Quantitative imaging biomarkers of immune-related adverse events in immune-checkpoint blockade-treated metastatic melanoma patients: a pilot study.

PURPOSE: To develop quantitative molecular imaging biomarkers of immune-related adverse event (irAE) development in malignant melanoma (MM) patients receiving immune-checkpoint inhibitors (ICI) imaged with 18F-FDG PET/CT. METHODS: 18F-FDG PET/CT images of 58 MM patients treated with anti-PD-1 or anti-CTLA-4 ICI were retrospectively analyzed for indication of irAE. Three target organs, most commonly affected by irAE, were considered: bowel, lung, and thyroid. Patient charts were reviewed to identify which patients experienced irAE, irAE grade, and time to irAE diagnosis. Target organs were segmented using a convolutional neural network (CNN), and novel quantitative imaging biomarkers - SUV percentiles (SUVX%) of 18F-FDG uptake within the target organs - were correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. Patients who did not experience irAE were used to establish normal ranges for target organ 18F-FDG uptake. RESULTS: A total of 31% (18/58) patients experienced irAE in the three target organs: bowel (n=6), lung (n=5), and thyroid (n=9). Optimal percentiles for identifying irAE were bowel (SUV95%, AUROC=0.79), lung (SUV95%, AUROC=0.98), and thyroid (SUV75%, AUROC=0.88). Optimal cut-offs for irAE detection were bowel (SUV95%>2.7 g/mL), lung (SUV95%>1.7 g/mL), and thyroid (SUV75%>2.1 g/mL). Normal ranges (95% confidence interval) for the SUV percentiles in patients without irAE were bowel [1.74, 2.86 g/mL], lung [0.73, 1.46 g/mL], and thyroid [0.86, 1.99 g/mL]. CONCLUSIONS: Increased 18F-FDG uptake within irAE-affected organs provides predictive information about the development of irAE in MM patients receiving ICI and represents a potential quantitative imaging biomarker for irAE. Some irAE can be detected on 18F-FDG PET/CT well before clinical symptoms appear.

Tackling the Lead Gremlins: A Response to Take-Home Lead Exposure in a Minnesota Industrial Facility, 2019.

Lead exposure that occurs from contamination inadvertently brought home from a workplace is known as take-home exposure. Take-home exposures are a public health hazard that adversely affects health equity for families and communities. This article describes coordinated action by agencies in Minnesota to curb lead exposure among children of workers at a facility that produces fishing sinkers and battery terminals. (Am J Public Health. 2022;112(S7):S655-S657. https://doi.org/10.2105/AJPH.2022.306982).

Coccidioidomycosis and COVID-19 Infection. An Analysis from a Single Medical Center Within the Coccidioidal Endemic Area.

At a single medical center, we identified 60 cases of coccidioidomycosis that were coincident with COVID-19 infection. Among these, seven patients developed new or clinically progressive coccidioidomycosis. Receipt of dexamethasone for COVID-19 infection was the only significant risk factor for the progression or development of clinically active coccidioidomycosis in this cohort. All patients survived and none developed disseminated coccidioidomycosis.

Biomechanical Robustness of a Contemporary Cementless Stem to Surgical Variation in Stem Size and Position.

Successful designs of total hip replacement (THR) need to be robust to surgical variation in sizing and positioning of the femoral stem. This study presents an automated method for comprehensive evaluation of the potential impact of surgical variability in sizing and positioning on the primary stability of a contemporary cementless femoral stem (Corail®, DePuy Synthes). A patient-specific finite element (FE) model of a femur was generated from computed tomography (CT) images from a female donor. An automated algorithm was developed to span the plausible surgical envelope of implant positions constrained by the inner cortical boundary. The analysis was performed on four stem sizes: oversized, ideal (nominal) sized, and undersized by up to two stem sizes. For each size, Latin hypercube sampling was used to generate models for 100 unique alignment scenarios. For each scenario, peak hip contact and muscle forces published for stair climbing were scaled to the donor's body weight and applied to the model. The risk of implant loosening was assessed by comparing the bone-implant micromotion/strains to thresholds (150 µm and 7000 µÎµ) above which fibrous tissue is expected to prevail and the periprosthetic bone to yield, respectively. The risk of long-term loosening due to adverse bone resorption was assessed using bone adaptation theory. The range of implant positions generated effectively spanned the available intracortical space. The Corail stem was found stable and robust to changes in size and position, with the majority of the bone-implant interface undergoing micromotion and interfacial strains that are well below 150 µm and 7000 µÎµ, respectively. Nevertheless, the range of implant positions generated caused an increase of up to 50% in peak micromotion and up to 25% in interfacial strains, particularly for retroverted stems placed in a medial position.

Role of quantitative imaging biomarkers in an early FDG-PET/CT for detection of immune-related adverse events in melanoma patients: a prospective study.

BACKGROUND: To evaluate the role of the novel quantitative imaging biomarker (QIB) SUVX% of 18F-FDG uptake extracted from early 18F-FDG-PET/CT scan at 4 weeks for the detection of immune-related adverse events (rAE) in a cohort of patients with metastatic melanoma (mM) patients receiving immune-checkpoint inhibitors (ICI). PATIENTS AND METHODS: In this prospective non-interventional, one-centre clinical study, patients with mM, receiving ICI treatment, were regularly followed by 18F-FDG PET/CT. Patients were scanned at baseline, early point at week four (W4), week sixteen (W16) and week thirty-two (W32) after ICI initiation. A convolutional neural network (CNN) was used to segment three organs: lung, bowel, thyroid. QIB of irAE - SUVX% - was analyzed within the target organs and correlated with the clinical irAE status. Area under the receiver-operating characteristic curve (AUROC) was used to quantify irAE detection performance. RESULTS: A total of 242 18F-FDG PET/CT images of 71 mM patients were prospectively collected and analysed. The early W4 scan showed improved detection only for the thyroid gland compared to W32 scan (p=0.047). The AUROC for detection of irAE in the three target organs was highest when SUVX% was extracted from W16 scan and was 0.76 for lung, 0.53 for bowel and 0.81 for thyroid. SUVX% extracted from W4 scan did not improve detection of irAE compared to W16 scan (lung: p = 0.54, bowel: p = 0.75, thyroid: p = 0.3, DeLong test), as well as compared to W32 scan in lungs (p = 0.32) and bowel (p = 0.3). CONCLUSIONS: Early time point 18F-FDG PET/CT at W4 did not lead to statistically significant earlier detection of irAE. However, organ 18F-FDG uptake as quantified by SUVX% proved to be a consistent QIB of irAE. To better assess the role of 18F-FDG PET/CT in irAE detection, the time evolution of 18F-FDG PET/CT quantifiable inflammation would be of essence, only achievable in multi centric studies.

An automated methodology for whole-body, multimodality tracking of individual cancer lesions.

Objective. Manual analysis of individual cancer lesions to assess disease response is clinically impractical and requires automated lesion tracking methodologies. However, no methodology has been developed for whole-body individual lesion tracking, across an arbitrary number of scans, and acquired with various imaging modalities.Approach. This study introduces a lesion tracking methodology and benchmarked it using 2368Ga-DOTATATE PET/CT and PET/MR images of eight neuroendocrine tumor patients. The methodology consists of six steps: (1) alignment of multiple scans via image registration, (2) body-part labeling, (3) automatic lesion-wise dilation, (4) clustering of lesions based on local lesion shape metrics, (5) assignment of lesion tracks, and (6) output of a lesion graph. Registration performance was evaluated via landmark distance, lesion matching accuracy was evaluated between each image pair, and lesion tracking accuracy was evaluated via identical track ratio. Sensitivity studies were performed to evaluate the impact of lesion dilation (fixed versus automatic dilation), anatomic location, image modalities (inter- versus intra-modality), registration mode (direct versus indirect registration), and track size (number of time-points and lesions) on lesion matching and tracking performance.Main results. Manual contouring yielded 956 lesions, 1570 lesion-matching decisions, and 493 lesion tracks. The median residual registration error was 2.5 mm. The automatic lesion dilation led to 0.90 overall lesion matching accuracy, and an 88% identical track ratio. The methodology is robust regarding anatomic locations, image modalities, and registration modes. The number of scans had a moderate negative impact on the identical track ratio (94% for 2 scans, 91% for 3 scans, and 81% for 4 scans). The number of lesions substantially impacted the identical track ratio (93% for 2 nodes versus 54% for ≥5 nodes).Significance. The developed methodology resulted in high lesion-matching accuracy and enables automated lesion tracking in PET/CT and PET/MR.

Assessing the Heterogeneity of Response of [68Ga] Ga-PSMA-11 PET/CT Lesions in Patients With Biochemical Recurrence of Prostate Cancer.

INTRODUCTION: Treatment of men with metastatic prostate cancer can be difficult due to the heterogeneity of response of lesions. [68Ga]Ga-PSMA-11 (PSMA) PET/CT assists with monitoring and directing clinical intervention; however, the impact of response heterogeneity has yet to be related to outcome measures. The aim of this study was to assess the impact of quantitative imaging information on the value of PSMA PET/CT to assess patient outcomes in response evaluation. PATIENTS AND METHODS: Baseline and follow-up (6 months) PSMA PET/CT of 162 men with oligometastatic PC treated with standard clinical care were acquired between 2015 and 2016 for analysis. An augmentative software medical device was used to track lesions between scans and quantify lesion change to categorize them as either new, increasing, stable, decreasing, or disappeared. Quantitative imaging features describing the size, intensity, extent, change, and heterogeneity of change (based on percent change in SUVtotal) among lesions were extracted and evaluated for association with overall survival (OS) using Cox regression models. Model performance was evaluated using the c-index. RESULTS: Forty-one (25%) of subjects demonstrated heterogeneous response at follow-up, defined as having at least 1 new or increasing lesion and at least 1 decreasing or disappeared lesion. Subjects with heterogeneous response demonstrated significantly shorter OS than subjects without (median OS = 76.6 months vs. median OS not reached, P < .05, c-index = 0.61). In univariate analyses, SUVtotal at follow-up was most strongly associated with OS (HR = 1.29 [1.19, 1.40], P < .001, c-index = 0.73). Multivariable models applied using heterogeneity of change features demonstrated higher performance (c-index = 0.79) than models without (c-index = 0.71-0.76, P < .05). CONCLUSION: Augmentative software tools enhance the evaluation change on serial PSMA PET scans and can facilitate lesional evaluation between timepoints. This study demonstrates that a heterogeneous response at a lesional level may impact adversely on patient outcomes and supports further investigation to evaluate the role of imaging to guide individualized patient management to improve clinical outcomes.

Histiocytic Sarcoma Treated with Pembrolizumab: A Case Report and Literature Review.

Histiocytic sarcoma (HS) is a rare hematologic malignancy that has historically been treated with lymphoma-based regimens with a median survival of 6 months. We describe a case of a 51-year-old woman who presented with acute back pain and cord compression. She was diagnosed with HS with diffuse skeletal lesions and high expression of programmed death ligand 1 (PD-L1). She was subsequently treated with chemotherapy plus off-label use of pembrolizumab followed by allogeneic stem cell transplantation. Ultimately, the patient died in the setting of progression of disease 17 months after her stem cell transplantation and 26 months after her diagnosis. This article also presents a literature review of cases of HS treated with programmed death ligand inhibition.

Multi-organ segmentation of CT via convolutional neural network: impact of training setting and scanner manufacturer.

Objective. Automated organ segmentation on CT images can enable the clinical use of advanced quantitative software devices, but model performance sensitivities must be understood before widespread adoption can occur. The goal of this study was to investigate performance differences between Convolutional Neural Networks (CNNs) trained to segment one (single-class) versus multiple (multi-class) organs, and between CNNs trained on scans from a single manufacturer versus multiple manufacturers.Methods. The multi-class CNN was trained on CT images obtained from 455 whole-body PET/CT scans (413 for training, 42 for testing) taken with Siemens, GE, and Phillips PET/CT scanners where 16 organs were segmented. The multi-class CNN was compared to 16 smaller single-class CNNs trained using the same data, but with segmentations of only one organ per model. In addition, CNNs trained on Siemens-only (N = 186) and GE-only (N = 219) scans (manufacturer-specific) were compared with CNNs trained on data from both Siemens and GE scanners (manufacturer-mixed). Segmentation performance was quantified using five performance metrics, including the Dice Similarity Coefficient (DSC).Results. The multi-class CNN performed well compared to previous studies, even in organs usually considered difficult auto-segmentation targets (e.g., pancreas, bowel). Segmentations from the multi-class CNN were significantly superior to those from smaller single-class CNNs in most organs, and the 16 single-class models took, on average, six times longer to segment all 16 organs compared to the single multi-class model. The manufacturer-mixed approach achieved minimally higher performance over the manufacturer-specific approach.Significance. A CNN trained on contours of multiple organs and CT data from multiple manufacturers yielded high-quality segmentations. Such a model is an essential enabler of image processing in a software device that quantifies and analyzes such data to determine a patient's treatment response. To date, this activity of whole organ segmentation has not been adopted due to the intense manual workload and time required.

Performance of an automated registration-based method for longitudinal lesion matching and comparison to inter-reader variability.

Objective.Patients with metastatic disease are followed throughout treatment with medical imaging, and accurately assessing changes of individual lesions is critical to properly inform clinical decisions. The goal of this work was to assess the performance of an automated lesion-matching algorithm in comparison to inter-reader variability (IRV) of matching lesions between scans of metastatic cancer patients.Approach.Forty pairs of longitudinal PET/CT and CT scans were collected and organized into four cohorts: lung cancers, head and neck cancers, lymphomas, and advanced cancers. Cases were also divided by cancer burden: low-burden (<10 lesions), intermediate-burden (10-29), and high-burden (30+). Two nuclear medicine physicians conducted independent reviews of each scan-pair and manually matched lesions. Matching differences between readers were assessed to quantify the IRV of lesion matching. The two readers met to form a consensus, which was considered a gold standard and compared against the output of an automated lesion-matching algorithm. IRV and performance of the automated method were quantified using precision, recall, F1-score, and the number of differences.Main results.The performance of the automated method did not differ significantly from IRV for any metric in any cohort (p> 0.05, Wilcoxon paired test). In high-burden cases, the F1-score (median [range]) was 0.89 [0.63, 1.00] between the automated method and reader consensus and 0.93 [0.72, 1.00] between readers. In low-burden cases, F1-scores were 1.00 [0.40, 1.00] and 1.00 [0.40, 1.00], for the automated method and IRV, respectively. Automated matching was significantly more efficient than either reader (p< 0.001). In high-burden cases, median matching time for the readers was 60 and 30 min, respectively, while automated matching took a median of 3.9 minSignificance.The automated lesion-matching algorithm was successful in performing lesion matching, meeting the benchmark of IRV. Automated lesion matching can significantly expedite and improve the consistency of longitudinal lesion-matching.

Effect of intraoperative treatment options on hip joint stability following total hip arthroplasty.

Dislocation remains the leading indication for revision of total hip arthroplasty (THA). The objective of this study was to use a computational model to compare the overall resistance to both anterior and posterior dislocation for the available THA constructs commonly considered by surgeons attempting to produce a stable joint. Patient-specific musculoskeletal models of THA patients performing activities consistent with anterior and posterior dislocation were developed to calculate joint contact forces and joint positions used for simulations of dislocation in a finite element model of the implanted hip that included an experimentally calibrated hip capsule representation. Dislocations were then performed with consideration of offset using +5 and +9 offset, iteratively with three lipped liner variations in jump distance (10°, 15°, and 20° lips), a size 40 head, and a dual-mobility construct. Dislocation resistance was quantified as the moment required to dislocate the hip and the integral of the moment-flexion angle (dislocation energy). Increasing head diameter increased resistive moment on average for anterior and posterior dislocation by 22% relative to a neutral configuration. A lipped liner resulted in increases in the resistive moment to posterior dislocation of 9%, 19%, and 47% for 10°, 15°, and 20° lips, a sensitivity of approximately 2.8 Nm/mm of additional jump distance. A dual-mobility acetabular design resulted in an average 38% increase in resistive moment and 92% increase in dislocation energy for anterior and posterior dislocation. A quantitative understanding of tradeoffs in the dislocation risk inherent to THA construct options is valuable in supporting surgical decision making.

Impact of COVID-19 on diagnosis of primary pulmonary coccidioidomycosis.

The COVID-19 pandemic has disrupted medical care worldwide and caused delays in care for many illnesses and procedures unrelated to COVID-19; however, less clear is how it may have affected diagnosis of conditions that present with similar symptoms, such as primary pulmonary coccidioidomycosis (PPC). We conducted an observational cohort study of patients diagnosed with PPC between March 1 and December 1 in 2 years: 2019 (before COVID-19) and in 2020 (after COVID-19) to compare the time from symptom onset to PPC diagnosis. Relevant demographic and clinical variables were collected, and statistical analyses were performed with the χ2 test, Wilcoxon rank sum test, and Cox proportional hazards regression analysis. During 2019, 83 patients were diagnosed with PPC. During 2020, 113 patients were diagnosed with PPC. For both groups, the median time from symptom onset to diagnosis of PPC was 14 days (P = .13). No significant differences in time to diagnosis existed between the 2 years for location of diagnosis (outpatient clinic, emergency department, or in hospital), for computed tomographic imaging performed before diagnosis, or for number of COVID-19 tests received before PPC diagnosis. In addition, there were no differences in the 2 years between the total number of clinical visits before diagnosis. However, patients in the post-COVID-19 group who had fever were diagnosed with PPC earlier than those without fever (hazard ratio, 1.77; 95% confidence interval, 1.15-2.73; P = .01). Contrary to what we expected, no significant delay in diagnosis of PPC occurred during the COVID-19 pandemic.

Interpretation and visualization techniques for deep learning models in medical imaging.

Deep learning (DL) approaches to medical image analysis tasks have recently become popular; however, they suffer from a lack of human interpretability critical for both increasing understanding of the methods' operation and enabling clinical translation. This review summarizes currently available methods for performing image model interpretation and critically evaluates published uses of these methods for medical imaging applications. We divide model interpretation in two categories: (1) understanding model structure and function and (2) understanding model output. Understanding model structure and function summarizes ways to inspect the learned features of the model and how those features act on an image. We discuss techniques for reducing the dimensionality of high-dimensional data and cover autoencoders, both of which can also be leveraged for model interpretation. Understanding model output covers attribution-based methods, such as saliency maps and class activation maps, which produce heatmaps describing the importance of different parts of an image to the model prediction. We describe the mathematics behind these methods, give examples of their use in medical imaging, and compare them against one another. We summarize several published toolkits for model interpretation specific to medical imaging applications, cover limitations of current model interpretation methods, provide recommendations for DL practitioners looking to incorporate model interpretation into their task, and offer general discussion on the importance of model interpretation in medical imaging contexts.

Image intensity histograms as imaging biomarkers: application to immune-related colitis.

Purpose.To investigate image intensity histograms as a potential source of useful imaging biomarkers in both a clinical example of detecting immune-related colitis (irColitis) in18F-FDG PET/CT images of immunotherapy patients and an idealized case of classifying digital reference objects (DRO).Methods.Retrospective analysis of bowel18F-FDG uptake in N = 40 patients receiving immune checkpoint inhibitors was conducted. A CNN trained to segment the bowel was used to generate the histogram of bowel18F-FDG uptake, and percentiles of the histogram were considered as potential metrics for detecting inflammation associated with irColitis. A model of the colon was also considered using cylindrical DRO. Classification of DRO with different intensity distributions was undertaken under varying geometry and noise settings.Results.The most predictive biomarker of irColitis was the 95th percentile of the bowel SUV histogram (SUV95%). Patients later diagnosed with irColitis had a significantly higher increase in SUV95%from baseline to first on-treatment PET than patients who did not experience irColitis (p = 0.02). An increase in SUV95%> + 40% separated pre-irColitis change from normal variability with a sensitivity of 75% and specificity of 88%. Furthermore, histogram percentiles were ideal metrics for classifying 'hot center' and 'cold center' DRO, and were robust to varying DRO geometry and noise, and to the presence of spoiler volumes unrelated to the detection task.Conclusions.The 95th percentile of the bowel SUV histogram was the optimal metric for detecting irColitis on18F-FDG PET/CT. Image intensity histograms are a promising source of imaging biomarkers for clinical tasks.

Development and validation of a longitudinal soft-tissue metastatic lesion matching algorithm.

Metastatic cancer presents with many, sometimes hundreds of metastatic lesions through the body, which often respond heterogeneously to treatment. Therefore, lesion-level assessment is necessary for a complete understanding of disease response. Lesion-level assessment typically requires manual matching of corresponding lesions, which is a tedious, subjective, and error-prone task. This study introduces a fully automated algorithm for matching of metastatic lesions in longitudinal medical images. The algorithm entails four steps: (1) image registration, (2) lesion dilation, (3) lesion clustering, and (4) linear assignment. In step (1), 3D deformable registration is used to register the scans. In step (2), lesion contours are conformally dilated. In step (3), lesion clustering is evaluated based on local metrics. In step (4), matching is assigned based on non-greedy cost minimization. The algorithm was optimized (e.g. choice of deformable registration algorithm, dilatation size) and validated on 140 scan-pairs of 32 metastatic cancer patients from two independent clinical trials, who received longitudinal PET/CT scans as part of their treatment response assessment. Registration error was evaluated using landmark distance. A sensitivity study was performed to evaluate the optimal lesion dilation magnitude. Lesion matching performance accuracy was evaluated for all patients and for a subset with high disease burden. Two investigated deformable registration approaches (whole body deformable and articulated deformable registrations) led to similar performance with the overall registration accuracy between 2.3 and 2.6 mm. The optimal dilation magnitude of 25 mm yielded almost a perfect matching accuracy of 0.98. No significant matching accuracy decrease was observed in the subset of patients with high lesion disease burden. In summary, lesion matching using our new algorithm was highly accurate and a significant improvement, when compared to previously established methods. The proposed method enables accurate automated metastatic lesion matching in whole-body longitudinal scans.