An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings.

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 < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2). Conclusions: Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer.

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Computational pathology identifies immune-mediated collagen disruption to predict clinical outcomes in gynecologic malignancies.

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.

The role of cells that are part of our immune system in altering the structure of the protein collagen within cancers is not fully understood, particularly within cancers that affect women such as ovarian, cervical and uterine cancers. Here, we developed a computer-based method called CollaTIL to explore how immune cells influence collagen in these tumors and affect their growth. We found that a higher presence of immune cells leads to less organized collagen in the tumor. Conversely, when there are fewer immune cells, the collagen tends to be more structured. Additionally, CollaTIL identifies patterns that predict patient outcomes in these cancers. These findings not only enhance our understanding of tumor biology but also may be useful in helping clinicians to predict which patients are at risk of their disease progressing.

Recurrent Oropharyngeal Squamous Cell Carcinomas Maintain Anti-tumor Immunity and Multinucleation Levels Following Completion of Radiation.

OBJECTIVE: Oropharyngeal squamous cell carcinoma (OPSCC) recurrence is almost universally fatal. Development of effective therapeutic options requires an improved understanding of recurrent OPSCC biology. METHODS: We analyzed paired primary-recurrent OPSCC from Veterans treated at the Michael E. DeBakey Veterans Affairs Medical Center between 2000 and 2020 who received curative intent radiation-based treatment (with or without chemotherapy). Patient tumors were analyzed using standard immunohistochemistry and automated imaging of infiltrating lymphocytes and multinucleated tumor cells coupled to machine learning algorithms. RESULTS: Primary and recurrent tumors demonstrated high concordance via p16 and p53 immunohistochemistry, with comparable levels of multinucleation. In contrast, recurrent tumors demonstrated significantly higher levels of CD8+ tumor infiltrating lymphocytes (p<0.05) and higher levels of PD-L1 expression (p<0.05). CONCLUSION: Exposure to chemo-radiation and recurrence following treatment preserves critical features of intrinsic tumor biology and the tumor immune microenvironment suggesting that novel treatment regimens may be as effective in the salvage setting as in the definitive intent setting.

Recurrent oropharyngeal squamous cell carcinomas maintain anti-tumor immunity and multinucleation levels following completion of radiation.

Objective: Oropharyngeal squamous cell carcinoma (OPSCC) recurrence is almost universally fatal. Development of effective therapeutic options requires an improved understanding of recurrent OPSCC biology. Methods: We analyzed paired primary-recurrent OPSCC from Veterans treated at the Michael E. DeBakey Veterans Affairs Medical Center between 2000 and 2020 who received curative intent radiation-based treatment (with or without chemotherapy). Patient tumors were analyzed using standard immunohistochemistry and automated imaging of infiltrating lymphocytes and multinucleated tumor cells coupled to machine learning algorithms. Results: Primary and recurrent tumors demonstrated high concordance via p16 and p53 immunohistochemistry, with comparable levels of multinucleation. In contrast, recurrent tumors demonstrated significantly higher levels of CD8+ tumor infiltrating lymphocytes (p<0.05) and higher levels of PD-L1 expression (p<0.05). Conclusion: Exposure to chemo-radiation and recurrence following treatment does not appear deleterious to underlying biological characteristics and anti-tumor immunity of oropharyngeal cancer, suggesting that novel treatment regimens may be as effective in the salvage setting as in the definitive intent setting.

Spatial analyses of immune cell infiltration in cancer: current methods and future directions: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer.

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector-based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well-described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland.

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: A report of the International Immuno-Oncology Biomarker Working Group on Breast Cancer

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives.

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Machine learning driven index of tumor multinucleation correlates with survival and suppressed anti-tumor immunity in head and neck squamous cell carcinoma patients.

OBJECTIVES: Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach. MATERIALS AND METHODS: Hematoxylin and eosin images from an institutional OPSCC cohort formed the training set (DTr). TCGA HNSCC patients (oral cavity, oropharynx and larynx/hypopharynx) formed the validation set (DV). Deep learning models were trained in DTr to calculate a multinucleation index (MuNI) score. Gene set enrichment analysis (GSEA) was then used to explore correlations between MuNI and tumor biology. RESULTS: MuNI correlated with overall survival. A multivariable nomogram that included MuNI, age, race, sex, T/N stage, and smoking status yielded a C-index of 0.65, and MuNI was prognostic of overall survival (2.25, 1.07-4.71, 0.03), independent of the other variables. High MuNI scores correlated with depletion of effector immunocyte subsets across all HNSCC sites independent of HPV and TP53 mutational status although the correlations were strongest in wild-type TP53 tumors potentially due to aberrant mitotic events and activation of DNA-repair mechanisms. CONCLUSION: MuNI is associated with survival in HNSCC across subsites. This may be driven by an association between high levels of multinucleation and a suppressive (potentially exhausted) tumor immune microenvironment. Mechanistic studies examining the link between multinucleation and tumor immunity will be required to characterize biological drivers of multinucleation and their impact on treatment response and outcomes.

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer.

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).

Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

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.

Image analysis reveals differences in tumor multinucleations in Black and White patients with human papillomavirus-associated oropharyngeal squamous cell carcinoma.

BACKGROUND: Understanding biological differences between different racial groups of human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) patients, who have differences in terms of incidence, survival, and tumor morphology, can facilitate accurate prognostic biomarkers, which can help develop personalized treatment strategies. METHODS: This study evaluated whether there were morphologic differences between HPV-associated tumors from Black and White patients in terms of multinucleation index (MuNI), an image analysis-derived metric that measures density of multinucleated tumor cells within epithelial regions on hematoxylin-eosin images and previously has been prognostic in HPV-associated OPSCC patients. In this study, the authors specifically evaluated whether the same MuNI cutoff that was prognostic of overall survival (OS) and disease-free survival in their previous study, TTR , is valid for Black and White patients, separately. We also evaluated population-specific cutoffs, TB for Blacks and TW for Whites, for risk stratification. RESULTS: MuNI was statistically significantly different between Black (mean, 3.88e-4; median, 3.67e-04) and White patients (mean, 3.36e-04; median, 2.99e-04), with p = .0078. Using TTR , MuNI was prognostic of OS in the entire population with hazard ratio (HR) of 1.71 (p = .002; 95% confidence interval [CI], 1.21-2.43) and in White patients with HR of 1.72 (p = .005; 95% CI, 1.18-2.51). Population-specific cutoff, TW , yielded improved HR of 1.77 (p = .003; 95% CI, 1.21-2.58) for White patients, whereas TB did not improve risk-stratification in Black patients with HR of 0.6 (p = .3; HR, 0.6; 95% CI, 0.2-1.80). CONCLUSIONS: Histological difference between White and Black patient tumors in terms of multinucleated tumor cells suggests the need for considering population-specific prognostic biomarkers for personalized risk stratification strategies for HPV-associated OPSCC patients.

Role of tumor infiltrating lymphocytes and spatial immune heterogeneity in sensitivity to PD-1 axis blockers in non-small cell lung cancer.

BACKGROUND: Tumor infiltrating lymphocytes (TILs) reflect adaptive antitumor immune responses in cancer and are generally associated with favorable prognosis. However, the relationships between TILs subsets and their spatial arrangement with clinical benefit from immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC) remains less explored. METHODS: We used multiplexed quantitative immunofluorescence panels to determine the association of major TILs subpopulations, CD8+ cytotoxic T cells, CD4+ helper T cells and CD20+ B cells, and T cell exhaustion markers, programmed cell death protein-1 (PD-1),lymphocyte-activation gene 3 (LAG-3) and T cell immunoglobulin mucin-3 (TIM-3) with outcomes in a multi-institutional cohort of baseline tumor samples from 179 patients with NSCLC treated with ICI. The analysis of full-face tumor biopsies including numerous fields of view allowed a detailed spatial analysis and assessment of tumor immune heterogeneity using a multiparametric quadratic entropy metric (Rao's Q Index (RQI)). RESULTS: TILs were preferentially located in the stromal tissue areas surrounding tumor-cell nests and CD8+ T cells were the most abundant subset. Higher density of stromal CD8+ cytotoxic T cells was significantly associated with longer survival, and this effect was more prominent in programmed death ligand-1 (PD-L1) positive cases. The role of baseline T cell infiltration to stratify PD-L1 expressing cases was confirmed measuring the T cell receptor-burden in an independent NSCLC cohort studied with whole-exome DNA sequencing. High levels of LAG-3 on T cells or elevated RQI heterogeneity index were associated with worse survival in the cohort. CONCLUSION: Baseline T cell density and T cell exhaustion marker expression can stratify outcomes in PD-L1 positive patients with NSCLC treated with ICI. Spatial immune heterogeneity can be measured using the RQI and is associated with survival in NSCLC.

A machine learning model for separating epithelial and stromal regions in oral cavity squamous cell carcinomas using H&E-stained histology images: A multi-center, retrospective study.

OBJECTIVE: Tissue slides from Oral cavity squamous cell carcinoma (OC-SCC), particularly the epithelial regions, hold morphologic features that are both diagnostic and prognostic. Yet, previously developed approaches for automated epithelium segmentation in OC-SCC have not been independently tested in a multi-center setting. In this study, we aimed to investigate the effectiveness and applicability of a convolutional neural network (CNN) model to perform epithelial segmentation using digitized H&E-stained diagnostic slides from OC-SCC patients in a multi-center setting. METHODS: A CNN model was developed to segment the epithelial regions of digitized slides (n = 810), retrospectively collected from five different centers. Deep learning models were trained and validated using well-annotated tissue microarray (TMA) images (n = 212) at various magnifications. The best performing model was locked down and used for independent testing with a total of 478 whole-slide images (WSIs). Manually annotated epithelial regions were used as the reference standard for evaluation. We also compared the model generated results with IHC-stained epithelium (n = 120) as the reference. RESULTS: The locked-down CNN model trained on the TMA image training cohorts with 10x magnification achieved the best segmentation performance. The locked-down model performed consistently and yielded Pixel Accuracy, Recall Rate, Precision Rate, and Dice Coefficient that ranged from 95.8% to 96.6%, 79.1% to 93.8%, 85.7% to 89.3%, and 82.3% to 89.0%, respectively for the three independent testing WSI cohorts. CONCLUSION: The automated model achieved a consistently accurate performance for automated epithelial region segmentation compared to manual annotations. This model could be integrated into a computer-aided diagnosis or prognosis system.

Image analysis reveals molecularly distinct patterns of TILs in NSCLC associated with treatment outcome.

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.

Machine Learning to Predict Risk of Relapse Using Cytologic Image Markers in Patients With Acute Myeloid Leukemia Posthematopoietic Cell Transplantation.

PURPOSE: Allogenic hematopoietic stem-cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML) and myelodysplastic syndrome (MDS). Relapse post-HCT is the most common cause of treatment failure and is associated with a poor prognosis. Pathologist-based visual assessment of aspirate images and the manual myeloblast counting have shown to be predictive of relapse post-HCT. However, this approach is time-intensive and subjective. The premise of this study was to explore whether computer-extracted morphology and texture features from myeloblasts' chromatin patterns could help predict relapse and prognosticate relapse-free survival (RFS) after HCT. MATERIALS AND METHODS: In this study, Wright-Giemsa-stained post-HCT aspirate images were collected from 92 patients with AML/MDS who were randomly assigned into a training set (St = 52) and a validation set (Sv = 40). First, a deep learning-based model was developed to segment myeloblasts. A total of 214 texture and shape descriptors were then extracted from the segmented myeloblasts on aspirate slide images. A risk score on the basis of texture features of myeloblast chromatin patterns was generated by using the least absolute shrinkage and selection operator with a Cox regression model. RESULTS: The risk score was associated with RFS in St (hazard ratio = 2.38; 95% CI, 1.4 to 3.95; P = .0008) and Sv (hazard ratio = 1.57; 95% CI, 1.01 to 2.45; P = .044). We also demonstrate that this resulting signature was predictive of AML relapse with an area under the receiver operating characteristic curve of 0.71 within Sv. All the relevant code is available at GitHub. CONCLUSION: The texture features extracted from chromatin patterns of myeloblasts can predict post-HCT relapse and prognosticate RFS of patients with AML/MDS.

The state of the art for artificial intelligence in lung digital pathology.

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Oropharyngeal cancer outcomes correlate with p16 status, multinucleation and immune infiltration.

Oropharyngeal squamous cell carcinoma (OPSCC), largely fueled by the human papillomavirus (HPV), has a complex biological and immunologic phenotype. Although HPV/p16 status can be used to stratify OPSCC patients as a function of survival, it remains unclear what drives an improved treatment response in HPV-associated OPSCC and whether targetable biomarkers exist that can inform a precision oncology approach. We analyzed OPSCC patients treated between 2000 and 2016 and correlated locoregional control (LRC), disease-free survival (DFS) and overall survival (OS) with conventional clinical parameters, risk parameters generated using deep-learning algorithms trained to quantify tumor-infiltrating lymphocytes (TILs) (OP-TIL) and multinucleated tumor cells (MuNI) and targeted transcriptomics. P16 was a dominant determinant of LRC, DFS and OS, but tobacco exposure, OP-TIL and MuNI risk features correlated with clinical outcomes independent of p16 status and the combination of p16, OP-TIL and MuNI generated a better stratification of OPSCC risk compared to individual parameters. Differential gene expression (DEG) analysis demonstrated overlap between MuNI and OP-TIL and identified genes involved in DNA repair, oxidative stress response and tumor immunity as the most prominent correlates with survival. Alteration of inflammatory/immune pathways correlated strongly with all risk features and oncologic outcomes. This suggests that development of OPSCC consists of an intersection between multiple required and permissive oncogenic and immunologic events which may be mechanistically linked. The strong relationship between tumor immunity and oncologic outcomes in OPSCC regardless of HPV status may provide opportunities for further biomarker development and precision oncology approaches incorporating immune checkpoint inhibitors for maximal anti-tumor efficacy.

Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities.

BACKGROUND: We present a computational approach (ArcTIL) for quantitative characterization of the architecture of tumor-infiltrating lymphocytes (TILs) and their interplay with cancer cells from digitized H&E-stained histology whole slide images and evaluate its prognostic role in three different gynecological cancer (GC) types and across three different treatment types (platinum, radiation and immunotherapy). METHODS: In this retrospective study, we included 926 patients with GC diagnosed with ovarian cancer (OC), cervical cancer, and endometrial cancer with available digitized diagnostic histology slides and survival outcome information. ArcTIL features quantifying architecture and spatial interplay between immune cells and the rest of nucleated cells (mostly comprised cancer cells) were extracted from the cell cluster graphs of nuclei within the tumor epithelial nests, surrounding stroma and invasive tumor front compartments on H&E-stained slides. A Cox proportional hazards model, incorporating ArcTIL features was fit on the OC training cohort (N=51), yielding an ArcTIL signature. A unique threshold learned from the training set stratified the patients into a low and high-risk group. RESULTS: The seven feature ArcTIL classifier was found to significantly correlate with overall survival in chemotherapy and radiotherapy-treated validation cohorts and progression-free survival in an immunotherapy-treated validation cohort. ArcTIL features relating to increased density of TILs in the epithelium and invasive tumor front were found to be associated with better survival outcomes when compared with those patients with an increased TIL density in the stroma. A statistically significant association was found between the ArcTIL signature and signaling pathways for blood vessel morphogenesis, vasculature development, regulation of cell differentiation, cell-substrate adhesion, biological adhesion, regulation of vasculature development, and angiogenesis. CONCLUSIONS: This study reveals that computationally-derived features from the spatial architecture of TILs and tumor cells are prognostic in GCs treated with chemotherapy, radiotherapy, and checkpoint blockade and are closely associated with central biological processes that impact tumor progression. These findings could aid in identifying therapy-refractory patients and further enable personalized treatment decision-making.

An Imaging Biomarker of Tumor-Infiltrating Lymphocytes to Risk-Stratify Patients With HPV-Associated Oropharyngeal Cancer.

BACKGROUND: Human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPSCC) has excellent control rates compared to nonvirally associated OPSCC. Multiple trials are actively testing whether de-escalation of treatment intensity for these patients can maintain oncologic equipoise while reducing treatment-related toxicity. We have developed OP-TIL, a biomarker that characterizes the spatial interplay between tumor-infiltrating lymphocytes (TILs) and surrounding cells in histology images. Herein, we sought to test whether OP-TIL can segregate stage I HPV-associated OPSCC patients into low-risk and high-risk groups and aid in patient selection for de-escalation clinical trials. METHODS: Association between OP-TIL and patient outcome was explored on whole slide hematoxylin and eosin images from 439 stage I HPV-associated OPSCC patients across 6 institutional cohorts. One institutional cohort (n = 94) was used to identify the most prognostic features and train a Cox regression model to predict risk of recurrence and death. Survival analysis was used to validate the algorithm as a biomarker of recurrence or death in the remaining 5 cohorts (n = 345). All statistical tests were 2-sided. RESULTS: OP-TIL separated stage I HPV-associated OPSCC patients with 30 or less pack-year smoking history into low-risk (2-year disease-free survival [DFS] = 94.2%; 5-year DFS = 88.4%) and high-risk (2-year DFS = 82.5%; 5-year DFS = 74.2%) groups (hazard ratio = 2.56, 95% confidence interval = 1.52 to 4.32; P < .001), even after adjusting for age, smoking status, T and N classification, and treatment modality on multivariate analysis for DFS (hazard ratio = 2.27, 95% confidence interval = 1.32 to 3.94; P = .003). CONCLUSIONS: OP-TIL can identify stage I HPV-associated OPSCC patients likely to be poor candidates for treatment de-escalation. Following validation on previously completed multi-institutional clinical trials, OP-TIL has the potential to be a biomarker, beyond clinical stage and HPV status, that can be used clinically to optimize patient selection for de-escalation.