Do Rib-Based Anchors Impair Chest Wall Motion in Early Onset Scoliosis (EOS)?

Purpose: There is a concern in pediatric surgery practice that rib-based fixation may limit chest wall motion in early onset scoliosis (EOS). The purpose of this study is to address the above concern by assessing the contribution of chest wall excursion to respiration before and after surgery. Methods: Quantitative dynamic magnetic resonance imaging (QdMRI) is performed on EOS patients (before and after surgery) and normal children in this retrospective study. QdMRI is purely an image-based approach and allows free breathing image acquisition. Tidal volume parameters for chest walls (CWtv) and hemi-diaphragms (Dtv) were analyzed on concave and convex sides of the spinal curve. EOS patients (1-14 years) and normal children (5-18 years) were enrolled, with an average interval of two years for dMRI acquisition before and after surgery. Results: CWtv significantly increased after surgery in the global comparison including all EOS patients (p < 0.05). For main thoracic curve (MTC) EOS patients, CWtv significantly improved by 50.24% (concave side) and 35.17% (convex side) after age correction (p < 0.05) after surgery. The average ratio of Dtv to CWtv on the convex side in MTC EOS patients was not significantly different from that in normal children (p=0.78), although the concave side showed the difference to be significant. Conclusion: Chest wall component tidal volumes in EOS patients measured via QdMRI did not decrease after rib-based surgery, suggesting that rib-based fixation does not impair chest wall motion in pediatric patients with EOS.

Virtual Growing Child (VGC): A general normative comparative system via quantitative dynamic MRI for quantifying pediatric regional respiratory anomalies with application in thoracic insufficiency syndrome (TIS).

Background: A normative database of regional respiratory structure and function in healthy children does not exist. Methods: VGC provides a database with four categories of regional respiratory measurement parameters including morphological, architectural, dynamic, and developmental. The database has 3,820 3D segmentations (around 100,000 2D slices with segmentations). Age and gender group analysis and comparisons for healthy children were performed using those parameters via two-sided t-testing to compare mean measurements, for left and right sides at end-inspiration (EI) and end-expiration (EE), for different age and gender specific groups. We also apply VGC measurements for comparison with TIS patients via an extrapolation approach to estimate the association between measurement and age via a linear model and to predict measurements for TIS patients. Furthermore, we check the Mahalanobis distance between TIS patients and healthy children of corresponding age. Findings: The difference between male and female groups (10-12 years) behave differently from that in other age groups which is consistent with physiology/natural growth behavior related to adolescence with higher right lung and right diaphragm tidal volumes for females(p<0.05). The comparison of TIS patients before and after surgery show that the right and left components are not symmetrical, and the left side diaphragm height and tidal volume has been significantly improved after surgery (p <0.05). The left lung volume at EE, and left diaphragm height at EI of TIS patients after surgery are closer to the normal children with a significant smaller Mahalanobis distance (MD) after surgery (p<0.05). Interpretation: The VGC system can serve as a reference standard to quantify regional respiratory abnormalities on dMRI in young patients with various respiratory conditions and facilitate treatment planning and response assessment. Funding: The grant R01HL150147 from the National Institutes of Health (PI Udupa).

Assessment of 3D hemi-diaphragmatic motion via free-breathing dynamic MRI in pediatric thoracic insufficiency syndrome.

Purpose: Thoracic insufficiency syndrome (TIS) affects ventilatory function due to spinal and thoracic deformities limiting lung space and diaphragmatic motion. Corrective orthopedic surgery can be used to help normalize skeletal anatomy, restoring lung space and diaphragmatic motion. This study employs free-breathing dynamic MRI (dMRI) and quantifies the 3D motion of each hemi-diaphragm surface in normal and TIS patients, and evaluates effects of surgical intervention. Materials and Methods: In a retrospective study of 149 pediatric patients with TIS and 190 healthy children, we constructed 4D images from free-breathing dMRI and manually delineated the diaphragm at end-expiration (EE) and end-inspiration (EI) time points. We automatically selected 25 points uniformly on each hemi-diaphragm surface, calculated their relative velocities between EE and EI, and derived mean velocities in 13 homologous regions for each hemi-diaphragm to provide measures of regional 3D hemi-diaphragm motion. T-testing was used to compare velocity changes before and after surgery, and to velocities in healthy controls. Results: The posterior-central region of the right hemi-diaphragm exhibited the highest average velocity post-operatively. Posterior regions showed greater velocity changes after surgery in both right and left hemi-diaphragms. Surgical reduction of thoracic Cobb angle displayed a stronger correlation with changes in diaphragm velocity than reduction in lumbar Cobb angle. Following surgery, the anterior regions of the left hemi-diaphragm tended to approach a more normal state. Conclusion: Quantification of regional motion of the 3D diaphragm surface in normal subjects and TIS patients via free-breathing dMRI is feasible. Derived measurements can be assessed in comparison to normal subjects to study TIS and the effects of surgery.

Uncommon Causes of Interlobular Septal Thickening on CT Images and Their Distinguishing Features.

Interlobular septa thickening (ILST) is a common and easily recognized feature on computed tomography (CT) images in many lung disorders. ILST thickening can be smooth (most common), nodular, or irregular. Smooth ILST can be seen in pulmonary edema, pulmonary alveolar proteinosis, and lymphangitic spread of tumors. Nodular ILST can be seen in the lymphangitic spread of tumors, sarcoidosis, and silicosis. Irregular ILST is a finding suggestive of interstitial fibrosis, which is a common finding in fibrotic lung diseases, including sarcoidosis and usual interstitial pneumonia. Pulmonary edema and lymphangitic spread of tumors are the commonly encountered causes of ILST. It is important to narrow down the differential diagnosis as much as possible by assessing the appearance and distribution of ILST, as well as other pulmonary and extrapulmonary findings. This review will focus on the CT characterization of the secondary pulmonary lobule and ILST. Various uncommon causes of ILST will be discussed, including infections, interstitial pneumonia, depositional/infiltrative conditions, inhalational disorders, malignancies, congenital/inherited conditions, and iatrogenic causes. Awareness of the imaging appearance and various causes of ILST allows for a systematic approach, which is important for a timely diagnosis. This study highlights the importance of a structured approach to CT scan analysis that considers ILST characteristics, associated findings, and differential diagnostic considerations to facilitate accurate diagnoses.

MRI-Based Radiomics Analysis of Levator Ani Muscle for Predicting Urine Incontinence after Robot-Assisted Radical Prostatectomy.

BACKGROUND: The exact role of the levator ani (LA) muscle in male continence remains unclear, and so this study aims to shed light on the topic by characterizing MRI-derived radiomic features of LA muscle and their association with postoperative incontinence in men undergoing prostatectomy. METHOD: In this retrospective study, 140 patients who underwent robot-assisted radical prostatectomy (RARP) for prostate cancer using preoperative MRI were identified. A biomarker discovery approach based on the optimal biomarker (OBM) method was used to extract features from MRI images, including morphological, intensity-based, and texture-based features of the LA muscle, along with clinical variables. Mathematical models were created using subsets of features and were evaluated based on their ability to predict continence outcomes. RESULTS: Univariate analysis showed that the best discriminators between continent and incontinent patients were patients age and features related to LA muscle texture. The proposed feature selection approach found that the best classifier used six features: age, LA muscle texture properties, and the ratio between LA size descriptors. This configuration produced a classification accuracy of 0.84 with a sensitivity of 0.90, specificity of 0.75, and an area under the ROC curve of 0.89. CONCLUSION: This study found that certain patient factors, such as increased age and specific texture properties of the LA muscle, can increase the odds of incontinence after RARP. The results showed that the proposed approach was highly effective and could distinguish and predict continents from incontinent patients with high accuracy.

A phase 1 trial of adoptive transfer of vaccine-primed autologous circulating T cells in ovarian cancer.

We have previously shown that vaccination with tumor-pulsed dendritic cells amplifies neoantigen recognition in ovarian cancer. Here, in a phase 1 clinical study ( NCT01312376 /UPCC26810) including 19 patients, we show that such responses are further reinvigorated by subsequent adoptive transfer of vaccine-primed, ex vivo-expanded autologous peripheral blood T cells. The treatment is safe, and epitope spreading with novel neopeptide reactivities was observed after cell infusion in patients who experienced clinical benefit, suggesting reinvigoration of tumor-sculpting immunity.

Periprostatic Adipose Tissue MRI Radiomics-Derived Features Associated with Clinically Significant Prostate Cancer.

Background: Altered systemic and cellular lipid metabolism plays a pivotal role in the pathogenesis of prostate cancer (PCa). In this study, we aimed to characterize T1-magnetic resonance imaging (MRI)-derived radiomic parameters of periprostatic adipose tissue (PPAT) associated with clinically significant PCa (Gleason score ≥7 [3 + 4]) in a cohort of men who underwent robot-assisted prostatectomy. Methods: Preoperative MRI scans of 98 patients were identified. The volume of interest was defined by identifying an annular shell-like region on each MRI slice to include all surgically resectable visceral adipose tissue. An optimal biomarker method was used to identify features from 7631 intensity- and texture-based properties that maximized the classification of patients into clinically significant PCa and indolent tumors at the final pathology analysis. Results: Six highest ranked optimal features were derived, which demonstrated a sensitivity, specificity, and accuracy of association with the presence of clinically significant PCa, and area under a receiver operating characteristic curve of 0.95, 0.39 0.82, and 0.82, respectively. Conclusion: A highly independent set of PPAT features derived from MRI scans that predict patients with clinically significant PCa was developed and tested. With future external validation, these features may provide a more precise scientific basis for deciding to omit biopsies in patients with borderline prostate-specific antigen kinetics and multiparametric MRI readings and help in the decision of enrolling patients into active surveillance.

Prediction of lymphoma response to CAR T cells by deep learning-based image analysis.

Clinical prognostic scoring systems have limited utility for predicting treatment outcomes in lymphomas. We therefore tested the feasibility of a deep-learning (DL)-based image analysis methodology on pre-treatment diagnostic computed tomography (dCT), low-dose CT (lCT), and 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) images and rule-based reasoning to predict treatment response to chimeric antigen receptor (CAR) T-cell therapy in B-cell lymphomas. Pre-treatment images of 770 lymph node lesions from 39 adult patients with B-cell lymphomas treated with CD19-directed CAR T-cells were analyzed. Transfer learning using a pre-trained neural network model, then retrained for a specific task, was used to predict lesion-level treatment responses from separate dCT, lCT, and FDG-PET images. Patient-level response analysis was performed by applying rule-based reasoning to lesion-level prediction results. Patient-level response prediction was also compared to prediction based on the international prognostic index (IPI) for diffuse large B-cell lymphoma. The average accuracy of lesion-level response prediction based on single whole dCT slice-based input was 0.82+0.05 with sensitivity 0.87+0.07, specificity 0.77+0.12, and AUC 0.91+0.03. Patient-level response prediction from dCT, using the "Majority 60%" rule, had accuracy 0.81, sensitivity 0.75, and specificity 0.88 using 12-month post-treatment patient response as the reference standard and outperformed response prediction based on IPI risk factors (accuracy 0.54, sensitivity 0.38, and specificity 0.61 (p = 0.046)). Prediction of treatment outcome in B-cell lymphomas from pre-treatment medical images using DL-based image analysis and rule-based reasoning is feasible. This approach can potentially provide clinically useful prognostic information for decision-making in advance of initiating CAR T-cell therapy.

Auto-segmentation of thoracic brachial plexuses for radiation therapy planning.

Recently, deep learning networks have achieved considerable success in segmenting organs in medical images. Several methods have used volumetric information with deep networks to achieve segmentation accuracy. However, these networks suffer from interference, risk of overfitting, and low accuracy as a result of artifacts, in the case of very challenging objects like the brachial plexuses. In this paper, to address these issues, we synergize the strengths of high-level human knowledge (i.e., natural intelligence (NI)) with deep learning (i.e., artificial intelligence (AI)) for recognition and delineation of the thoracic brachial plexuses (BPs) in computed tomography (CT) images. We formulate an anatomy-guided deep learning hybrid intelligence approach for segmenting thoracic right and left brachial plexuses consisting of 2 key stages. In the first stage (AAR-R), objects are recognized based on a previously created fuzzy anatomy model of the body region with its key organs relevant for the task at hand wherein high-level human anatomic knowledge is precisely codified. The second stage (DL-D) uses information from AAR-R to limit the search region to just where each object is most likely to reside and performs encoder-decoder delineation in slices. The proposed method is tested on a dataset that consists of 125 images of the thorax acquired for radiation therapy planning of tumors in the thorax and achieves a Dice coefficient of 0.659.

Mendability Index: A new metric for estimating the effort required for manually editing auto-segmentations of objects of interest.

Auto-segmentation of medical images is critical to boost precision radiology and radiation oncology efficiency, thereby improving medical quality for both health care practitioners and patients. An appropriate metric to evaluate auto-segmentation results is one of the significant tools necessary for building an effective, robust, and practical auto-segmentation technique. However, by comparing the predicted segmentation with the ground truth, currently widely-used metrics usually focus on the overlapping area (Dice Coefficient) or the most severe shifting of the boundary (Hausdorff Distance), which seem inconsistent with human reader behaviors. Human readers usually verify and correct auto-segmentation contours and then apply the modified segmentation masks to guide clinical application in diagnosis or treatment. A metric called Mendability Index (MI) is proposed to better estimate the effort required for manually editing the auto-segmentations of objects of interest in medical images so that the segmentations become acceptable for the application at hand. Considering different human behaviors for different errors, MI classifies auto-segmented errors into three types with different quantitative behaviors. The fluctuation of human subjective delineation is also considered in MI. 505 3D computed tomography (CT) auto-segmentations consisting of 6 objects from 3 institutions with the corresponding ground truth and the recorded manual mending time needed by experts are used to validate the performance of the proposed MI. The correlation between the time for editing with the segmentation metrics demonstrates that MI is generally more suitable for indicating mending efforts than Dice Coefficient or Hausdorff Distance, suggesting that MI may be an effective metric to quantify the clinical value of auto-segmentations.

Combination ATR (ceralasertib) and PARP (olaparib) Inhibitor (CAPRI) Trial in Acquired PARP Inhibitor-Resistant Homologous Recombination-Deficient Ovarian Cancer.

PURPOSE: Addition of ataxia telangiectasia and Rad3-related kinase inhibitors (ATRi) to PARP inhibitors (PARPi) overcomes PARPi resistance in high-grade serous ovarian cancer (HGSOC) cell and mouse models. We present the results of an investigator-initiated study of combination PARPi (olaparib) and ATRi (ceralasertib) in patients with acquired PARPi-resistant HGSOC. PATIENTS AND METHODS: Eligible patients had recurrent, platinum-sensitive BRCA1/2 mutated or homologous recombination (HR)-deficient (HRD) HGSOC and clinically benefited from PARPi (response by imaging/CA-125 or duration of maintenance therapy; > 12 months first-line or > 6 months ≥ second-line) before progression. No intervening chemotherapy was permitted. Patients received olaparib 300 mg twice daily and ceralasertib 160 mg daily on days 1 to 7 of a 28-day cycle. Primary objectives were safety and objective response rate (ORR). RESULTS: Thirteen patients enrolled were evaluable for safety and 12 for efficacy; 62% (n = 8) had germline BRCA1/2 mutations, 23% (n = 3) somatic BRCA1/2 mutations, and 15% (n = 2) tumors with positive HRD assay. Prior PARPi indication was treatment for recurrence (54%, n = 7), second-line maintenance (38%, n = 5) and first-line treatment with carboplatin/paclitaxel (8%, n = 1). There were 6 partial responses yielding an ORR of 50% (95% confidence interval, 0.15-0.72). Median treatment duration was 8 cycles (range 4-23+). Grade (G) 3/4 toxicities were 38% (n = 5); 15% (n = 2) G3 anemia, 23% (n = 3) G3 thrombocytopenia, 8% (n = 1) G4 neutropenia. Four patients required dose reductions. No patient discontinued treatment due to toxicity. CONCLUSIONS: Combination olaparib and ceralasertib is tolerable and shows activity in HR-deficient platinum-sensitive recurrent HGSOC that benefited and then progressed with PARPi as the penultimate regimen. These data suggest that ceralasertib resensitizes PARPi-resistant HGSOCs to olaparib, warranting further investigation.

Assessment of Regional Functional Effects of Surgical Treatment in Thoracic Insufficiency Syndrome via Dynamic Magnetic Resonance Imaging.

BACKGROUND: Quantitative regional assessment of thoracic function would enable clinicians to better understand the regional effects of therapy and the degree of deviation from normality in patients with thoracic insufficiency syndrome (TIS). The purpose of this study was to determine the regional functional effects of surgical treatment in TIS via quantitative dynamic magnetic resonance imaging (MRI) in comparison with healthy children. METHODS: Volumetric parameters were derived via 129 dynamic MRI scans from 51 normal children (November 2017 to March 2019) and 39 patients with TIS (preoperatively and postoperatively, July 2009 to May 2018) for the left and right lungs, the left and right hemi-diaphragms, and the left and right hemi-chest walls during tidal breathing. Paired t testing was performed to compare the parameters from patients with TIS preoperatively and postoperatively. Mahalanobis distances between parameters of patients with TIS and age-matched normal children were assessed to evaluate the closeness of patient lung function to normality. Linear regression functions were utilized to estimate volume deviations of patients with TIS from normality, taking into account the growth of the subjects. RESULTS: The mean Mahalanobis distances for the right hemi-diaphragm tidal volume (RDtv) were -1.32 ± 1.04 preoperatively and -0.05 ± 1.11 postoperatively (p = 0.001). Similarly, the mean Mahalanobis distances for the right lung tidal volume (RLtv) were -1.12 ± 1.04 preoperatively and -0.10 ± 1.26 postoperatively (p = 0.01). The mean Mahalanobis distances for the ratio of bilateral hemi-diaphragm tidal volume to bilateral lung tidal volume (BDtv/BLtv) were -1.68 ± 1.21 preoperatively and -0.04 ± 1.10 postoperatively (p = 0.003). Mahalanobis distances decreased after treatment, suggesting reduced deviations from normality. Regression results showed that all volumes and tidal volumes significantly increased after treatment (p < 0.001), and the tidal volume increases were significantly greater than those expected from normal growth for RDtv, RLtv, BDtv, and BLtv (p < 0.05). CONCLUSIONS: Postoperative tidal volumes of bilateral lungs and bilateral hemi-diaphragms of patients with TIS came closer to those of normal children, indicating positive treatment effects from the surgical procedure. Quantitative dynamic MRI facilitates the assessment of regional effects of a surgical procedure to treat TIS. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

Post-acquisition Standardization of Positron Emission Tomography Images.

Purpose: Tissue radiotracer activity measured from positron emission tomography (PET) images is an important biomarker that is clinically utilized for diagnosis, staging, prognostication, and treatment response assessment in patients with cancer and other clinical disorders. Using PET image values to define a normal range of metabolic activity for quantification purposes is challenging due to variations in patient-related factors and technical factors. Although the formulation of standardized uptake value (SUV) has compensated for some of these variabilities, significant non-standardness still persists. We propose an image processing method to substantially mitigate these variabilities. Methods: The standardization method is similar for activity concentration (AC) PET and SUV PET images with some differences and consists of two steps. The calibration step is performed only once for each of AC PET or SUV PET, employs a set of images of normal subjects, and requires a reference object, while the transformation step is executed for each patient image to be standardized. In the calibration step, a standardized scale is determined along with 3 key image intensity landmarks defined on it including the minimum percentile intensity smin, median intensity sm, and high percentile intensity smax. smin and sm are estimated based on image intensities within the body region in the normal calibration image set. The optimal value of the maximum percentile ß corresponding to the intensity smax is estimated via an optimization process by using the reference object to optimally separate the highly variable high uptake values from the normal uptake intensities. In the transformation step, the first two landmarks - the minimum percentile intensity pα(I), and the median intensity pm(I) - are found for the given image I for the body region, and the high percentile intensity pß(I) is determined corresponding to the optimally estimated high percentile value ß. Subsequently, intensities of I are mapped to the standard scale piecewise linearly for different segments.We employ three strategies for evaluation and comparison with other standardization methods: (i) Comparing coefficient of variation (CVO) of mean intensity within test objects O across different normal test subjects before and after standardization; (ii) Comparing mean absolute difference (MDO) of mean intensity within test objects O across different subjects in repeat scans before and after standardization; (iii) Comparing CVO of mean intensity across different normal subjects before and after standardization where the scans came from different brands of scanners. Results: Our data set consisted of 84 FDG-PET/CT scans of the body torso including 38 normal subjects and two repeat-scans of 23 patients. We utilized one of two objects - liver and spleen - as a reference object and the other for testing. The proposed standardization method reduced CVO and MDO by a factor of 3-8 in comparison to other standardization methods and no standardization. Upon standardization by our method, the image intensities (both for AC and SUV) from two different brands of scanners become statistically indistinguishable, while without standardization, they differ significantly and by a factor of 3-9. Conclusions: The proposed method is automatic, outperforms current standardization methods, and effectively overcomes the residual variation left over in SUV and inter-scanner variations.

[18F]FluorThanatrace ([18F]FTT) PET Imaging of PARP-Inhibitor Drug-Target Engagement as a Biomarker of Response in Ovarian Cancer, a Pilot Study.

PURPOSE: PARP inhibitors have become the standard-of-care treatment for homologous recombination deficient (HRD) high-grade serous ovarian cancer (HGSOC). However, not all HRD tumors respond to PARPi. Biomarkers to predict response are needed. [18F]FluorThanatrace ([18F]FTT) is a PARPi-analog PET radiotracer that noninvasively measures PARP-1 expression. Herein, we evaluate [18F]FTT as a biomarker to predict response to PARPi in patient-derived xenograft (PDX) models and subjects with HRD HGSOC. EXPERIMENTAL DESIGN: In PDX models, [18F]FTT-PET was performed before and after PARPi (olaparib), ataxia-telangiectasia inhibitor (ATRi), or both (PARPi-ATRi). Changes in [18F]FTT were correlated with tumor volume changes. Subjects were imaged with [18F]FTT-PET at baseline and after ∼1 week of PARPi. Changes in [18F]FTT-PET uptake were compared with changes in tumor size (RECISTv1.1), CA-125, and progression-free survival (PFS). RESULTS: A decrease in [18F]FTT tumor uptake after PARPi correlated with response to PARPi, or PARPi-ATRi treatment in PARPi-resistant PDX models (r = 0.77-0.81). In subjects (n = 11), percent difference in [18F]FTT-PET after ∼7 days of PARPi compared with baseline correlated with best RECIST response (P = 0.01), best CA-125 response (P = 0.033), and PFS (P = 0.027). All subjects with >50% reduction in [18F]FTT uptake had >6-month PFS and >50% reduction in CA-125. Utilizing only baseline [18F]FTT uptake did not predict such responses. CONCLUSIONS: The decline in [18F]FTT uptake shortly after PARPi initiation provides a measure of drug-target engagement and shows promise as a biomarker to guide PARPi therapies in this pilot study. These results support additional preclinical mechanistic and clinical studies in subjects receiving PARPi ± combination therapy. See related commentary by Liu and Zamarin, p. 1384.

QdMRI: A system for comprehensive analysis of thoracic dynamics via dynamic MRI.

Quantitative thoracic dynamic magnetic resonance imaging (QdMRI), a recently developed technique, provides a potential solution for evaluating treatment effects in thoracic insufficiency syndrome (TIS). In this paper, we integrate all related algorithms and modules during our work from the past 10 years on TIS into one system, named QdMRI, to address the following questions: (1) How to effectively acquire dynamic images? For many TIS patients, subjects are unable to cooperate with breathing instructions during image acquisition. Image acquisition can only be implemented under free-breathing conditions, and it is not feasible to use a surrogate device for tracing breathing signals. (2) How to assess the thoracic structures from the acquired image, such as lungs, left and right, separately? (3) How to depict the dynamics of thoracic structures due to respiration motion? (4) How to use the structural and functional information for the quantitative evaluation of surgical TIS treatment and for the design of the surgery plan? The QdMRI system includes 4 major modules: dynamic MRI (dMRI) acquisition, 4D image construction, image segmentation (from 4D image), and visualization of segmentation results, dynamic measurements, and comparisons of measurements from TIS patients with those from normal children. Scanning/image acquisition time for one subject is ~20 minutes, 4D image construction time is ~5 minutes, image segmentation of lungs via deep learning is 70 seconds for all time points (with the average DICE 0.96 in healthy children), and measurement computation time is 2 seconds.

Preoperative Computed Tomography Morphological Features Indicative of Incisional Hernia Formation After Abdominal Surgery.

OBJECTIVE: To investigate key morphometric features identifiable on routine preoperative computed tomography (CT) imaging indicative of incisional hernia (IH) formation following abdominal surgery. BACKGROUND: IH is a pervasive surgical disease that impacts all surgical disciplines operating in the abdominopelvic region and affecting 13% of patients undergoing abdominal surgery. Despite the significant costs and disability associated with IH, there is an incomplete understanding of the pathophysiology of hernia. METHODS: A cohort of patients (n=21,501) that underwent colorectal surgery was identified, and clinical data and demographics were extracted, with a primary outcome of IH. Two datasets of case-control matched pairs were created for feature measurement, classification, and testing. Morphometric linear and volumetric measurements were extracted as features from anonymized preoperative abdominopelvic CT scans. Multivariate Pearson testing was performed to assess correlations among features. Each feature's ability to discriminate between classes was evaluated using 2-sided paired t testing. A support vector machine was implemented to determine the predictive accuracy of the features individually and in combination. RESULTS: Two hundred and twelve patients were analyzed (106 matched pairs). Of 117 features measured, 21 features were capable of discriminating between IH and non-IH patients. These features are categorized into three key pathophysiologic domains: 1) structural widening of the rectus complex, 2) increased visceral volume, 3) atrophy of abdominopelvic skeletal muscle. Individual prediction accuracy ranged from 0.69 to 0.78 for the top 3 features among 117. CONCLUSIONS: Three morphometric domains identifiable on routine preoperative CT imaging were associated with hernia: widening of the rectus complex, increased visceral volume, and body wall skeletal muscle atrophy. This work highlights an innovative pathophysiologic mechanism for IH formation hallmarked by increased intra-abdominal pressure and compromise of the rectus complex and abdominopelvic skeletal musculature.

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring.

BACKGROUND: Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end-to-end deep learning (DL) networks, are weak in garnering high-level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge. PURPOSE: We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation. METHODS: The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI-based automatic anatomy recognition object recognition (AAR-R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL-based recognition (DL-R), which refines the coarse recognition results of AAR-R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR-R fuzzy model of each object guided by the BBs output by DL-R; and (v) DL-based delineation (DL-D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system. RESULTS: The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground-truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53 mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto-contours and clinically drawn contours. CONCLUSIONS: The HI system is observed to behave like an expert human in robustness in the contouring task but vastly more efficiently. It seems to use NI help where image information alone will not suffice to decide, first for the correct localization of the object and then for the precise delineation of the boundary.

Object recognition in medical images via anatomy-guided deep learning.

PURPOSE: Despite advances in deep learning, robust medical image segmentation in the presence of artifacts, pathology, and other imaging shortcomings has remained a challenge. In this paper, we demonstrate that by synergistically marrying the unmatched strengths of high-level human knowledge (i.e., natural intelligence (NI)) with the capabilities of deep learning (DL) networks (i.e., artificial intelligence (AI)) in garnering intricate details, these challenges can be significantly overcome. Focusing on the object recognition task, we formulate an anatomy-guided deep learning object recognition approach named AAR-DL which combines an advanced anatomy-modeling strategy, model-based non-deep-learning object recognition, and deep learning object detection networks to achieve expert human-like performance. METHODS: The AAR-DL approach consists of 4 key modules wherein prior knowledge (NI) is made use of judiciously at every stage. In the first module AAR-R, objects are recognized based on a previously created fuzzy anatomy model of the body region with all its organs following the automatic anatomy recognition (AAR) approach wherein high-level human anatomic knowledge is precisely codified. This module is purely model-based with no DL involvement. Although the AAR-R operation lacks accuracy, it is robust to artifacts and deviations (much like NI), and provides the much-needed anatomic guidance in the form of rough regions-of-interest (ROIs) for the following DL modules. The 2nd module DL-R makes use of the ROI information to limit the search region to just where each object is most likely to reside and performs DL-based detection of the 2D bounding boxes (BBs) in slices. The 2D BBs hug the shape of the 3D object much better than 3D BBs and their detection is feasible only due to anatomy guidance from AAR-R. In the 3rd module, the AAR model is deformed via the found 2D BBs providing refined model information which now embodies both NI and AI decisions. The refined AAR model more actively guides the 4th refined DL-R module to perform final object detection via DL. Anatomy knowledge is made use of in designing the DL networks wherein spatially sparse objects and non-sparse objects are handled differently to provide the required level of attention for each. RESULTS: Utilizing 150 thoracic and 225 head and neck (H&N) computed tomography (CT) data sets of cancer patients undergoing routine radiation therapy planning, the recognition performance of the AAR-DL approach is evaluated on 10 thoracic and 16 H&N organs in comparison to pure model-based approach (AAR-R) and pure DL approach without anatomy guidance. Recognition accuracy is assessed via location error/ centroid distance error, scale or size error, and wall distance error. The results demonstrate how the errors are gradually and systematically reduced from the 1st module to the 4th module as high-level knowledge is infused via NI at various stages into the processing pipeline. This improvement is especially dramatic for sparse and artifact-prone challenging objects, achieving a location error over all objects of 4.4 mm and 4.3 mm for the two body regions, respectively. The pure DL approach failed on several very challenging sparse objects while AAR-DL achieved accurate recognition, almost matching human performance, showing the importance of anatomy guidance for robust operation. Anatomy guidance also reduces the time required for training DL networks considerably. CONCLUSIONS: (i) High-level anatomy guidance improves recognition performance of DL methods. (ii) This improvement is especially noteworthy for spatially sparse, low-contrast, inconspicuous, and artifact-prone objects. (iii) Once anatomy guidance is provided, 3D objects can be detected much more accurately via 2D BBs than 3D BBs and the 2D BBs represent object containment with much more specificity. (iv) Anatomy guidance brings stability and robustness to DL approaches for object localization. (v) The training time can be greatly reduced by making use of anatomy guidance.

Implementation of FDG-PET/CT imaging methodology for quantification of inflammatory response in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial.

We measured changes in 18F-fluorodeoxyglucose (FDG) uptake on positron emission tomography/computed tomography (PET/CT) images in the lung parenchyma to quantify the degree of lung inflammation in patients with locally advanced non-small cell lung cancer (NSCLC) who received radiotherapy (RT). The goal of this study was to demonstrate successful implementation of this imaging methodology on NSCLC patients and to report quantitative statistics between pre-RT and post-RT. Seventy-one patients with NSCLC underwent FDG-PET/CT imaging before and after RT in a prospective study (ACRIN 6668/RTOG 0235). Comparisons between pre-RT and post-RT PET/CT were conducted for partial volume corrected (PVC)-mean standardized uptake value (SUVmean), PVC-global lung parenchymal glycolysis (GLPG), and lung volume for both ipsilateral and contralateral lungs using the nonparametric Wilcoxon signed-rank test. Regression modeling was conducted to associate clinical characteristics with post-RT PET/CT parameters. There was a significant increase in average SUVmean and GLPG of the ipsilateral lung (relative change 40% and 20%) between pre-RT and post-RT PET/CT scans (P<0.0001 and P=0.004). Absolute increases in PVC-SUVmean and PVC-GLPG were more pronounced (ΔPVC-SUVmean 0.32 versus ΔSUVmean 0.28; ΔPVC-GLPG 463.34 cc versus ΔGLPG 352.90 cc) and highly significant (P<0.0001). In contrast, the contralateral lung demonstrated no significant difference between pre-RT to post-RT in either GLPG (P=0.12) or SUVmean (P=0.18). The only clinical feature significantly associated with post-RT PET/CT parameters was clinical staging. Our study demonstrated inflammatory response in the ipsilateral lung of NSCLC patients treated with photon RT, suggesting that PET/CT parameters may serve as biomarkers for radiation pneumonitis (RP).

Combination ATR and PARP Inhibitor (CAPRI): A phase 2 study of ceralasertib plus olaparib in patients with recurrent, platinum-resistant epithelial ovarian cancer.

OBJECTIVE: Platinum-resistant, high-grade serous ovarian cancer (HGSOC) has limited treatment options. Preclinical data suggest that poly(ADP-ribose) polymerase inhibitors (PARPi) and ataxia telangiectasia and Rad3-related kinase inhibitors (ATRi) are synergistic. CAPRI (NCT03462342) is an investigator-initiated study of olaparib plus ceralasertib in recurrent HGSOC. Herein, we present results from the platinum-resistant cohort. METHODS: A Simon 2-stage design was utilized. Platinum-resistant HGSOC patients received ceralasertib 160 mg orally daily, days 1-7 and olaparib 300 mg orally twice daily, days 1-28 of a 28-day cycle until toxicity or progression. Primary endpoints were toxicity and efficacy including objective response rate (ORR) by RECIST. Secondary endpoint was progression-free survival (PFS). The null hypothesis (≤5% ORR) would be rejected if there were ≥ 1 responses in 12 patients. RESULTS: Fourteen PARPi-naïve patients were evaluable for toxicity; 12 were evaluable for response. Three had BRCA1 mutations (1 germline, 2 somatic). Adverse events possibly related to treatment were primarily grade (G) 1/2. G3 toxicities included nausea (14.3%), fatigue (7.1%), anorexia (7.1%), and anemia (7.1%). No objective responses occurred. Best response was stable disease in 9 patients and progressive disease in three. Five patients had a ≥ 20% to <30% reduction in disease burden, including 3 with BRCA1 mutations. Three of 11 patients (27%; 2 with BRCA1 mutations) evaluable by Gynecologic Cancer Intergroup criteria had >50% CA-125 decline, including 2 with CA-125 normalization. Median PFS was 4.2 months overall (90% CI:3.5-8.2) and 8.2 months (3.6 months-not determined) for patients with BRCA1 mutations. CONCLUSIONS: Olaparib plus ceralasertib is well-tolerated. No objective responses occurred, though a signal of activity was seen particularly in disease associated with BRCA1. Further evaluation of this combination should include alternate dosing strategies in genomically-selected populations.