral or skeletal muscle vascular health index in obese Zucker rats and provide insight into the superiority of some pharmacological agents despite similar effectiveness in terms of impact on intended targets. In addition, we demonstrate the utility of including a wider, more integrative approach to the study of microvasculopathy under settings of elevated disease risk and following pharmacological intervention. A major benefit of integrating vascular health index is an increased understanding of the development, timing and efficacy of interventions through greater insight into integrated microvascular function in combination with individual, higher resolution metrics.

Performance sensitivity analysis of brain metastasis stereotactic radiosurgery outcome prediction using MRI radiomics.

Recent studies have used T1w contrast-enhanced (T1w-CE) magnetic resonance imaging (MRI) radiomic features and machine learning to predict post-stereotactic radiosurgery (SRS) brain metastasis (BM) progression, but have not examined the effects of combining clinical and radiomic features, BM primary cancer, BM volume effects, and using multiple scanner models. To investigate these effects, a dataset of n = 123 BMs from 99 SRS patients with 12 clinical features, 107 pre-treatment T1w-CE radiomic features, and BM progression determined by follow-up MRI was used with a random decision forest model and 250 bootstrapped repetitions. Repeat experiments assessed the relative accuracy across primary cancer sites, BM volume groups, and scanner model pairings. Correction for accuracy imbalances across volume groups was investigated by removing volume-correlated features. We found that using clinical and radiomic features together produced the most accurate model with a bootstrap-corrected area under the receiver operating characteristic curve of 0.77. Accuracy also varied by primary cancer site, BM volume, and scanner model pairings. The effect of BM volume was eliminated by removing features at a volume-correlation coefficient threshold of 0.25. These results show that feature type, primary cancer, volume, and scanner model are all critical factors in the accuracy of radiomics-based prognostic models for BM SRS that must be characterised and controlled for before clinical translation.

A novel vascular health index: Using data analytics and population health to facilitate mechanistic modeling of microvascular status.

The study of vascular function across conditions has been an intensive area of investigation for many years. While these efforts have revealed many factors contributing to vascular health, challenges remain for integrating results across research groups, animal models, and experimental conditions to understand integrated vascular function. As such, the insights attained in clinical/population research from linking datasets, have not been fully realized in the basic sciences, thus frustrating advanced analytics and complex modeling. To achieve comparable advances, we must address the conceptual challenge of defining/measuring integrated vascular function and the technical challenge of combining data across conditions, models, and groups. Here, we describe an approach to establish and validate a composite metric of vascular function by comparing parameters of vascular function in metabolic disease (the obese Zucker rat) to the same parameters in age-matched, "healthy" conditions, resulting in a common outcome measure which we term the vascular health index (VHI). VHI allows for the integration of datasets, thus expanding sample size and permitting advanced modeling to gain insight into the development of peripheral and cerebral vascular dysfunction. Markers of vascular reactivity, vascular wall mechanics, and microvascular network density are integrated in the VHI. We provide a detailed presentation of the development of the VHI and provide multiple measures to assess face, content, criterion, and discriminant validity of the metric. Our results demonstrate how the VHI captures multiple indices of dysfunction in the skeletal muscle and cerebral vasculature with metabolic disease and provide context for an integrated understanding of vascular health under challenged conditions.

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

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

Arteriolar dysgenesis in ischemic-regenerating skeletal muscle revealed by automated micromorphometry, computational modeling, and perfusion analysis.

Rebuilding the local vasculature is central to restoring the health of muscles subjected to ischemic injury. Arteriogenesis yields remodeled collateral arteries that circumvent the obstruction, and angiogenesis produces capillaries to perfuse the regenerating myofibers. However, the vital intervening network of arterioles that feed the regenerated capillaries is poorly understood and is an investigative challenge. We used machine learning and automated micromorphometry to quantify the arteriolar landscape in distal hindlimb muscles in mice that have regenerated after femoral artery excision. Assessment of 1,546 arteriolar sections revealed a striking (>2-fold) increase in arteriolar density in regenerated muscle 14 and 28 days after ischemic injury. Lumen caliber was initially similar to that of control arterioles but after 4 wk lumen area was reduced by 46%. In addition, the critical smooth muscle layer was attenuated throughout the arteriolar network, across a 150- to 5-µm diameter range. To understand the consequences of the reshaped distal hindlimb arterioles, we undertook computational flow modeling, which revealed blunted flow augmentation. Moreover, impaired flow reserve was confirmed in vivo by laser-Doppler analyses of flow in response to directly applied sodium nitroprusside. Thus, in hindlimb muscles regenerating after ischemic injury, the arteriolar network is amplified, inwardly remodels, and is diffusely undermuscularized. These defects and the associated flow restraints could contribute to the deleterious course of peripheral artery disease and merit attention when considering therapeutic innovations.NEW & NOTEWORTHY We report a digital pipeline for interrogating the landscape of arterioles in mouse skeletal muscle, using machine learning and automated micromorphometry. This revealed that in muscle regenerating after ischemic injury, the arteriolar density is increased but lumen caliber and smooth muscle content are reduced. Computational modeling and experimental validation reveal this arteriolar network to be functionally compromised, with diminished microvascular flow reserve.

Imaging Biomarkers in Prostate Stereotactic Body Radiotherapy: A Review and Clinical Trial Protocol.

Advances in imaging have changed prostate radiotherapy through improved biochemical control from focal boost and improved detection of recurrence. These advances are reviewed in the context of prostate stereotactic body radiation therapy (SBRT) and the ARGOS/CLIMBER trial protocol. ARGOS/CLIMBER will evaluate 1) the safety and feasibility of SBRT with focal boost guided by multiparametric MRI (mpMRI) and 18F-PSMA-1007 PET and 2) imaging and laboratory biomarkers for response to SBRT. To date, response to prostate SBRT is most commonly evaluated using the Phoenix Criteria for biochemical failure. The drawbacks of this approach include lack of lesion identification, a high false-positive rate, and delay in identifying treatment failure. Patients in ARGOS/CLIMBER will receive dynamic 18F-PSMA-1007 PET and mpMRI prior to SBRT for treatment planning and at 6 and 24 months after SBRT to assess response. Imaging findings will be correlated with prostate-specific antigen (PSA) and biopsy results, with the goal of early, non-invasive, and accurate identification of treatment failure.

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

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

Defining radio-recurrent intra-prostatic target volumes using PSMA-targeted PET/CT and multi-parametric MRI.

PURPOSE: Our purpose was to evaluate intra-prostatic cancer volumes for salvage radiotherapy in men with recurrent prostate cancer confined to the prostate post-primary radiotherapy using mpMRI and 18F-DCFPyL PET/CT (PET). METHODS: Men with biochemical failure post-primary radiotherapy were enrolled in a multi-centre trial investigating mpMRI and PET. All men with isolated intra-prostatic recurrence are included in this secondary analysis. The intra-prostatic gross tumour volume (GTV) was manually delineated on mpMRI and was also delineated on PET using three methods: 1. manually, 2. using a 30% threshold of maximum intra-prostatic standard uptake value (SUVmax), and 3. using a 67% threshold of this SUVmax. Clinical target volumes (CTV) including expansions on each GTV were generated. Conformity indices were performed between the mpMRI CTV and each PET CTV. Correlation with biopsy and clinical outcomes were performed. RESULTS: Of the 36 men included, 30 (83%) had disease in two quadrants or less using the combination of mpMRI and PET. Mean target volume (union of CTV on mpMRI and CTV manually delineated on PET) was 12.2 cc (49% of prostate gland volume). 12/36 (33%) men had a biopsy. Per-patient sensitivity was 91% for mpMRI and 82% for PET. CONCLUSIONS: mpMRI and PET provide complementary information for delineation of intra-prostatic recurrent disease. Union of CTV on mpMRI and PET is often less than 50% of the prostate, suggesting this imaging could help define a target for focal salvage therapy.

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

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

Nitric oxide synthase inhibition in healthy adults reduces regional and total cerebral macrovascular blood flow and microvascular perfusion.

The importance of nitric oxide (NO) in regulating cerebral blood flow (CBF) remains unresolved, due in part to methodological approaches, which lack a comprehensive assessment of both global and regional effects. Importantly, NO synthase (NOS) expression and activity appear greater in some anterior brain regions, suggesting region-specific NOS influence on CBF. We hypothesized that NO contributes to basal CBF in healthy adults, in a regionally distinct pattern that predominates in the anterior circulation. Fourteen healthy adults (7 females; 24 ± 5 years) underwent two magnetic resonance imaging (MRI) study visits with saline (placebo) or the NOS inhibitor, L-NMMA, administered in a randomized, single-blind approach. 4D flow MRI quantified total and regional macrovascular CBF, whereas arterial spin labelling (ASL) MRI quantified total and regional microvascular perfusion. L-NMMA (or volume-matched saline) was infused intravenously for 5 min prior to imaging. L-NMMA reduced CBF (L-NMMA: 722 ± 100 vs. placebo: 771 ± 121 ml/min, P = 0.01) with similar relative reductions (5-7%) in anterior and posterior cerebral circulations, due in part to the reduced cross-sectional area of 9 of 11 large cerebral arteries. Global microvascular perfusion (ASL) was reduced by L-NMMA (L-NMMA: 42 ± 7 vs. placebo: 47 ± 8 ml/100g/min, P = 0.02), with 7-11% reductions in both hemispheres of the frontal, parietal and temporal lobes, and in the left occipital lobe. We conclude that NO contributes to macrovascular and microvascular regulation including larger artery resting diameter. Contrary to our hypothesis, the influence of NO on cerebral perfusion appears regionally uniform in healthy young adults. KEY POINTS: Cerebral blood flow (CBF) is vital for brain health, but the signals that are key to regulating CBF remain unclear. Nitric oxide (NO) is produced in the brain, but its importance in regulating CBF remains controversial since prior studies have not studied all regions of the brain simultaneously. Using modern MRI approaches, a drug that inhibits the enzymes that make NO (L-NMMA) reduced CBF by up to 11% in different brain regions. NO helps maintain proper CBF in healthy adults. These data will help us understand whether the reductions in CBF that occur during ageing or cardiovascular disease are related to shifts in NO signalling.

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

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