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Background: Breast cancer is the most common cancer worldwide. There has been emerging interest in studying differences in breast tumor phenotypes between South Asian and South American women given that racial/ethnic disparities in incidence and mortality have been demonstrated in multiple studies. South Asian women are more likely to be diagnosed with an advanced stage breast tumor despite lower incidence than in North American women. There is evidence that computer-extracted nuclear morphological features on H&E slide images may be associated with breast cancer aggressiveness, specifically recurrence and disease free survival. However, studies have mostly focused on North American women. In this work, we evaluated whether there is a difference in computer extracted features of nuclear morphology from H&E tissue slide images between South Asian (SA) and North American (NA) women and we also investigated how these differences could impact the development of population specific breast cancer prognostic models. Methods: H&E slides of breast tumors from patients who were diagnosed with ER+ early stage invasive breast cancer from Tata Memorial Centre, India (SA: 69 (20 recurrence)) and from University Hospitals Cleveland Medical Center (NA: 121 (20 recurrence)), along with outcome information were collected. All slides were digitized on either a Ventana DP 200 or a Roche Ventana iScan HT slide scanner. For each image, a conditional Generative Adversarial Network model was employed to segment the individual nuclei, which were used to generate 241 nuclear features including nuclear architecture, shape, orientation disorder, and texture features. Half of the patients (95) were randomly selected as the training set (Stra) with the remaining patients as the hold-out validation set (Stest). Three elastic net regularized Cox regression models (MSA, MNA, MSA+NA) were trained fitting between the nuclear features and disease free survival (DFS) respectively for SA subset, NA subset, and SA & NA set in Stra. The top five prognostic features were identified respectively from each of the three models (MSA, MNA, MSA+NA) which were further validated on Stest to evaluate their prognostic value in prediction of DFS. Results: We found that the prognostic features identified by MNA and MSA+NA were mostly shape features (three out of five for MNA, four out of five for MSA+NA), while the prognostic features identified by the model specifically trained with the SA population (MSA) were mostly texture features (three out of five), possibly reflecting chromatin patterns in the cell. MSA yielded a better performance (Hazard Ratio=4.99 (p=0.00928, CI=1.32-18.9) from log-rank test between model derived high and low risk categories) on SA population in Stest compared to MNA and MSA+NA. Conclusion: We found that nuclear histomorphometry features were different between breast cancer patients from South Asia and those from North America. The prognostic capability of the computational pathology-based models for South Asian women could be significantly improved by taking into account population-specific information. An additional independent validation set is needed to confirm the preliminary findings presented here. Citation Format: Haojia Li, Kaustav Bera, Paula Toro, Pingfu Fu, Vidya Rao, Shabina Siddique, Aparna Harbhajanka, Haley Sechrist, Zelin Zhang, Sangeeta Desai, Vani Parmar, Anant Madabhushi. Computerized image analysis of nuclear morphological features reveals differences in phenotype and prognosis of disease free survival of early stage ER+ breast cancers for South Asian and North American women [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-45.

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman's capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman's capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.

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.

Early detection of esophageal neoplasia enables curative endoscopic therapy, but the current diagnostic standard of care has low sensitivity because early neoplasia is often inconspicuous with conventional white light endoscopy. Here we hypothesized that spectral endoscopy could enhance contrast for neoplasia in surveillance of patients with Barrett’s esophagus (BE). A custom spectral endoscope was deployed in a pilot clinical study of 20 patients to capture 715 in vivo tissue spectra matched with gold standard diagnosis from histopathology. Spectral endoscopy was sensitive to changes in neovascularization during the progression of disease; both non-dysplastic and neoplastic BE showed higher blood volume relative to healthy squamous tissue (p = 0.001 and 0.02, respectively), and vessel radius appeared larger in neoplasia relative to non-dysplastic BE (p = 0.06). We further developed a deep learning algorithm capable of classifying spectra of neoplasia vs. non-dysplastic BE with high accuracy (84.8% accuracy, 83.7% sensitivity, 85.5% specificity, 78.3% positive predictive value and 89.4% negative predictive value). Exploiting the newly acquired library of labeled spectra to model custom color filter sets identified a potential 12-fold enhancement in contrast between neoplasia and non-dysplastic BE using application-specific color filters compared to standard-of-care white light imaging (perceptible color difference = 32.4, 2.7 respectively). This work demonstrates the potential of endoscopic spectral imaging to extract vascular properties in BE, to classify disease stages using deep learning, and to enable high-contrast endoscopy.

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 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.

BACKGROUND: Patients with p16(+) oropharyngeal squamous cell carcinoma (OPSCC) are potentially cured with definitive treatment. However, there are currently no reliable biomarkers of treatment failure for p16(+) OPSCC. Pathologist-based visual assessment of tumor cell multinucleation (MN) has been shown to be independently prognostic of disease-free survival (DFS) in p16(+) OPSCC. However, its quantification is time intensive, subjective, and at risk of interobserver variability. METHODS: We present a deep-learning–based metric, the multinucleation index (MuNI), for prognostication in p16(+) OPSCC. This approach quantifies tumor MN from digitally scanned H&E-stained slides. Representative H&E-stained whole-slide images from 1094 patients with previously untreated p16(+) OPSCC were acquired from 6 institutions for optimization and validation of the MuNI. RESULTS: The MuNI was prognostic for DFS, overall survival (OS), or distant metastasis–free survival (DMFS) in p16(+) OPSCC, with HRs of 1.78 (95% CI: 1.37–2.30), 1.94 (1.44–2.60), and 1.88 (1.43–2.47), respectively, independent of age, smoking status, treatment type, or tumor and lymph node (T/N) categories in multivariable analyses. The MuNI was also prognostic for DFS, OS, and DMFS in patients with stage I and stage III OPSCC, separately. CONCLUSION: MuNI holds promise as a low-cost, tissue-nondestructive, H&E stain–based digital biomarker test for counseling, treatment, and surveillance of patients with p16(+) OPSCC. These data support further confirmation of the MuNI in prospective trials. FUNDING: National Cancer Institute (NCI), NIH; National Institute for Biomedical Imaging and Bioengineering, NIH; National Center for Research Resources, NIH; VA Merit Review Award from the US Department of VA Biomedical Laboratory Research and Development Service; US Department of Defense (DOD) Breast Cancer Research Program Breakthrough Level 1 Award; DOD Prostate Cancer Idea Development Award; DOD Lung Cancer Investigator-Initiated Translational Research Award; DOD Peer-Reviewed Cancer Research Program; Ohio Third Frontier Technology Validation Fund; Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering; Clinical and Translational Science Award (CTSA) program, Case Western Reserve University; NCI Cancer Center Support Grant, NIH; Career Development Award from the US Department of VA Clinical Sciences Research and Development Program; Dan L. Duncan Comprehensive Cancer Center Support Grant, NIH; and Computational Genomic Epidemiology of Cancer Program, Case Comprehensive Cancer Center. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, the US Department of VA, the DOD, or the US Government.

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.

Purpose: We used computerized image analysis and machine learning approaches to characterize spatial arrangement features of the immune response from digitized autopsied H&E tissue images of the lung in coronavirus disease 2019 (COVID-19) patients. Additionally, we applied our approach to tease out potential morphometric differences from autopsies of patients who succumbed to COVID-19 versus H1N1. Approach: H&E lung whole slide images from autopsy specimens of nine COVID-19 and two H1N1 patients were computationally interrogated. 606 image patches ([Formula: see text] per patient) of [Formula: see text] were extracted from the 11 autopsied patient studies. A watershed-based segmentation approach in conjunction with a machine learning classifier was employed to identify two types of nuclei families: lymphocytes and non-lymphocytes (i.e., other nucleated cells such as pneumocytes, macrophages, and neutrophils). Based off the proximity of the individual nuclei, clusters for each nuclei family were constructed. For each of the resulting clusters, a series of quantitative measurements relating to architecture and density of nuclei clusters were calculated. A receiver operating characteristics-based feature selection method, violin plots, and the t-distributed stochastic neighbor embedding algorithm were employed to study differences in immune patterns. Results: In COVID-19, the immune response consistently showed multiple small-size lymphocyte clusters, suggesting that lymphocyte response is rather modest, possibly due to lymphocytopenia. In H1N1, we found larger lymphocyte clusters that were proximal to large clusters of non-lymphocytes, a possible reflection of increased prevalence of macrophages and other immune cells. Conclusion: Our study shows the potential of computational pathology to uncover immune response features that may not be obvious by routine histopathology visual inspection.

The aim of this study was to investigate whether social rank (SR) and stability in the order of entrance to the milking parlour are associated with production traits. The study was conducted on a dairy farm where cows (n = 215) were managed in three groups according to lactation stage (group 1: 78 cows, 0–100 days in milk (DIM); group 2: 65 cows, 101–200 DIM and group 3: 72 cows, >200 DIM). SR was calculated from observations made from agonistic behaviour performed at the water troughs and feed bunks of each pen (n = 3). The animals were classified into three levels of dominance based on at least five clear interactions, resulting in: 61 dominant, 75 intermediate and 69 subordinate cows based on SR. Stability in the order of entry was estimated as the standard deviation of the entry position. SR was weakly correlated with milk yield, urea and protein content in milk. The results showed that stable cows had higher milk production and entered the milking parlour after the non-stable animals. Stability in the order of entry to the milking parlour was not affected by SR. Overall, the use of milking facilities appears to be associated with production traits rather than SR.

The aim of the study was to characterize Chilean consumers of commercial fluid milk and determine if sociodemographic characteristics, knowledge and/or perception on nutritive components of milk affect its consumption. A face-to-face interview was conducted through a stratified random sampling with 501 persons in the Province of Santiago, Metropolitan Region, Chile. The interviews were conducted at the entrance of major supermarkets from 5 different municipalities of the Province of Santiago. Interviews were performed from 10.00 h a.m to 13.00 h p.m. and from 15.00 hours to 17.30 h p.m. every day. Sixteen percent of respondents declared not consuming milk due to animal cruelty followed by self-reported lactose intolerance among other reasons. The most consumed types of milk were whole (25 %), semi-skimmed (24 %) and skimmed (25 %) followed by other types. The majority of respondents that consume milk declared to do it at home (93 %; n = 393), while the rest (7 %; n = 30) declared to consume milk elsewhere (at a coffee shop, restaurant, as a guest, etc.). Participants declared to make their purchase by prioritizing price followed by brand, type of milk, expiration date, nutritional information, packaging, and milk origin. Respondents (86 %) declared to know some nutritional benefits from drinking milk: 47.6 % said that milk was important due to its mineral supply, followed by supply of vitamins (17 %), proteins (17.1 %), fat (13.5 %), sugars (4.5 %) and other components (0.4 %). Most of the answers were associated in some degree with sociodemographic factors. Overall, women declared to consume more skimmed milk whereas, males declared to consume in first place whole milk. Respondents between 18-35 years old declared to consume skimmed and whole milks, while respondents with more than 55 years declared to consume fortified milk followed by semi-skimmed. Consumption of skimmed milk was higher in the high-income socioeconomic class whereas the low-income socioeconomic class resulted in higher consumption of whole milk.

PURPOSE Glioblastoma is an aggressive and universally fatal tumor. Morphological information as captured from cellular regions on surgically resected histopathology slides has the ability to reveal the inherent heterogeneity in Glioblastoma and thus has prognostic implications. In this work, we hypothesized that capturing morphological attributes from high cellularity regions on Hematoxylin and Eosin (H&E)-stained digitized tissue slides using an end-to-end deep-learning pipeline will enable risk-stratification of GBM tumors based on overall survival. METHODS A large multi-cohort study consisting of N=514 H&E-stained digitized tissue slides along with overall-survival data (OS) was obtained from the Ivy Glioblastoma atlas project (Ivy-GAP (N=41)), TCGA (N=379), and CPTAC (N=94). Our deep-learning pipeline consisted of two stages. First stage involved segmenting cellular tumor (CT) from necrotic-regions and background using Resnet-18 model, while the second stage involved predicting OS, using only the segmented CT regions identified in the first stage. For the segmentation stage, we leveraged the Ivy-GAP cohort, where CT annotations confirmed by expert neuropathologists were available, to serve as the training set. Using this training model, the CT regions on the remaining cohort (TCGA, CPTAC) (i.e. test set) were identified. For the survival-prediction stage, the last layer of ResNet18 model was replaced with a cox layer (ResNet-Cox), and further fine-tuned using OS and censor information. Independent validation of ResNet-Cox was performed on two hold-out sites from TCGA and one from CPTAC. RESULTS Our segmentation model achieved an accuracy of 0.89 in reliably identifying CT regions on the validation data. The segmented CT regions on the test cohort were further confirmed by two experts. Our ResNet-Cox model achieved a concordance-index of 0.73 on MD Anderson Cancer Center (N=60), 0.71 on Henry Ford Hospital (N=96), and 0.68 on CPTAC data (N=41). CONCLUSION Deep-learning features captured from cellular tumor of H&E-stained histopathology images may predict survival in Glioblastoma.

584 Background: Approximately 30% of all breast cancers are characterized as triple-negative (TNBC). TNBC typically occurs in younger women and is associated with a poorer prognosis relative to other breast cancer subtypes. High levels of tumor-infiltrating lymphocytes (TILs) in residual disease after Neoadjuvant chemotherapy (NACT) have previously been shown to be associated with better prognosis in TNBC. In this work, we sought to evaluate the prognostic value of computationally derived measures of TIL spatial architecture in residual TNBC after NACT. Methods: H&E-stained samples from 92 patients (pts) with TNBC (41 died, 45 had disease recurrence) and residual disease after NACT were retrospectively collected from 2 sites: Instituto Nacional de Enfermedades Neoplásicas (S1) and University Hospitals (S2). 45 pts (16 deaths, 23 recurrences) from S1 formed the training set and 47 pts (25 deaths, 22 recurrences) from S2 formed the independent validation cohort. Samples were digitized at 20x. Computerized algorithms automatically identified 2 types of nuclei (TILs and non-TILs) and built clusters for each nuclei type based on cell proximity. The spatial arrangement of these clusters was then quantified using network graph metrics. The top 5 features, determined by least absolute shrinkage and selection operator, were used to train a Cox regression model that assigned a risk of death and recurrence to each patient on the training set. The percentile 33 risk score was used as a threshold for stratifying pts on the validation set as either low or high risk. For comparison, we also employed a model based on TIL density alone. Survival analysis was used to evaluate the performance of both approaches on disease-free survival (DFS) and overall survival (OS). Results: Pts in S2 (n=47) identified as “high risk” by the model based on spatial architecture of residual TILs had a significantly shorter survival time. The median OS for pts at high risk was 25 months vs. 55 months for low-risk pts. The median DFS for pts at high risk was 32 months vs 51 months for low-risk pts. Univariable analysis showed this model was prognostic for both OS (Hazard Ratio (HR) = 2.57, 95% Confidence Interval (CI): 1.07-6.16, p=0.03) and DFS (HR=2.38, CI: 1.01-5.62, p=0.04). In contrast, the model based on TIL density was not prognostic for OS (HR=1.24, CI: 0.33-4.63, p=0.73) nor DFS (HR=1.19, CI: 0.32-4.34, p=0.78). Conclusions: A computerized image analysis model based on measurements of spatial arrangement of residual TILs and surrounding cells was found to be prognostic in TNBC pts who received NACT. This method appears to be more prognostic than TIL density alone. Additional multisite validation and multivariable analysis is needed to further establish the independent prognostic utility of TIL based image biomarkers in the post-NACT TNBC.

8572 Background: Small Cell Lung Cancer (SCLC) is an aggressive malignancy with a rapid growth, and Chemotherapy remains mainstay of treatment. Identifying therapeutic targets in SCLC presents a challenge, partially due to a lack of accurate and consistently predictive biomarkers. In this study we sought to evaluate the utility of a combination of computer-extracted radiographic and pathology features from pretreatment baseline CT and H&E biopsy images to predict sensitivity to platinum-based chemotherapy and overall survival (OS) in SCLC. Methods: Seventy-eight patients with extensive and limited-stage SCLC who received platinum-doublet chemotherapy were selected. Objective response to chemotherapy (RECIST criteria) and overall survival (OS) as clinical endpoints were available for 51 and 78 patients respectively. The patients were divided randomly into two sets (Training (Sd), Validation (Sv)) with a constraint (equal number of responders and nonresponders in Sd)—Sd comprised twenty-one patients with SCLC. Sv included thirty patients. CT scans and digitized Hematoxylin Eosin-stained (H&E) biopsy images were acquired for each patient. A set of CT derived (46%) and tissue derived (53%) image features were captured. These included shape and textural patterns of the tumoral and peritumoral regions from CT scans and of tumor regions on H&E images. A random forest feature selection and linear regression model were used to identify the most predictive CT and H&E derived image features associated with chemotherapy response from Sd. A Cox proportional hazard regression model was used with these features to compute a risk score for each patients in Sd. Patients in Sv were stratified into high and low-risk groups based on the median risk score. Kaplan-Meier survival analysis was used to assess the prognostic ability of the risk score on Sv. Results: The risk score comprised nine CT (intra and peri-tumoral texture) and six H&E derived (cancer cell texture and shape) features. A linear regression model in conjunction with these 15 features was significantly associated with chemo-sensitivity in Sv (AUC = 0.76, PRC = 0.81). A multivariable model with these 15 features was significantly associated with OS in Sv (HR = 2.5, 95% CI: 1.3-4.9, P = 0.0043). Kaplan-Meier survival analysis revealed a significantly reduced OS in the high-risk group compared to the low-risk group. Conclusions: A combined CT and H&E tissue derived image signature model predicted response to chemotherapy and improved OS in SCLC patients. Image features from baseline CT scans and H&E tissue slide images may help in better risk stratification of SCLC patients. Additional independent validation of these quantitative image-based biomarkers is warranted.

e12571 Background: Breast cancer is the most common cancer in women and constitutes a public health problem due to its high rates of morbidity and mortality. Diagnosis is made through routine histopathology studies that are complemented by phenotypic expression studies of hormone receptors (estrogen and progesterone receptors), human epidermal growth factor 2 (HER2) and cell proliferation index (Ki67) for their subtyping into molecular groups (luminal A, luminal B, Her2, and triple negative), which have different prognostic and therapeutic implications. Globally, a high prevalence of the Luminal A subtype has been reported, predominantly in North America, Europe, and some Latin American countries; however, the reports in the Colombian population are heterogeneous. The objective of this research is to establish an incidence profile of these molecular subtypes in a population of the coffee region in Colombia. Methods: Samples of 377 patients with a diagnosis of infiltrating breast cancer were collected retrospectively between 2015 and 2018. Inclusion criteria included: 1) Availability of personal data (year of diagnosis and age) 2) Infiltrating breast carcinoma. 3) Diagnosis between 2015 and 2018. 4) Study of complete immunohistochemistry and/or in situ hybridization (ISH) markers. Results: The histological subtypes included: NOS infiltrating ductal carcinomas (339 cases; 89.9 %), infiltrating lobular (23 cases; 6.1 %), infiltrating mucinous (6 cases; 1.5 %), infiltrating papillary (1 case; 0.2 %) and mixed patterns: ductal - lobular (3 cases; 0.7 %) and mucinous - ductal (5 cases; 1.3 %), of whom 56.2% (212 cases) correspond to luminal B, 22.2% (84 cases) to Luminal A, 14.8% (56 cases) to triple negative and 6.6% (25 cases) to HER2. Conclusions: These findings contrast with the prevalence reported worldwide. Therefore, in the population of the Colombian coffee region, this predominance of the molecular subtype luminal B should be considered when establishing prognosis and treatment plan by the medical staff.

6559 Background: While overall, patients with p16+ oropharyngeal squamous cell carcinoma (OPSCC) have a favorable prognosis, subsets of patients experience disease recurrence (DR) and death despite aggressive multimodality treatment. Aside from routine staging criteria, there are no biomarkers of tumor behavior routinely employed in OPSCC to identify patients at higher risk of DR. In this study we sought to evaluate whether the interplay between tumor-infiltrating lymphocytes (TILs) & cancer cells, in both stromal and epithelial compartments from digitized H&E-stained slides, can predict DR in OPSCC patients. Methods: OPSCC resected specimens from 354 patients (66 with DR) were retrospectively collected from 3 different sites. 107 (16 DR) patients from site 1 formed the training set and 247 (50 DR) patients from sites 2 & 3 formed the independent validation cohort. Computerized algorithms automatically identified 4 types of nuclei (TILs & non-TILs in both stromal & epithelial regions), defined clusters for each nuclei type based on cell proximity, and used network graph concepts to capture measurements relating to the arrangement of these clusters. The top 10 features determined by a statistical selection method (LASSO) were used to train a Cox regression model that assigns a risk of DR to each patient on the training set. The median risk score was used as threshold for stratifying patients on the validation set into low and high-risk of DR. Survival analysis was used to evaluate the stratification given by the trained model. Results: Patients identified by the TIL interplay model as high risk for DR had statistically worse disease specific survival. Univariate analysis yielded an HR=2.49 (95% CI: 1.22-5.07, p=0.04) for site 2 and HR=3.62 (95% CI: 1.39-9.43, p=0.03) for site 3. Multivariate analysis controlling the effect of different clinical variables is shown in the attached table. Conclusions: We introduce a prognostic model based on the automated quantification of the interplay between tumor microenvironment cells that is able to help distinguish OPSCC patients with higher DR risk from those who will experience longer disease-free survival. [Table: see text]

6566 Background: Oropharyngeal squamous cell carcinoma patients can have major morbidity from current treatment regimens, necessitating accurate identification of patients with aggressive versus indolent tumors. In this study, we sought to evaluate whether the combination of computer extracted features of tumor cell multinucleation (MN) and spatial interplay of tumor-infiltrating lymphocytes (TILs) is prognostic of overall survival (OS) in OPSCC patients. Methods: OPSCC specimens from 688 patients were retrospectively collected from 3 different sites. 141 patients from site 1 formed the training set (D1) and 322 patients from site 2 and 225 patients from site 3 formed the independent validation cohort (D2, n = 547). A machine learning (ML) model was employed to automatically calculate a Multi-nucleation risk index (MNI), which is the ratio of the number of MN to the number of epithelial cells, to each patient. A separate ML model was also used to capture measurements related to the interplay between TILs and tumor cells (SpaTIL), which were then used to compute a risk score using a Cox regression model. The median value of both the MNIs and the SpaTIL risk scores in D2 were used to identify patients as either low- or high-risk. A definitive label was assigned to each patient by combining the class labels obtained from the MNI and SpaTIL models using a logical AND operation. Results: In D2, the patients with high-risk scores had statistically significantly worse survival in univariate analysis. The univariate analysis yielded an HR = 1.91 (95% CI: 1.25-2.93, p = 0.0027) for D. Multivariate analysis controlling the effect of different clinical variables is shown in the table. Conclusions: We presented a computational pathology approach to prognosticate disease outcome in OPSCC by combining features relating to density of multinucleation and spatial arrangement of TILs and validated the approach on a large multi-site dataset. With additional validation the approach could potentially help identify OPSCC patients who could benefit from de-escalation of therapy. [Table: see text]

Patient: Male, 47 Final Diagnosis: Membranoproliferative glomerulonephritis Symptoms: Nephrotic syndrome Medication: — Clinical Procedure: Renal biopsy Specialty: Nephrology Objective: Rare disease Background: Visceral leishmaniasis is an important opportunistic disease in HIV-positive patients. The information available on the effects of such co-infection in the kidney is limited. We describe a patient with HIV/leishmania coinfection who developed nephrotic syndrome and membranoproliferative glomerulonephritis. As far as we know, only 2 cases of this nephropathy in HIV/leishmania coinfection have been reported. Case Report: A 47-year-old man developed nephrotic syndrome. He had been diagnosed with HIV infection and visceral leishmaniasis and was treated with antiretroviral therapy, antimonial compounds, liposomal amphotericin B and miltefosine, but the leishmania followed a relapsing course. Renal biopsy disclosed membranoproliferative glomerulonephritis and leishmania amastigotes were seen within glomerular capillary lumens. He was given miltefosine and liposomal amphotericin B but the leishmaniasis persisted. Stage 3B chronic renal disease and nephrotic range proteinuria tend to become stable by 15-month follow-up. Conclusions: Our case illustrated some aspects of leishmaniasis in HIV patients: its relapsing course, the difficulties in therapy, and the renal involvement.

Tumor-infiltrating lymphocytes (TILs) have proved to play an important role in predicting prognosis, survival, and response to treatment in patients with a variety of solid tumors. Unfortunately, currently, there are not a standardized methodology to quantify the infiltration grade. The aim of this work is to evaluate variability among the reports of TILs given by a group of pathologists who examined a set of digitized Non-Small Cell Lung Cancer samples (n=60). 28 pathologists answered a different number of histopathological images. The agreement among pathologists was evaluated by computing the Kappa index coefficient and the standard deviation of their estimations. Furthermore, TILs reports were correlated with patient’s prognosis and survival using the Pearson’s correlation coefficient. General results show that the agreement among experts grading TILs in the dataset is low since Kappa values remain below 0.4 and the standard deviation values demonstrate that in none of the images there was a full consensus. Finally, the correlation coefficient for each pathologist also reveals a low association between the pathologists’ predictions and the prognosis/survival data. Results suggest the need of defining standardized, objective, and effective strategies to evaluate TILs, so they could be used as a biomarker in the daily routine.