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Abstract Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration (AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest risk of GA progression is important for targeted utilization of emerging therapies. This study aimed to comprehensively evaluate the role of shape-based fractal dimension features ( $${F}_{fd}$$ F fd ) of sub-retinal pigment epithelium (sub-RPE) compartment and texture-based radiomics features ( $${F}_{t}$$ F t ) of Ellipsoid Zone (EZ)-RPE and sub-RPE compartments for risk stratification for subfoveal GA (sfGA) progression. This was a retrospective study of 137 dry AMD subjects with a 5-year follow-up. Based on sfGA status at year 5, eyes were categorized as Progressors and Non-progressors. A total of 15 shape-based $${F}_{fd}$$ F fd of sub-RPE surface and 494 $${F}_{t}$$ F t from each of sub-RPE and EZ-RPE compartments were extracted from baseline spectral domain-optical coherence tomography scans. The top nine features were identified from $${F}_{fd}$$ F fd and $${F}_{t}$$ F t feature pool separately using minimum Redundancy maximum Relevance feature selection and used to train a Random Forest (RF) classifier independently using three-fold cross validation on the training set ( $${S}_{t}$$ S t , N = 90) to distinguish between sfGA Progressors and Non-progressors. Combined $${F}_{fd}$$ F fd and $${F}_{t}$$ F t was also evaluated in predicting risk of sfGA progression. The RF classifier yielded AUC of 0.85, 0.79 and 0.89 on independent test set ( $${S}_{v}$$ S v , N = 47) using $${F}_{fd}$$ F fd , $${F}_{t}$$ F t , and their combination, respectively. Using combined $${F}_{fd}$$ F fd and $${F}_{t}$$ F t , the improvement in AUC was statistically significant on $${S}_{v}$$ S v with p-values of 0.032 and 0.04 compared to using only $${F}_{fd}$$ F fd and only $${F}_{t}$$ F t , respectively. Combined $${F}_{fd}$$ F fd and $${F}_{t}$$ F t appears to identify high-risk patients. Our results show that FD and texture features could be potentially used for predicting risk of sfGA progression and future therapeutic response.

Abstract Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder ( http://cohortfinder.com ), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning. We demonstrate CohortFinder improves ML model performance in downstream digital pathology and medical image processing tasks. CohortFinder is freely available for download at cohortfinder.com.

Abstract The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Background: The changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy are a complex area of study. Previous research underscores the importance of investigating the immune and collagen components within the tumor microenvironment for prognostic implications. Methods: In this study, we utilized computational pathology techniques with Hematoxylin and Eosin-stained images to quantitatively characterize the immune and collagen architecture within the tumor microenvironment of patients with high-grade serous ovarian carcinoma. Results: Our analysis of 12 pre- and post-neoadjuvant chemotherapy images revealed an increase in immune infiltrate, primarily within the epithelial region. Additionally, post-neoadjuvant chemotherapy images exhibited chaotic collagen architecture compared to pre-neoadjuvant chemotherapy images. Importantly, features extracted from post-neoadjuvant chemotherapy images showed associations with overall survival, potentially aiding in the selection of patients for immunotherapy trials. Conclusions: These findings offer critical insights into the changes in the tumor microenvironment of high-grade serous ovarian carcinomas following neoadjuvant chemotherapy and their potential implications for clinical outcomes.

Background and objective: Intravitreal injection of anti-VEGF agents is the first-line treatment for patients with neovascular-age related macular degeneration (nAMD). One unique serious adverse event that may be associated with these agents is intraocular inflammation (IOI). The main purpose of this analysis was to evaluate the potential presence of texture-based radiomics features characterizing heterogeneity within the vitreous compartment of spectral domain optical coherence tomography (SD-OCT) images that may precede or develop in association with IOI and might serve as OCT biomarkers for IOI. Methods: This is a post-hoc analysis of a subset of cases (N = 67) involving IOI, endophthalmitis, and/or retinal vascular occlusion in the phase 3 HAWK trial. These were investigator determined diagnoses that were also confirmed by the safety review committee. Intraocular inflammation was any signs of inflammation within the eye, endophthalmitis was inflammation associated with presumed infection, and retinal vascular occlusions consisted of intraocular inflammation with concurrent vascular occlusions/vasculitis. Out of 67 eyes, 34 belonged to the Safety group with an IOI event and 33 were propensity-matched Controls. A total of 481 texture-based radiomics features were extracted from the vitreous compartment of the SD-OCT scans at pre-IOI time point (i.e., much earlier than the actual event). Most discriminating five features, selected by the Wilcoxon Rank Sum feature selection were evaluated using Random Forest (RF) classifier on the training set (Str, N = 47) to differentiate between the two patient groups. Classifier performance was subsequently validated on the independent test set (St, N = 20). Additionally, the classifier performance in discriminating the Control and Safety group was also validated on St at the IOI event timepoint. Results: The RF classifier yielded area under the Receiver Operating Characteristics curve (AUC) of 0.76 and 0.81 on St using texture-based radiomics features at pre-IOI and event time-point, respectively. Conclusions: In this analysis, the presence of a pre-IOI safety signal was detected in the form of textural heterogeneity within the vitreous compartment even prior to the actual event being identified by the investigator. This finding may help the clinicians to assess for underlying posterior inflammation.

Abstract This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.

Abstract Background The role of immune cells in collagen degradation within the tumor microenvironment (TME) is unclear. Immune cells, particularly tumor-infiltrating lymphocytes (TILs), are known to alter the extracellular matrix, affecting cancer progression and patient survival. However, the quantitative evaluation of the immune modulatory impact on collagen architecture within the TME remains limited. Methods We introduce CollaTIL, a computational pathology method that quantitatively characterizes the immune-collagen relationship within the TME of gynecologic cancers, including high-grade serous ovarian (HGSOC), cervical squamous cell carcinoma (CSCC), and endometrial carcinomas. CollaTIL aims to investigate immune modulatory impact on collagen architecture within the TME, aiming to uncover the interplay between the immune system and tumor progression. Results We observe that an increased immune infiltrate is associated with chaotic collagen architecture and higher entropy, while immune sparse TME exhibits ordered collagen and lower entropy. Importantly, CollaTIL-associated features that stratify disease risk are linked with gene signatures corresponding to TCA-Cycle in CSCC, and amino acid metabolism, and macrophages in HGSOC. Conclusions CollaTIL uncovers a relationship between immune infiltration and collagen structure in the TME of gynecologic cancers. Integrating CollaTIL with genomic analysis offers promising opportunities for future therapeutic strategies and enhanced prognostic assessments in gynecologic oncology.

Objectives: To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE). Methods: Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC). Results: Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p

Abstract Digital pathology allows computerized analysis of tumor ecosystem using whole slide images (WSIs). Here, we present single-cell morphological and topological profiling (sc-MTOP) to characterize tumor ecosystem by extracting the features of nuclear morphology and intercellular spatial relationship for individual cells. We construct a single-cell atlas comprising 410 million cells from 637 breast cancer WSIs and dissect the phenotypic diversity within tumor, inflammatory and stroma cells respectively. Spatially-resolved analysis identifies recurrent micro-ecological modules representing locoregional multicellular structures and reveals four breast cancer ecotypes correlating with distinct molecular features and patient prognosis. Further analysis with multiomics data uncovers clinically relevant ecosystem features. High abundance of locally-aggregated inflammatory cells indicates immune-activated tumor microenvironment and favorable immunotherapy response in triple-negative breast cancers. Morphological intratumor heterogeneity of tumor nuclei correlates with cell cycle pathway activation and CDK inhibitors responsiveness in hormone receptor-positive cases. sc-MTOP enables using WSIs to characterize tumor ecosystems at the single-cell level.

Abstract Liver is one of the most common sites for metastases, which can occur on account of primary tumors from multiple sites of origin. Identifying the primary site of origin (PSO) of a metastasis can help in guiding therapeutic options for liver metastases. In this pilot study, we hypothesized that computer extracted handcrafted (HC) histomorphometric features can be utilized to identify the PSO of liver metastases. Cellular features, including tumor nuclei morphological and graph features as well as cytoplasm texture features, were extracted by computer algorithms from 175 slides (114 patients). The study comprised three experiments: (1) comparing and (2) fusing a machine learning (ML) model trained with HC pathomic features and deep learning (DL)‐based classifiers to predict site of origin; (3) identifying the section of the primary tumor from which metastases were derived. For experiment 1, we divided the cohort into training sets composed of primary and matched liver metastases [60 patients, 121 whole slide images (WSIs)], and a hold‐out validation set (54 patients, 54 WSIs) composed solely of liver metastases of known site of origin. Using the extracted HC features of the training set, a combination of supervised machine classifiers and unsupervised clustering was applied to identify the PSO. A random forest classifier achieved areas under the curve (AUCs) of 0.83, 0.64, 0.82, and 0.64 in classifying the metastatic tumor from colon, esophagus, breast, and pancreas on the validation set. The top features related to nuclear and peri‐nuclear shape and textural attributes. We also trained a DL network to serve as a direct comparison to our method. The DL model achieved AUCs for colon: 0.94, esophagus: 0.66, breast: 0.79, and pancreas: 0.67 in identifying PSO. A decision fusion‐based strategy was deployed to fuse the trained ML and DL classifiers and achieved slightly better results than ML or DL classifier alone (colon: 0.93, esophagus: 0.68, breast: 0.81, and pancreas: 0.69). For the third experiment, WSI‐level attention maps were also generated using a trained DL network to generate a composite feature similarity heat map between paired primaries and their associated metastases. Our experiments revealed that epithelium‐rich and moderately differentiated tumor regions of primary tumors were quantitatively similar to paired metastatic tumors. Our findings suggest that a combination of HC and DL features could potentially help identify the PSO for liver metastases while at the same time also potentially identify the spatial sites of origin for the metastases within primary tumors.

Abstract The combination of Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) and endocrine therapy (ET) is the standard of care for hormone receptor-positive (HR + ), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC). Currently, there are no robust biomarkers that can predict response to CDK4/6i, and it is not clear which patients benefit from this therapy. Since MBC patients with liver metastases have a poorer prognosis, developing predictive biomarkers that could identify patients likely to respond to CDK4/6i is clinically important. Here we show the ability of imaging texture biomarkers before and a few cycles after CDK4/6i therapy, to predict early response and overall survival (OS) on 73 MBC patients with known liver metastases who received palbociclib plus ET from two sites. The delta radiomic model was associated with OS in validation set (HR: 2.4; 95% CI, 1.06–5.6; P = 0.035; C-index = 0.77). Compared to RECIST response, delta radiomic features predicted response with area under the curve (AUC) = 0.72, 95% confidence interval (CI) 0.67–0.88. Our study revealed that radiomics features can predict a lack of response earlier than standard anatomic/RECIST 1.1 assessment and warrants further study and clinical validation.

Abstract Chemoradiation is a common therapeutic regimen for human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC). However, not all patients benefit from chemotherapy, especially patients with low-risk characteristics. We aim to develop and validate a prognostic and predictive radiomic image signature (pRiS) to inform survival and chemotherapy benefit using computed tomography (CT) scans from 491 stage I and II HPV-associated OPSCC, which were divided into three cohorts D1–D3. The prognostic performance of pRiS was evaluated on two test sets (D2, n = 162; D3, n = 269) using concordance index. Patients from D2 and D3 who received either radiotherapy alone or chemoradiation were used to validate pRiS as predictive of added benefit of chemotherapy. Seven features were selected to construct pRiS, which was found to be prognostic of overall survival (OS) on univariate analysis in D2 (hazard ratio [HR] = 2.14, 95% confidence interval [CI], 1.1–4.16, p = 0.02) and D3 (HR = 2.74, 95% CI, 1.34–5.62, p = 0.006). Chemotherapy was associated with improved OS for high-pRiS patients in D2 (radiation vs chemoradiation, HR = 4.47, 95% CI, 1.73–11.6, p = 0.002) and D3 (radiation vs chemoradiation, HR = 2.99, 95% CI, 1.04–8.63, p = 0.04). In contrast, chemotherapy did not improve OS for low-pRiS patients, which indicates these patients did not derive additional benefit from chemotherapy and could be considered for treatment de-escalation. The proposed radiomic signature was prognostic of patient survival and informed benefit from chemotherapy for stage I and II HPV-associated OPSCC patients.

Abstract The tumor immune composition influences prognosis and treatment sensitivity in lung cancer. The presence of effective adaptive immune responses is associated with increased clinical benefit after immune checkpoint blockers. Conversely, immunotherapy resistance can occur as a consequence of local T-cell exhaustion/dysfunction and upregulation of immunosuppressive signals and regulatory cells. Consequently, merely measuring the amount of tumor-infiltrating lymphocytes (TILs) may not accurately reflect the complexity of tumor-immune interactions and T-cell functional states and may not be valuable as a treatment-specific biomarker. In this work, we investigate an immune-related biomarker (PhenoTIL) and its value in associating with treatment-specific outcomes in non-small cell lung cancer (NSCLC). PhenoTIL is a novel computational pathology approach that uses machine learning to capture spatial interplay and infer functional features of immune cell niches associated with tumor rejection and patient outcomes. PhenoTIL’s advantage is the computational characterization of the tumor immune microenvironment extracted from H&E-stained preparations. Association with clinical outcome and major non-small cell lung cancer (NSCLC) histology variants was studied in baseline tumor specimens from 1,774 lung cancer patients treated with immunotherapy and/or chemotherapy, including the clinical trial Checkmate 057 (NCT01673867).

Abstract Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN−) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN− IBC. H&E images from a total of n = 321 patients with ER+ and LN− IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02–5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18–7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20–89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.

Abstract Background Bile duct brush specimens are difficult to interpret as they often present inflammatory and reactive backgrounds due to the local effects of stricture, atypical reactive changes, or previously installed stents, and often have low to intermediate cellularity. As a result, diagnosis of biliary adenocarcinomas is challenging and often results in large interobserver variability and low sensitivity Objective In this work, we used computational image analysis to evaluate the role of nuclear morphological and texture features of epithelial cell clusters to predict the presence of pancreatic and biliary tract adenocarcinoma on digitized brush cytology specimens. Methods Whole slide images from 124 patients, either diagnosed as benign or malignant based on clinicopathological correlation, were collected and randomly split into training (ST, N = 58) and testing (Sv, N = 66) sets, with the exception of cases diagnosed as atypical on cytology were included in Sv. Nuclear boundaries on cell clusters extracted from each image were segmented via a watershed algorithm. A total of 536 quantitative morphometric features pertaining to nuclear shape, size, and aggregate cluster texture were extracted from within the cell clusters. The most predictive features from patients in ST were selected via rank‐sum, t‐test, and minimum redundancy maximum relevance (mRMR) schemes. The selected features were then used to train three machine‐learning classifiers. Results Malignant clusters tended to exhibit lower textural homogeneity within the nucleus, greater textural entropy around the nuclear membrane, and longer minor axis lengths. The sensitivity of cytology alone was 74% (without atypicals) and 46% (with atypicals). With machine diagnosis, the sensitivity improved to 68% from 46% when atypicals were included and treated as nonmalignant false negatives. The specificity of our model was 100% within the atypical category. Conclusion We achieved an area under the receiver operating characteristic curve (AUC) of 0.79 on Sv, which included atypical cytological diagnosis.

ObjectiveThe aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy.MethodsThis retrospective study comprised N = 121 biopsy-proven PCa patients on AS at a single institution, of whom N = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS−) was defined when GGG remained the same during a median period of 18 months. Of N = 50 patients at baseline, N = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T bi-parametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (Cbr), baseline radiomics + baseline clinical (Cbrbcl), delta radiomics (CΔr), delta radiomics + baseline clinical (CΔrbcl), and delta radiomics + delta clinical (CΔrΔcl).ResultsAn AUC of 0.64 ± 0.09 was obtained for Cbr, which increased to 0.70 ± 0.18 with the integration of clinical variables (Cbrbcl). CΔr yielded an AUC of 0.74 ± 0.15. Integrating delta radiomics with baseline clinical variables yielded an AUC of 0.77 ± 0.23. CΔrΔclresulted in the best AUC of 0.84 ± 0.20 (p < 0.05) among all combinations.ConclusionOur preliminary findings suggest that delta radiomics were more strongly associated with upgrade events compared to PIRADS and other clinical variables. Delta radiomics on serial MRI in combination with changes in clinical variables (PSA and tumor volume) between baseline and follow-up showed the strongest association with biopsy upgrade in PCa patients on AS. Further independent multi-site validation of these preliminary findings is warranted.

Abstract Despite known histological, biological, and clinical differences between lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), relatively little is known about the spatial differences in their corresponding immune contextures. Our study of over 1000 LUAD and LUSC tumors revealed that computationally derived patterns of tumor-infiltrating lymphocytes (TILs) on H&E images were different between LUAD (N = 421) and LUSC (N = 438), with TIL density being prognostic of overall survival in LUAD and spatial arrangement being more prognostically relevant in LUSC. In addition, the LUAD-specific TIL signature was associated with OS in an external validation set of 100 NSCLC treated with more than six different neoadjuvant chemotherapy regimens, and predictive of response to therapy in the clinical trial CA209-057 (n = 303). In LUAD, the prognostic TIL signature was primarily comprised of CD4+ T and CD8+ T cells, whereas in LUSC, the immune patterns were comprised of CD4+ T, CD8+ T, and CD20+ B cells. In both subtypes, prognostic TIL features were associated with transcriptomics-derived immune scores and biological pathways implicated in immune recognition, response, and evasion. Our results suggest the need for histologic subtype-specific TIL-based models for stratifying survival risk and predicting response to therapy. Our findings suggest that predictive models for response to therapy will need to account for the unique morphologic and molecular immune patterns as a function of histologic subtype of NSCLC.

Background: around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective: To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods: This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results: Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p

Objective: To identify radiomic and clinical features associated with post-ablation recurrence of AF, given that cardiac morphologic changes are associated with persistent atrial fibrillation (AF), and initiating triggers of AF often arise from the pulmonary veins which are targeted in ablation. Methods: Subjects with pre-ablation contrast CT scans prior to first-time catheter ablation for AF between 2014–2016 were retrospectively identified. A training dataset (D1) was constructed from left atrial and pulmonary vein morphometric features extracted from equal numbers of consecutively included subjects with and without AF recurrence determined at 1 year. The top-performing combination of feature selection and classifier methods based on C-statistic was evaluated on a validation dataset (D2), composed of subjects retrospectively identified between 2005–2010. Clinical models ( $\text{M}_{\mathrm {C}}$ ) were similarly evaluated and compared to radiomic ( $\text{M}_{\mathrm {R}}$ ) and radiomic-clinical models ( $\text{M}_{\mathrm {RC}}$ ), each independently validated on D2. Results: Of 150 subjects in D1, 108 received radiofrequency ablation and 42 received cryoballoon. Radiomic features of recurrence included greater right carina angle, reduced anterior-posterior atrial diameter, greater atrial volume normalized to height, and steeper right inferior pulmonary vein angle. Clinical features predicting recurrence included older age, greater BMI, hypertension, and warfarin use; apixaban use was associated with reduced recurrence. AF recurrence was predicted with radio-frequency ablation models on D2 subjects with C-statistics of 0.68, 0.63, and 0.70 for radiomic, clinical, and combined feature models, though these were not prognostic in patients treated with cryoballoon. Conclusions: Pulmonary vein morphology associated with increased likelihood of AF recurrence within 1 year of catheter ablation was identified on cardiac CT. Significance: Radiomic and clinical features-based predictive models may assist in identifying atrial fibrillation ablation candidates with greatest likelihood of successful outcome.

Abstract Image‐based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open‐source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROIs) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (1) accept accurately annotated regions or (2) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (1) 337,386 nuclei in 5 pancreatic WSIs, (2) 5,692 tubules in 10 colorectal WSIs, and (3) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102×, 9×, and 39× were, respectively, witnessed while retaining f‐scores >0.95, suggesting that QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.

IntroductionMedulloblastoma (MB) is a malignant, heterogenous brain tumor. Advances in molecular profiling have led to identifying four molecular subgroups of MB (WNT, SHH, Group 3, Group 4), each with distinct clinical behaviors. We hypothesize that (1) aggressive MB tumors, growing heterogeneously, induce pronounced local structural deformations in the surrounding parenchyma, and (b) these local deformations as captured on Gadolinium (Gd)-enhanced-T1w MRI are independently associated with molecular subgroups, as well as overall survival in MB patients.MethodsIn this work, a total of 88 MB studies from 2 institutions were analyzed. Following tumor delineation, Gd-T1w scan for every patient was registered to a normal age-specific T1w-MRI template via deformable registration. Following patient-atlas registration, local structural deformations in the brain parenchyma were obtained for every patient by computing statistics from deformation magnitudes obtained from every 5mm annular region, 0 < d < 60 mm, where d is the distance from the tumor infiltrating edge.ResultsMulti-class comparison via ANOVA yielded significant differences between deformation magnitudes obtained for Group 3, Group 4, and SHH molecular subgroups, observed up to 60-mm outside the tumor edge. Additionally, Kaplan-Meier survival analysis showed that the local deformation statistics, combined with the current clinical risk-stratification approaches (molecular subgroup information and Chang’s classification), could identify significant differences between high-risk and low-risk survival groups, achieving better performance results than using any of these approaches individually.DiscussionThese preliminary findings suggest there exists significant association of our tumor-induced deformation descriptor with overall survival in MB, and that there could be an added value in using the proposed radiomic descriptor along with the current risk classification approaches, towards more reliable risk assessment in pediatric MB.

Purpose: No established biomarkers currently exist for therapeutic efficacy and durability of anti–VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Design: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Participants: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non–super responders (patients who did not achieve or maintain retinal fluid resolution). Methods: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. Main Outcome Measures: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. Results: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub–retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Conclusions: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non–super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Summary: Background: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio

BackgroundPrecision estimation of cardiovascular risk remains the cornerstone of atherosclerotic cardiovascular disease (ASCVD) prevention. While coronary artery calcium (CAC) scoring is the best available non-invasive quantitative modality to evaluate risk of ASCVD, it excludes risk related to prior myocardial infarction, cardiomyopathy, and arrhythmia which are implicated in ASCVD. The high-dimensional and inter-correlated nature of ECG data makes it a good candidate for analysis using machine learning techniques and may provide additional prognostic information not captured by CAC. In this study, we aimed to develop a quantitative ECG risk score (eRiS) to predict major adverse cardiovascular events (MACE) alone, or when added to CAC. Further, we aimed to construct and validate a novel nomogram incorporating ECG, CAC and clinical factors for ASCVD.MethodsWe analyzed 5,864 patients with at least 1 cardiovascular risk factor who underwent CAC scoring and a standard ECG as part of the CLARIFY study (ClinicalTrials.gov Identifier: NCT04075162). Events were defined as myocardial infarction, coronary revascularization, stroke or death. A total of 649 ECG features, consisting of measurements such as amplitude and interval measurements from all deflections in the ECG waveform (53 per lead and 13 overall) were automatically extracted using a clinical software (GE Muse™ Cardiology Information System, GE Healthcare). The data was split into 4 training (Str) and internal validation (Sv) sets [Str (1): Sv (1): 50:50; Str (2): Sv (2): 60:40; Str (3): Sv (3): 70:30; Str (4): Sv (4): 80:20], and the results were compared across all the subsets. We used the ECG features derived from Str to develop eRiS. A least absolute shrinkage and selection operator-Cox (LASSO-Cox) regularization model was used for data dimension reduction, feature selection, and eRiS construction. A Cox-proportional hazards model was used to assess the benefit of using an eRiS alone (Mecg), CAC alone (Mcac) and a combination of eRiS and CAC (Mecg+cac) for MACE prediction. A nomogram (Mnom) was further constructed by integrating eRiS with CAC and demographics (age and sex). The primary endpoint of the study was the assessment of the performance of Mecg, Mcac, Mecg+cac and Mnom in predicting CV disease-free survival in ASCVD.FindingsOver a median follow-up of 14 months, 494 patients had MACE. The feature selection strategy preserved only about 18% of the features that were consistent across the various strata (Str). The Mecg model, comprising of eRiS alone was found to be significantly associated with MACE and had good discrimination of MACE (C-Index: 0.7, p =

Abstract Collagen fiber organization has been found to be implicated in breast cancer prognosis. In this study, we evaluated whether computerized features of Collagen Fiber Orientation Disorder in Tumor-associated Stroma (CFOD-TS) on Hematoxylin & Eosin (H&E) slide images were prognostic of Disease Free Survival (DFS) in early stage Estrogen Receptor Positive (ER+) Invasive Breast Cancers (IBC). A Cox regression model named MCFOD-TS, was constructed using cohort St (N = 78) to predict DFS based on CFOD-TS features. The prognostic performance of MCFOD-TS was validated on cohort Sv (N = 219), a prospective clinical trial dataset (ECOG 2197). MCFOD-TS was prognostic of DFS in both St and Sv, independent of clinicopathological variables. Additionally, the molecular pathways regarding cell cycle regulation were identified as being significantly associated with MCFOD-TS derived risk scores. Our results also found that collagen fiber organization was more ordered in patients with short DFS. Our study provided a H&E image-based pipeline to derive a potential prognostic biomarker for early stage ER+ IBC without the need of special collagen staining or advanced microscopy techniques.

Abstract Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p

Purpose: Various pathways and cytokines are implicated in pathogenesis of diabetic macular edema (DME). Computational imaging biomarkers (CIBs) of vessel tortuosity from ultra-widefield fluorescein angiography (UWFA) and texture patterns from OCT images have been associated with anti–vascular endothelial growth factor (VEGF) therapy treatment response in DME. This analysis was a radiogenomic assessment of the association between underlying cytokines, UWFA, and OCT-based DME CIBs. Design: Biclustering analysis based on UWFA and OCT CIBs to identify a common imaging phenotype across patients with subsequent assessment of underlying cytokine signatures and treatment response attributes. Participants: The IMAGINE DME study was a post hoc study of cytokine expressions that included 24 eyes with sufficient baseline aqueous humor samples and an in-depth assessment of the imaging studies obtained during the phase I/II DmeAntiVEgf study (DAVE) that measured different cytokine expressions. Methods: A total of 151 graph or morphologic features quantifying leakage shape, size, density, interobject distance, and architecture of leakage spots and 5 vessel tortuosity features were extracted from the baseline UWFA scans, and 494 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images. Biclustering enables simultaneous clustering of patients and features and was used to aggregate patients in terms of their commonality of phenotypes (based on similar imaging attributes) and to identify commonality in terms of cytokine expression and treatment response to anti-VEGF therapy. Main Outcome Measures: Identification of eyes with similar imaging phenotypes to evaluate commonalities of patterns and underlying cytokine expression. Results: Strong correlations between VEGF and 7 UWFA leakage morphologic features (Pearson correlation coefficient [PCC], 0.45–0.51; P < 0.05), 1 vascular tortuosity-based UWFA feature (PCC, 0.45; P = 0.00016), and 2 OCT-derived intraretinal fluid texture features (PCC, 0.58–0.63; P < 0.05) were identified. Strong correlation between intraretinal fluid features and other cytokines (PCC, 0.41–0.59; P < 0.05) were also observed. Conclusions: This study identified groups of eyes with similar imaging phenotypes as defined by UWFA and OCT CIBs that demonstrated similar treatment response patterns and cytokine expression, including a strong association between VEGF with UWFA-derived leakage morphologic and vessel tortuosity features.

ObjectiveThe timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans.MethodsThe study comprised 268 patients from 4 institutions with resected (

ObjectiveTo derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa.MethodsThis retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (CS) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (CR) was trained for BCR. An integrated classifier CS+R was then trained using predictions from CS and CR. These models were trained on D1 (N = 71, 27 BCR+) and evaluated on independent hold-out set D2 (N = 62, 12 BCR+). CS+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years.ResultsCS+R resulted in a higher AUC (0.75) compared to CR (0.70, p = 0.04) and CS (0.69, p = 0.01) on D2 in predicting BCR. On univariable analysis, CS+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. CS+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). CS+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07).ConclusionsRadiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.

ObjectiveThe disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models—radiomics (MRM), clinical (MCM), and combined clinical–radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans.MethodsWe performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D1T (N = 473), and 40% test set D1V (N = 314). The patients from institution-2 were used for an independent validation test set D2V (N = 110). A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D1T.ResultsThe three out of the top five features identified using D1T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709–0.799) on D1T, 0.836 on D1V, and 0.748 D2V. The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743–0.825) on D1T, 0.813 on D1V, and 0.688 on D2V. Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774–0.853) on D1T, 0.847 on D1V, and 0.771 on D2V. The MRCM had an overall improvement in the performance of ~5.85% (D1T: p = 0.0031; D1Vp = 0.0165; D2V: p = 0.0369) over MCM.ConclusionThe novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.

Abstract Objective To investigate left atrial shape differences on CT scans of atrial fibrillation (AF) patients with (AF+) versus without (AF−) post-ablation recurrence and whether these shape differences predict AF recurrence. Methods This retrospective study included 68 AF patients who had pre-catheter ablation cardiac CT scans with contrast. AF recurrence was defined at 1 year, excluding a 3-month post-ablation blanking period. After creating atlases of atrial models from segmented AF+ and AF− CT images, an atlas-based implicit shape differentiation method was used to identify surface of interest (SOI). After registering the SOI to each patient model, statistics of the deformation on the SOI were used to create shape descriptors. The performance in predicting AF recurrence using shape features at and outside the SOI and eight clinical factors (age, sex, left atrial volume, left ventricular ejection fraction, body mass index, sinus rhythm, and AF type [persistent vs paroxysmal], catheter-ablation type [Cryoablation vs Irrigated RF]) were compared using 100 runs of fivefold cross validation. Results Differences in atrial shape were found surrounding the pulmonary vein ostia and the base of the left atrial appendage. In the prediction of AF recurrence, the area under the receiver-operating characteristics curve (AUC) was 0.67 for shape features from the SOI, 0.58 for shape features outside the SOI, 0.71 for the clinical parameters, and 0.78 combining shape and clinical features. Conclusion Differences in left atrial shape were identified between AF recurrent and non-recurrent patients using pre-procedure CT scans. New radiomic features corresponding to the differences in shape were found to predict post-ablation AF recurrence.

Objective: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy is the first-line treatment for both the diseases; however, the degree of response varies for individual patients. The main objective of this work was to identify the (i) texture-based radiomics features within individual fluid and retinal tissue compartments of baseline spectral-domain optical coherence tomography (SD-OCT) images and (ii) the specific spatial compartments that contribute most pertinent features for predicting therapeutic response. Methods: A total of 962 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images, obtained from the PERMEATE study. Top-performing features selected from the consensus of different feature selection methods were evaluated in conjunction with four different machine learning classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM) in a cross-validated approach to distinguish eyes tolerating extended interval dosing (non-rebounders) and those requiring more frequent dosing (rebounders). Results: Combination of fluid and retinal tissue features yielded a cross-validated area under receiver operating characteristic curve (AUC) of 0.78±0.08 in distinguishing rebounders from non-rebounders. Conclusions: This study revealed that the texture-based radiomics features pertaining to IRF subcompartment were most discriminating between rebounders and non-rebounders to anti-VEGF therapy. Clinical Impact: With further validation, OCT-based imaging biomarkers could be used for treatment management of DME patients.

Background The landmark study of durvalumab as consolidation therapy in NSCLC patients (PACIFIC trial) demonstrated significantly longer progression-free survival (PFS) in patients with locally advanced, unresectable non-small cell lung cancer (NSCLC) treated with durvalumab (immunotherapy, IO) therapy after chemoradiotherapy (CRT). In clinical practice in the USA, durvalumab continues to be used in patients across all levels of programmed cell death ligand-1 (PD-L1) expression. While immune therapies have shown promise in several cancers, some patients either do not respond to the therapy or have cancer recurrence after an initial response. It is not clear so far who will benefit of this therapy or what the mechanisms behind treatment failure are.Methods A total of 133 patients with unresectable stage III NSCLC who underwent durvalumab after CRT or CRT alone were included. Patients treated with durvalumab IO after CRT were randomly split into training (D1=59) and test (D2=59) sets and the remaining 15 patients treated with CRT alone were grouped in D3. Radiomic textural patterns from within and around the target nodules were extracted. A radiomic risk score (RRS) was built and was used to predict PFS and overall survival (OS). Patients were divided into high-risk and low-risk groups based on median RRS.Results RRS was found to be significantly associated with PFS in D1 (HR=2.67, 95% CI 1.85 to 4.13, p

Molecular subtypes of medulloblastoma [Sonic Hedgehog (SHH), Wingless/INT (WNT), Group 3, and Group 4] are defined by common patterns of gene expression. These differential gene expression patterns appear to result in different histomorphology and prognosis. Quantitative histomorphometry is a well-known method of computer-aided pathology image analysis. The hypotheses we sought to examine in this preliminary proof of concept study were whether computer extracted nuclear morphological features of medulloblastomas from digitized tissue slide images could independently: (1) distinguish between molecularly determined subgroups and (2) identify patterns within these subgroups that correspond with clinical outcome. Our dataset was composed of 46 medulloblastoma patients: 16 SHH (5 dead, 11 survived), 3 WNT (0 dead, 3 survived), 12 Group 3 (4 dead, 8 survived), and 15 were Group 4 (5 dead, 10 survived). A watershed-based thresholding scheme was used to automatically identify individual nuclei within digitized whole slide hematoxylin and eosin tissue images. Quantitative histomorphometric features corresponding to the texture (variation in pixel intensity), shape (variations in size, roundness), and architectural rearrangement (distances between, and number of connected neighbors) of nuclei were subsequently extracted. These features were ranked using feature selection schemes and these top-ranked features were then used to train machine-learning classifiers via threefold cross-validation to separate patients based on: (1) molecular subtype and (2) disease-specific outcomes within the individual molecular subtype groups. SHH and WNT tumors were separated from Groups 3 and 4 tumors with a maximum area under the receiver operating characteristic curve (AUC) of 0.7, survival within Group 3 tumors was predicted with an AUC of 0.92, and Group 3 and 4 patients were separated into high- and low-risk groups with p = 0.002. Model prediction was quantitatively compared with age, stage, and histological subtype using univariate and multivariate Cox hazard ratio models. Age was the most statistically significant variable for predicting survival in Group 3 and 4 tumors, but model predictions had the highest hazard ratio value. Quantitative nuclear histomorphometry can be used to study medulloblastoma genetic expression phenotypes as it may distinguish meaningful features of disease pathology.

Abstract Background Oncotype DX (ODx) is a 12-gene assay assessing the recurrence risk (high, intermediate, and low) of ductal carcinoma in situ (pre-invasive breast cancer), which guides clinicians regarding prescription of radiotherapy. However, ODx is expensive, time-consuming, and tissue-destructive. In addition, the actual prognostic meaning for the intermediate ODx risk category remains unclear. Methods In this work, we evaluated the ability of quantitative nuclear histomorphometric features extracted from hematoxylin and eosin-stained slide images of 62 ductal carcinoma in situ (DCIS) patients to distinguish between the corresponding ODx risk categories. The prognostic value of the identified image signature was further evaluated on an independent validation set of 30 DCIS patients in its ability to distinguish those DCIS patients who progressed to invasive carcinoma versus those who did not. Following nuclear segmentation and feature extraction, feature ranking strategies were employed to identify the most discriminating features between individual ODx risk categories. The selected features were then combined with machine learning classifiers to establish models to predict ODx risk categories. The model performance was evaluated using the average area under the receiver operating characteristic curve (AUC) using cross validation. In addition, an unsupervised clustering approach was also implemented to evaluate the ability of nuclear histomorphometric features to discriminate between the ODx risk categories. Results Features relating to spatial distribution, orientation disorder, and texture of nuclei were identified as most discriminating between the high ODx and the intermediate, low ODx risk categories. Additionally, the AUC of the most discriminating set of features for the different classification tasks was as follows: (1) high vs low ODx (0.68), (2) high vs. intermediate ODx (0.67), (3) intermediate vs. low ODx (0.57), (4) high and intermediate vs. low ODx (0.63), (5) high vs. low and intermediate ODx (0.66). Additionally, the unsupervised clustering resulted in intermediate ODx risk category patients being co-clustered with low ODx patients compared to high ODx. Conclusion Our results appear to suggest that nuclear histomorphometric features can distinguish high from low and intermediate ODx risk category patients. Additionally, our findings suggest that histomorphometric features for intermediate ODx were more similar to low ODx compared to high ODx risk category.

BackgroundSmall cell lung cancer (SCLC) is an aggressive malignancy characterized by initial chemosensitivity followed by resistance and rapid progression. Presently, there are no predictive biomarkers that can accurately guide the use of systemic therapy in SCLC patients. This study explores the role of radiomic features from both within and around the tumor lesion on pretreatment CT scans to a) prognosticate overall survival (OS) and b) predict response to chemotherapy.MethodsOne hundred fifty-three SCLC patients who had received chemotherapy were included. Lung tumors were contoured by an expert reader. The patients were divided randomly into approximately equally sized training (Str = 77) and test sets (Ste = 76). Textural descriptors were extracted from the nodule (intratumoral) and parenchymal regions surrounding the nodule (peritumoral). The clinical endpoints of this study were OS, progression-free survival (PFS), and best objective response to chemotherapy. Patients with complete or partial response were defined as “responders,” and those with stable or progression of disease were defined as “non-responders.” The radiomic risk score (RRS) was generated by using the least absolute shrinkage and selection operator (LASSO) with the Cox regression model. Patients were classified into the high-risk or low-risk groups based on the median of RRS. Association of the radiomic signature with OS was evaluated on Str and then tested on Ste. The features identified by LASSO were then used to train a linear discriminant analysis (LDA) classifier (MRad) to predict response to chemotherapy. A prognostic nomogram (NRad+Clin) was also developed on Str by combining clinical and prognostic radiomic features and validated on Ste. The Kaplan–Meier survival analysis and log-rank statistical tests were performed to assess the discriminative ability of the features. The discrimination performance of the NRad+Clin was assessed by Harrell’s C-index. To estimate the clinical utility of the nomogram, decision curve analysis (DCA) was performed by calculating the net benefits for a range of threshold probabilities in predicting which high-risk patients should receive more aggressive treatment as compared with the low-risk patients.ResultsA univariable Cox regression analysis indicated that RRS was significantly associated with OS in Str (HR: 1.53; 95% CI, [1.1–2.2; p = 0.021]; C-index = 0.72) and Ste (HR: 1.4, [1.1–1.82], p = 0.0127; C-index = 0.69). The RRS was also significantly associated with PFS in Str (HR: 1.89, [1.4–4.61], p = 0.047; C-index = 0.7) and Ste (HR: 1.641, [1.1–2.77], p = 0.04; C-index = 0.67). MRad was able to predict response to chemotherapy with an area under the receiver operating characteristic curve (AUC) of 0.76 ± 0.03 within Str and 0.72 within Ste. Predictors, including the RRS, gender, age, stage, and smoking status, were used in the prognostic nomogram. The discrimination ability of the NRad+Clin model on Str and Ste was C-index [95% CI]: 0.68 [0.66–0.71] and 0.67 [0.63–0.69], respectively. DCA indicated that the NRad+Clin model was clinically useful.ConclusionsRadiomic features extracted within and around the lung tumor on CT images were both prognostic of OS and predictive of response to chemotherapy in SCLC patients.

PurposeThere is a lack of biomarkers for accurately prognosticating outcome in both human papillomavirus-related (HPV+) and tobacco- and alcohol-related (HPV−) oropharyngeal squamous cell carcinoma (OPSCC). The aims of this study were to i) develop and evaluate radiomic features within (intratumoral) and around tumor (peritumoral) on CT scans to predict HPV status; ii) investigate the prognostic value of the radiomic features for both HPV− and HPV+ patients, including within individual AJCC eighth edition-defined stage groups; and iii) develop and evaluate a clinicopathologic imaging nomogram involving radiomic, clinical, and pathologic factors for disease-free survival (DFS) prediction for HPV+ patients.Experimental DesignThis retrospective study included 582 OPSCC patients, of which 462 were obtained from The Cancer Imaging Archive (TCIA) with available tumor segmentation and 120 were from Cleveland Clinic Foundation (CCF, denoted as SCCF) with HPV+ OPSCC. We subdivided the TCIA cohort into training (ST, 180 patients) and validation (SV, 282 patients) based on an approximately 3:5 ratio for HPV status prediction. The top 15 radiomic features that were associated with HPV status were selected by the minimum redundancy–maximum relevance (MRMR) using ST and evaluated on SV. Using 3 of these 15 top HPV status-associated features, we created radiomic risk scores for both HPV+ (RRSHPV+) and HPV− patients (RRSHPV−) through a Cox regression model to predict DFS. RRSHPV+ was further externally validated on SCCF. Nomograms for the HPV+ population (Mp+RRS) were constructed. Both RRSHPV+ and Mp+RRS were used to prognosticate DFS for the AJCC eighth edition-defined stage I, stage II, and stage III patients separately.ResultsRRSHPV+ was prognostic for DFS for i) the whole HPV+ population [hazard ratio (HR) = 1.97, 95% confidence interval (CI): 1.35–2.88, p < 0.001], ii) the AJCC eighth stage I population (HR = 1.99, 95% CI: 1.04–3.83, p = 0.039), and iii) the AJCC eighth stage II population (HR = 3.61, 95% CI: 1.71–7.62, p < 0.001). HPV+ nomogram Mp+RRS (C-index, 0.59; 95% CI: 0.54–0.65) was also prognostic of DFS (HR = 1.86, 95% CI: 1.27–2.71, p = 0.001).ConclusionCT-based radiomic signatures are associated with both HPV status and DFS in OPSCC patients. With additional validation, the radiomic signature and its corresponding nomogram could potentially be used for identifying HPV+ OPSCC patients who might be candidates for therapy deintensification.

Purpose: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features. Design: Retrospective image analysis study. Participants: Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color fundus photography. Exclusion criteria were any previous laser photocoagulation, low image quality, intravitreal or periocular pharmacotherapy within 6 months of imaging, and any other significant retinal disease including posterior uveitis, retinal vein occlusion, and choroidal neovascularization. Methods: The centered early mid-phase UWFA frame that captured the maximum vessel area was selected using automated custom software for each eye. Panretinal and zonal vascular features were extracted using a machine learning algorithm. Eyes with DR were graded for DR severity as mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). Parameters of normal eyes were compared with age- and gender-matched patients with DR using the t test. Differences between severity groups were evaluated by the analysis of variance and Kruskal-Wallis tests, generalized linear mixed-effects models, and random forest regression models. Main Outcome Measures: Diabetic retinopathy severity and vascular features (panretinal and zonal vessel area, length and geodesic distance, panretinal area index, tortuosity measures, vascular density measures, and zero vessel density rate). Results: Ninety-seven eyes from 60 patients with DR and 12 normal eyes from 12 patients that underwent UWFA for evaluation of fellow eye pathology had images of sufficient quality to be included in this analysis. The mean age was 60 ± 10 years in DR eyes and 46 ± 17 years in normal eyes. Panretinal vessel area, mean geodesic distance, skewness, and kurtosis of local vessel density was significantly higher in normal eyes compared with the age- and gender-matched eyes with DR (P < 0.05). Zero vessel density rate, skewness of vessel density, and mean mid-peripheral geodesic distance were among the most important features for distinguishing mild NPDR from advanced forms of DR and PDR versus eyes without PDR. Conclusions: Automated analysis of retinal vasculature demonstrated associations with DR severity and visual and subvisual vascular biomarkers. Further studies are needed to evaluate the clinical significance of these parameters for DR prognosis and therapeutic response.

Summary: Background: Biparametric MRI (comprising T2-weighted MRI and apparent diffusion coefficient maps) is increasingly being used to characterise prostate cancer. Although previous studies have combined Prostate Imaging–Reporting & Data System (PI-RADS)-based MRI findings with routinely available clinical variables and with deep learning-based imaging predictors, respectively, for prostate cancer risk stratification, none have combined all three. We aimed to construct an integrated nomogram (referred to as ClaD) combining deep learning-based imaging predictions, PI-RADS scoring, and clinical variables to identify clinically significant prostate cancer on biparametric MRI. Methods: In this retrospective multicentre study, we included patients with prostate cancer, with histopathology or biopsy reports and a screening or diagnostic MRI scan in the axial view, from four cohorts in the USA (from University Hospitals Cleveland Medical Center, Icahn School of Medicine at Mount Sinai, Cleveland Clinic, and Long Island Jewish Medical Center) and from the PROSTATEx Challenge dataset in the Netherlands. We constructed an integrated nomogram combining deep learning, PI-RADS score, and clinical variables (prostate-specific antigen, prostate volume, and lesion volume) using multivariable logistic regression to identify clinically significant prostate cancer on biparametric MRI. We used data from the first three cohorts to train the nomogram and data from the remaining two cohorts for independent validation. We compared the performance of our ClaD integrated nomogram with that of integrated nomograms combining clinical variables with either the deep learning-based imaging predictor (referred to as DIN) or PI-RADS score (referred to as PIN) using area under the receiver operating characteristic curves (AUCs). We also compared the ability of the nomograms to predict biochemical recurrence on a subset of patients who had undergone radical prostatectomy. We report cross-validation AUCs as means for the training set and used AUCs with 95% CIs to assess the performance on the test set. The difference in AUCs between the models were tested for statistical significance using DeLong's test. We used log-rank tests and Kaplan-Meier curves to analyse survival. Findings: We investigated 592 patients (823 lesions) with prostate cancer who underwent 3T multiparametric MRI at five hospitals in the USA between Jan 8, 2009, and June 3, 2017. The training data set consisted of 368 patients from three sites (the PROSTATEx Challenge cohort [n=204], University Hospitals Cleveland Medical Center [n=126], and Icahn School of Medicine at Mount Sinai [n=38]), and the independent validation data set consisted of 224 patients from two sites (Cleveland Clinic [n=151] and Long Island Jewish Medical Center [n=73]). The ClaD clinical nomogram yielded an AUC of 0·81 (95% CI 0·76–0·85) for identification of clinically significant prostate cancer in the validation data set, significantly improving performance over the DIN (0·74 [95% CI 0·69–0·80], p=0·0005) and PIN (0·76 [0·71–0·81], p

Background: We developed and validated a prognostic and predictive computational pathology risk score (CoRiS) using H&E stained tissue images from patients with early-stage non-small cell lung cancer (ES-NSCLC). Methods: 1330 patients with ES-NSCLC were acquired from 3 independent sources and divided into four cohorts D1-4. D1 comprised 100 surgery treated patients and was used to identify prognostic features via an elastic-net Cox model to predict overall and disease-free survival. CoRiS was constructed using the Cox model coefficients for the top features. The prognostic performance of CoRiS was evaluated on D2 (N=331), D3 (N=657) and D4 (N=242). Patients from D2 and D3 which comprised surgery + chemotherapy were used to validate CoRiS as predictive of added benefit to adjuvant chemotherapy (ACT) by comparing survival between different CoRiS defined risk groups. Findings: CoRiS was found to be prognostic on univariable analysis, D2 (hazard ratio (HR) = 1.41, adjusted (adj.) P = .01) and D3 (HR = 1.35, adj. P < .001). Multivariable analysis showed CoRiS was independently prognostic, D2 (HR = 1.41, adj. P < .001) and D3 (HR = 1.35, adj. P < .001), after adjusting for clinico-pathologic factors. CoRiS was also able to identify high-risk patients who derived survival benefit from ACT D2 (HR = 0.42, adj. P = .006) and D3 (HR = 0.46, adj. P = .08). Interpretation: CoRiS is a tissue non-destructive, quantitative and low-cost tool that could potentially help guide management of ES-NSCLC patients.

Abstract Background For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI. Methods Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve. Results The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods. Conclusions Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.

Abstract Adenocarcinomas and active granulomas can both have a spiculated appearance on computed tomography (CT) and both are often fluorodeoxyglucose (FDG) avid on positron emission tomography (PET) scan, making them difficult to distinguish. Consequently, patients with benign granulomas are often subjected to invasive surgical biopsies or resections. In this study, quantitative vessel tortuosity (QVT), a novel CT imaging biomarker to distinguish between benign granulomas and adenocarcinomas on routine non-contrast lung CT scans is introduced. Our study comprised of CT scans of 290 patients from two different institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. In conjunction with a machine learning classifier, the top informative and stable QVT features yielded an area under receiver operating characteristic curve (ROC AUC) of 0.85 in the independent validation set. On the same cohort, the corresponding AUCs for two human experts including a radiologist and a pulmonologist were found to be 0.61 and 0.60, respectively. QVT features also outperformed well known shape and textural radiomic features which had a maximum AUC of 0.73 (p-value = 0.002), as well as features learned using a convolutional neural network AUC = 0.76 (p-value = 0.028). Our results suggest that QVT features could potentially serve as a non-invasive imaging biomarker to distinguish granulomas from adenocarcinomas on non-contrast CT scans.

Background: We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher. Methods: A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D1, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D1 and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D2, N = 127). Findings: “RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79).” Interpretation: RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy. Funding: The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

A significant challenge in Glioblastoma (GBM) management is identifying pseudo-progression (PsP), a benign radiation-induced effect, from tumor recurrence, on routine imaging following conventional treatment. Previous studies have linked tumor lobar presence and laterality to GBM outcomes, suggesting that disease etiology and progression in GBM may be impacted by tumor location. Hence, in this feasibility study, we seek to investigate the following question: Can tumor location on treatment-naïve MRI provide early cues regarding likelihood of a patient developing pseudo-progression vs. tumor recurrence? In this study, 74 pre-treatment Glioblastoma MRI scans with PsP (33) and tumor recurrence (41) were analyzed. First, enhancing lesion on Gd-T1w MRI and peri-lesional hyperintensities on T2w/FLAIR were segmented by experts and then registered to a brain atlas. Using patients from the two phenotypes, we construct two atlases by quantifying frequency of occurrence of enhancing lesion and peri-lesion hyperintensities, by averaging voxel intensities across the population. Analysis of differential involvement was then performed to compute voxel-wise significant differences (p-value < 0.05) across the atlases. Statistically significant clusters were finally mapped to a structural atlas to provide anatomic localization of their location. Our results demonstrate that patients with tumor recurrence showed prominence of their initial tumor in the parietal lobe, while patients with PsP showed a multi-focal distribution of the initial tumor in the frontal and temporal lobes, insula, and putamen. These preliminary results suggest that lateralization of pre-treatment lesions toward certain anatomical areas of the brain may allow to provide early cues regarding assessing likelihood of occurrence of pseudo-progression from tumor recurrence on MRI scans.