AI powered quantification of nuclear morphology in cancers enables prediction of genome instability and prognosis.

While alterations in nucleus size, shape, and color are ubiquitous in cancer, comprehensive quantification of nuclear morphology across a whole-slide histologic image remains a challenge. Here, we describe the development of a pan-tissue, deep learning-based digital pathology pipeline for exhaustive nucleus detection, segmentation, and classification and the utility of this pipeline for nuclear morphologic biomarker discovery. Manually-collected nucleus annotations were used to train an object detection and segmentation model for identifying nuclei, which was deployed to segment nuclei in H&E-stained slides from the BRCA, LUAD, and PRAD TCGA cohorts. Interpretable features describing the shape, size, color, and texture of each nucleus were extracted from segmented nuclei and compared to measurements of genomic instability, gene expression, and prognosis. The nuclear segmentation and classification model trained herein performed comparably to previously reported models. Features extracted from the model revealed differences sufficient to distinguish between BRCA, LUAD, and PRAD. Furthermore, cancer cell nuclear area was associated with increased aneuploidy score and homologous recombination deficiency. In BRCA, increased fibroblast nuclear area was indicative of poor progression-free and overall survival and was associated with gene expression signatures related to extracellular matrix remodeling and anti-tumor immunity. Thus, we developed a powerful pan-tissue approach for nucleus segmentation and featurization, enabling the construction of predictive models and the identification of features linking nuclear morphology with clinically-relevant prognostic biomarkers across multiple cancer types.

AI-based histologic scoring enables automated and reproducible assessment of enrollment criteria and endpoints in NASH clinical trials.

Clinical trials in nonalcoholic steatohepatitis (NASH) require histologic scoring for assessment of inclusion criteria and endpoints. However, guidelines for scoring key features have led to variability in interpretation, impacting clinical trial outcomes. We developed an artificial intelligence (AI)-based measurement (AIM) tool for scoring NASH histology (AIM-NASH). AIM-NASH predictions for NASH Clinical Research Network (CRN) grades of necroinflammation and stages of fibrosis aligned with expert consensus scores and were reproducible. Continuous scores produced by AIM-NASH for key histological features of NASH correlated with mean pathologist scores and with noninvasive biomarkers and strongly predicted patient outcomes. In a retrospective analysis of the ATLAS trial, previously unmet pathological endpoints were met when scored by the AIM-NASH algorithm alone. Overall, these results suggest that AIM-NASH may assist pathologists in histologic review of NASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient therapeutic response.

Integration of deep learning-based histopathology and transcriptomics reveals key genes associated with fibrogenesis in patients with advanced NASH.

Nonalcoholic steatohepatitis (NASH) is the most common chronic liver disease globally and a leading cause for liver transplantation in the US. Its pathogenesis remains imprecisely defined. We combined two high-resolution modalities to tissue samples from NASH clinical trials, machine learning (ML)-based quantification of histological features and transcriptomics, to identify genes that are associated with disease progression and clinical events. A histopathology-driven 5-gene expression signature predicted disease progression and clinical events in patients with NASH with F3 (pre-cirrhotic) and F4 (cirrhotic) fibrosis. Notably, the Notch signaling pathway and genes implicated in liver-related diseases were enriched in this expression signature. In a validation cohort where pharmacologic intervention improved disease histology, multiple Notch signaling components were suppressed.

Association of artificial intelligence-powered and manual quantification of programmed death-ligand 1 (PD-L1) expression with outcomes in patients treated with nivolumab ± ipilimumab.

Assessment of programmed death ligand 1 (PD-L1) expression by immunohistochemistry (IHC) has emerged as an important predictive biomarker across multiple tumor types. However, manual quantitation of PD-L1 positivity can be difficult and leads to substantial inter-observer variability. Although the development of artificial intelligence (AI) algorithms may mitigate some of the challenges associated with manual assessment and improve the accuracy of PD-L1 expression scoring, use of AI-based approaches to oncology biomarker scoring and drug development has been sparse, primarily due to the lack of large-scale clinical validation studies across multiple cohorts and tumor types. We developed AI-powered algorithms to evaluate PD-L1 expression on tumor cells by IHC and compared it with manual IHC scoring in urothelial carcinoma, non-small cell lung cancer, melanoma, and squamous cell carcinoma of the head and neck (prospectively determined during the phase II and III CheckMate clinical trials). 1,746 slides were retrospectively analyzed, the largest investigation of digital pathology algorithms on clinical trial datasets performed to date. AI-powered quantification of PD-L1 expression on tumor cells identified more PD-L1-positive samples compared with manual scoring at cutoffs of ≥1% and ≥5% in most tumor types. Additionally, similar improvements in response and survival were observed in patients identified as PD-L1-positive compared with PD-L1-negative using both AI-powered and manual methods, while improved associations with survival were observed in patients with certain tumor types identified as PD-L1-positive using AI-powered scoring only. Our study demonstrates the potential for implementation of digital pathology-based methods in future clinical practice to identify more patients who would benefit from treatment with immuno-oncology therapy compared with current guidelines using manual assessment.

Telling the difference and the telling differences between hospital in the home and outpatient parenteral antibiotic therapy.

Doctors, authors, funders and hospital managers should take care to distinguish the important differences between hospital in the home (HIH) and outpatient parenteral antimicrobial therapy (OPAT) services. HIH is an inpatient service delivered at home usually by (or on behalf of) hospitals, which aims to substitute for a traditional inpatient stay. It does so by delivering a wide range of hospital treatments to patients at home, or residential aged care, using hospital medical and nursing staff, delivery technologies and venous access, pharmacy, radiology and pathology, and a structured system of on call and governance. OPAT is an outpatient service, usually run through infectious diseases physicians' offices or departments. Most care is delivered in infusion centres and requires patients to travel for their care. Generally, there is no after-hours support. HIH has supplanted the role of OPAT due to improved governance and a wider clinical and severity scope. HIH is accessible from hospital emergency departments or directly from residential aged care facilities. Inpatient capacity has been expanded during the COVID-19 pandemic. There is evidence that both HIH and OPAT can successfully treat their selected patient groups. There are no head-to-head studies, but in observational comparisons there might be more adverse drug events in OPAT. OPAT places a greater onus of care, supervision and travel needs on the patient and family. Where HIH is not available, OPAT may remain an alternative for some patients. However, HIH seeks to redefine the delivery of inpatient care away from the location of care.

A research agenda for hospital at home.

BACKGROUND: Hospital at home (HaH) provides hospital-level care at home as a substitute for traditional hospital care. Interest in HaH is increasing markedly. While multiple studies of HaH have demonstrated that HaH provides safe, high-quality, cost-effective care, there remain many unanswered research questions. The objective of this study is to develop a research agenda to guide future HaH-related research. METHODS: Survey of attendees of first World HaH Congress 2019 for input on research for the future HaH development. Selection and ranking of important topic areas for future HaH-related research. Development of research domains and research questions and issues using grounded theory approach, supplemented by focused literature reviews. RESULTS: 240 conference attendees responded to the survey (response rate, 55.3%). The majority were from Europe (64%) and North America (11%) and were HaH program leaders (29%), HaH physicians (27%), and researchers (13%). Nine research domains for future HaH research were identified: 1) definition of the HaH model of care; 2) the HaH clinical model; 3) measurement and outcomes of HaH; 4) patient and caregiver experience with HaH; 5) education and training of HaH clinicians; 6) technology and telehealth for HaH; 7) regulatory and payment issues in HaH; 8) implementation and scaling of HaH; and 9) ethical issues in HaH. Key research issues and questions were identified for each domain. CONCLUSIONS: While highly evidence-based, unanswered research questions regarding HaH remain, focusing research efforts on the domains identified in this study will serve to improve HaH for all key HaH stakeholders.

Digital pathology and artificial intelligence in translational medicine and clinical practice.

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

The development of a home-based therapeutic platform for multiple myeloma.

BACKGROUND: Multiple Myeloma (MM) accounts for 1-2% of all malignancies but is the second most common hematological malignancy. It is characterized by a proliferation of malignant plasma cells. The treatment paradigm of MM in Australia is traditionally hospital-based, complex, and costly. While MM comprises 1-2% of cancer diagnoses, it appears in the top 10 cancer diagnoses requiring hospital admission. The cumulative time spent receiving treatment is a significant burden for patients. The ability to receive treatment at home and maximize time away from hospital-based settings is a key preference for patients receiving anticancer therapies over a prolonged period of time. METHODS: The Peter MacCallum Cancer Centre and Royal Melbourne Hospital's combined Clinical Hematology Unit has collaborated with their Hospital in the Home departments to develop several innovative programs to address this. RESULTS: We describe our current active programs and potential developments in home-based MM therapy. CONCLUSION: We have enabled large numbers of patients to receive complex therapies in their own home and the COVID-19 pandemic has increased the pace of the roll out without any compromise in safety. We anticipate that the next raft of immunotherapies will be able to transition into the @Home treatment setting in the coming years.

A Machine Learning Approach to Liver Histological Evaluation Predicts Clinically Significant Portal Hypertension in NASH Cirrhosis.

BACKGROUND AND AIMS: The hepatic venous pressure gradient (HVPG) is the standard for estimating portal pressure but requires expertise for interpretation. We hypothesized that HVPG could be extrapolated from liver histology using a machine learning (ML) algorithm. APPROACH AND RESULTS: Patients with NASH with compensated cirrhosis from a phase 2b trial were included. HVPG and biopsies from baseline and weeks 48 and 96 were reviewed centrally, and biopsies evaluated with a convolutional neural network (PathAI, Boston, MA). Using trichrome-stained biopsies in the training set (n = 130), an ML model was developed to recognize fibrosis patterns associated with HVPG, and the resultant ML HVPG score was validated in a held-out test set (n = 88). Associations between the ML HVPG score with measured HVPG and liver-related events, and performance of the ML HVPG score for clinically significant portal hypertension (CSPH) (HVPG ≥ 10 mm Hg), were determined. The ML-HVPG score was more strongly correlated with HVPG than hepatic collagen by morphometry (ρ = 0.47 vs. ρ = 0.28; P < 0.001). The ML HVPG score differentiated patients with normal (0-5 mm Hg) and elevated (5.5-9.5 mm Hg) HVPG and CSPH (median: 1.51 vs. 1.93 vs. 2.60; all P < 0.05). The areas under receiver operating characteristic curve (AUROCs) (95% CI) of the ML-HVPG score for CSPH were 0.85 (0.80, 0.90) and 0.76 (0.68, 0.85) in the training and test sets, respectively. Discrimination of the ML-HVPG score for CSPH improved with the addition of a ML parameter for nodularity, Enhanced Liver Fibrosis, platelets, aspartate aminotransferase (AST), and bilirubin (AUROC in test set: 0.85; 95% CI: 0.78, 0.92). Although baseline ML-HVPG score was not prognostic, changes were predictive of clinical events (HR: 2.13; 95% CI: 1.26, 3.59) and associated with hemodynamic response and fibrosis improvement. CONCLUSIONS: An ML model based on trichrome-stained liver biopsy slides can predict CSPH in patients with NASH with cirrhosis.

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association.

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.

Hospital in the home-based management of acute diverticulitis: a safe and effective alternative to inpatient care.

BACKGROUND: The traditional approach to management of acute uncomplicated diverticulitis involves admission to hospital, antibiotic therapy, gut rest and monitoring for the development of complications. Despite evidence to suggest this can safely be performed in an outpatient setting, inpatient care remains standard practice in Australia potentially due to a variety of factors (van Dijk et al. 2018; Cirocchi et al. 2019). Hospital in the home (HIH) allows patients requiring complex care including intravenous antibiotics, intravenous fluids and complex pain relief to be managed at home. This study examined the safety and efficacy of HIH-based care for acute diverticulitis over a 16-year period. METHODOLOGY: A retrospective review of cases of acute diverticulitis managed under our HIH service from the period of 1st of January 2004 to 20th of October 2020 was completed. Baseline descriptive data relating to age, co-morbidities and severity of diverticulitis was collected. Details of medical treatment provided and subsequent complications were also collected. RESULTS: During the study period, 23 patients with acute diverticulitis were treated under the HIH unit. Among the study population, the median age was 60 (interquartile range 15) with a slight female predominance (n = 13, 56.5%). This represented the first presentation in 60.9% of patients. Average length of stay was 3.6 days (SD = 1.0) with no acute complications recorded in the study period. Two patients (8.7%) had further episodes of acute diverticulitis within 60 days. CONCLUSION: In this study, the lack of complications demonstrated indicates that HIH-based management of acute diverticulitis may be a viable and safe alternative to inpatient care.

Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotypes.

Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures (AUROC 0.601-0.864), including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to 'black-box' methods. Our HIF-based approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association.

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH.

BACKGROUND AND AIMS: Manual histological assessment is currently the accepted standard for diagnosing and monitoring disease progression in NASH, but is limited by variability in interpretation and insensitivity to change. Thus, there is a critical need for improved tools to assess liver pathology in order to risk stratify NASH patients and monitor treatment response. APPROACH AND RESULTS: Here, we describe a machine learning (ML)-based approach to liver histology assessment, which accurately characterizes disease severity and heterogeneity, and sensitively quantifies treatment response in NASH. We use samples from three randomized controlled trials to build and then validate deep convolutional neural networks to measure key histological features in NASH, including steatosis, inflammation, hepatocellular ballooning, and fibrosis. The ML-based predictions showed strong correlations with expert pathologists and were prognostic of progression to cirrhosis and liver-related clinical events. We developed a heterogeneity-sensitive metric of fibrosis response, the Deep Learning Treatment Assessment Liver Fibrosis score, which measured antifibrotic treatment effects that went undetected by manual pathological staging and was concordant with histological disease progression. CONCLUSIONS: Our ML method has shown reproducibility and sensitivity and was prognostic for disease progression, demonstrating the power of ML to advance our understanding of disease heterogeneity in NASH, risk stratify affected patients, and facilitate the development of therapies.

Home ward bound: features of hospital in the home use by major Australian hospitals, 2011-2017.

OBJECTIVE: To describe uptake of hospital in the home (HIH) by major Australian hospitals and the characteristics of patients and their HIH admissions; to assess change in HIH admission numbers relative to total hospital activity. DESIGN: Descriptive, retrospective study of HIH activity, analysing previously collected census data for all multi-day hospital inpatient admissions to included hospitals during the period 1 January 2011 - 31 December 2017. SETTING, PARTICIPANTS: Nineteen principal referrer hospital members of the Health Roundtable in Australia. MAIN OUTCOME MEASURES: HIH admissions by diagnosis-related group (DRG); patient and admission characteristics. RESULTS: 80 167 of 2 185 421 admissions to the 19 hospitals included HIH care, or 3.7% (95% CI, 3.6-3.7%) of all admissions. Median length of stay for admissions including HIH (7.3 days; IQR, 3.1-14 days) was longer than that for those that did not (2.7 days; IQR, 1.6-5.1 days). For HIH admissions, the proportion of men was higher (54.4% v 45.9%), the proportion of patients who died in hospital was lower (0.3% v 1.4%), and re-admission within 28 days was less frequent (2.3% v 3.6%). The 50 DRGs with greatest HIH activity encompassed 65 811 HIH admissions (82.1%), or 8.4% (95% CI, 8.4-8.5%) of all admissions in these DRGs. HIH admission numbers grew more rapidly than non-HIH admissions, but the difference was not statistically significant. CONCLUSIONS: HIH care is most frequently provided to patients requiring hospital treatment related to infections, venous thromboembolism, or post-surgical care. Its use could be expanded in clinical areas where it is currently used, and extended to others where it is not. HIH activity is growing. It should be systematically monitored and reported to allow better overview of its use and outcomes.

Temperature profiles of antibiotic-containing elastomeric infusion devices used by ambulatory care patients.

PURPOSE: The temperature profiles of antibiotic-containing elastomeric infusion devices used by ambulatory care patients under various environmental conditions were evaluated. METHODS: A prospective, descriptive survey of temperature exposure was conducted in 4 publically funded hospitals. Over a 12-month period, electronic temperature-recording devices were attached to the antibiotic infusion devices (infusers) of prospectively randomized hospital-in-the-home (HITH) participants. Temperatures were recorded immediately after infuser connection and every 5 minutes thereafter for 24 hours. A structured data collection form was used to collect information on basic clinical and demographic characteristics and aspects of daily living (i.e., how and where the infuser was carried during the day, times the participant went to and arose from bed, location of the infuser while sleeping, and dates and times the infuser was connected and disconnected). RESULTS: A total of 115 patients successfully completed the study (17-91 years old, 55% males). A total of 31,298 temperature readings were collected. The storage location of the infuser did not influence daytime readings. However, the overnight storage location did have a significant impact on the temperatures recorded overnight. The mean temperatures of infusers stored on the bed or on the body overnight were significantly higher than those for infusers stored away from the bed. Diurnal and seasonal influences were also detected. Significantly warmer temperatures were recorded in afternoons and evenings and during the summer months. CONCLUSION: Antibiotics administered to HITH patients via continuous infusion were frequently exposed to temperatures in excess of 25 °C. Specific patient behaviors and seasonal and chronological factors influenced temperatures. The findings challenge the validity of current fixed-temperature models for testing stability, which do not reflect conditions found in clinical practice.