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Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes compared with patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients have been included in previous studies. To determine the genomic landscape of gastric cancer in Hispanic/Latino patients, we performed whole-exome sequencing (WES) and RNA sequencing on tumor samples from 57 patients; germline analysis was conducted on 83 patients. The results were compared with data from Asian and White patients published by The Cancer Genome Atlas. Hispanic/Latino patients had a significantly larger proportion of genomically stable subtype tumors compared with Asian and White patients (65% vs. 21% vs. 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type cancer, 7 (16%) had germline variants in CDH1. Variant carriers were significantly younger than noncarriers (41 vs. 50 years, P < 0.05). In silico algorithms predicted five variants to be deleterious. For two variants that were predicted to be benign, in vitro modeling demonstrated that these mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino patients with gastric cancer possess unique genomic landscapes, including a high rate of CDH1 germline variants that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary. Significance: Gastric cancer in Hispanic/Latino patients has unique genomic profiles that may contribute to the aggressive clinical phenotypes seen in these patients.

PURPOSE Large-scale analysis of real-world evidence is often limited to structured data fields that do not contain reliable information on recurrence status and disease sites. In this report, we describe a natural language processing (NLP) framework that uses data from free-text, unstructured reports to classify recurrence status and sites of recurrence for patients with breast and hepatocellular carcinomas (HCC). METHODS Using two cohorts of breast cancer and HCC cases, we validated the ability of a previously developed NLP model to distinguish between no recurrence, local recurrence, and distant recurrence, based on clinician notes, radiology reports, and pathology reports compared with manual curation. A second NLP model was trained and validated to identify sites of recurrence. We compared the ability of each NLP model to identify the presence, timing, and site of recurrence, when compared against manual chart review and International Classification of Diseases coding. RESULTS A total of 1,273 patients were included in the development and validation of the two models. The NLP model for recurrence detects distant recurrence with an area under the curve of 0.98 (95% CI, 0.96 to 0.99) and 0.95 (95% CI, 0.88 to 0.98) in breast and HCC cohorts, respectively. The mean accuracy of the NLP model for detecting any site of distant recurrence was 0.9 for breast cancer and 0.83 for HCC. The NLP model for recurrence identified a larger proportion of patients with distant recurrence in a breast cancer database (11.1%) compared with International Classification of Diseases coding (2.31%). CONCLUSION We developed two NLP models to identify distant cancer recurrence, timing of recurrence, and sites of recurrence based on unstructured electronic health record data. These models can be used to perform large-scale retrospective studies in oncology.

Pancreatic cancer is the fourth leading cause of cancer-related death among men and women in the United States. A major challenge in treatment remains patients’ advanced disease at diagnosis. The NCCN Guidelines for Pancreatic Adenocarcinoma provides recommendations for the diagnosis, evaluation, treatment, and follow-up for patients with pancreatic cancer. Although survival rates remain relatively unchanged, newer modalities of treatment, including targeted therapies, provide hope for improving patient outcomes. Sections of the manuscript have been updated to be concordant with the most recent update to the guidelines. This manuscript focuses on the available systemic therapy approaches, specifically the treatment options for locally advanced and metastatic disease.

Suboptimal generalization of machine learning models on unseen data is a key challenge which hampers the clinical applicability of such models to medical imaging. Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem. Here, we propose STRAP ( S tyle TR ansfer A ugmentation for histo P athology), a form of data augmentation based on random style transfer from non-medical style sources such as artistic paintings, for learning domain-agnostic visual representations in computational pathology. Style transfer replaces the low-level texture content of an image with the uninformative style of randomly selected style source image, while preserving the original high-level semantic content. This improves robustness to domain shift and can be used as a simple yet powerful tool for learning domain-agnostic representations. We demonstrate that STRAP leads to state-of-the-art performance, particularly in the presence of domain shifts, on two particular classification tasks in computational pathology. Our code is available at https://github.com/rikiyay/style-transfer-for-digital-pathology .

Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still experience tumor recurrence at 5 years post-surgery. We developed and validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary resection, directly from hematoxylin and eosin-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved concordance indices of 0.724 and 0.683 on the internal and external test cohorts, respectively, exceeding the performance of the standard Tumor-Node-Metastasis classification system. The model’s risk score stratified patients into low- and high-risk subgroups with statistically significant differences in their survival distributions, and was an independent risk factor for post-surgical recurrence in both test cohorts. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods and help refine the clinical management of patients undergoing primary surgical resection for HCC.

AimsDeep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on H&E stain is a tedious, imprecise task which might benefit from DL assistance. Here, we developed a DL assistant for diagnosing H. pylori in gastric biopsies and tested its impact on pathologist diagnostic accuracy and turnaround time.Methods and resultsH&E-stained whole-slide images (WSI) of 303 gastric biopsies with ground truth confirmation by immunohistochemistry formed the study dataset; 47 and 126 WSI were respectively used to train and optimize our DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area-under-the-receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934–0.987). On H. pylori-positive cases, assisted diagnoses were faster (, the fixed effect size for assistance = –0.557, p = 0.003) and much more accurate (OR = 13.37, p< 0.001) than unassisted diagnoses. However, assistance increased diagnostic uncertainty on H. pylori- negative cases, resulting in an overall decrease in assisted accuracy (OR = 0.435, p = 0.016) and negligible impact on overall turnaround time ( for assistance = 0.010, p = 0.860).ConclusionsDL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.

Recurrence risk stratification of patients undergoing primary surgical resection for hepatocellular carcinoma (HCC) is an area of active investigation, and several staging systems have been proposed to optimize treatment strategies. However, as many as 70% of patients still have tumor recurrence at 5 years post-surgery. Routine hematoxylin and eosin (H&E)-stained histopathology slides may contain morphologic features associated with tumor recurrence. In this study, we developed and independently validated a deep learning-based system (HCC-SurvNet) that provides risk scores for disease recurrence after primary surgical resection, directly from H&E-stained digital whole-slide images of formalin-fixed, paraffin embedded liver resections. Our model achieved a concordance index of 0.724 on a held-out internal test set of 53 patients, and 0.683 on an external test set of 198 patients, exceeding the performance of standard staging using the American Joint Committee on Cancer (AJCC)/International Union against Cancer (UICC) Tumor-Node-Metastasis (TNM) classification system, on both the internal and external test cohorts (p = 0.018 and 0.025, respectively). We observed statistically significant differences in the survival distributions between low- and high-risk subgroups, as stratified by the risk scores predicted by HCC-SurvNet on both the internal and external test sets (log-rank p-value: 0.0013 and < 0.0001, respectively). On multivariable Cox proportional hazards analysis, the risk score was an independent risk factor for post-surgical recurrence, on both the internal (hazard ratio (HR) = 7.44 (95% CI: 1.60, 34.6), p = 0.0105) and external (HR = 2.37 (95% CI: 1.27, 4.43), p = 0.0069) test sets. Our results suggest that deep learning-based models can provide recurrence risk scores which may augment current patient stratification methods, and help refine the clinical management of patients undergoing primary surgical resection for HCC.

Detecting microsatellite instability (MSI) in colorectal cancer is crucial for clinical decision making, as it identifies patients with differential treatment response and prognosis. Universal MSI testing is recommended, but many patients remain untested. A critical need exists for broadly accessible, cost-efficient tools to aid patient selection for testing. Here, we investigate the potential of a deep learning-based system for automated MSI prediction directly from haematoxylin and eosin (HE)-stained whole-slide images (WSIs).Our deep learning model (MSINet) was developed using 100 HE-stained WSIs (50 with microsatellite stability [MSS] and 50 with MSI) scanned at 40× magnification, each from a patient randomly selected in a class-balanced manner from the pool of 343 patients who underwent primary colorectal cancer resection at Stanford University Medical Center (Stanford, CA, USA; internal dataset) between Jan 1, 2015, and Dec 31, 2017. We internally validated the model on a holdout test set (15 HE-stained WSIs from 15 patients; seven cases with MSS and eight with MSI) and externally validated the model on 484 HE-stained WSIs (402 cases with MSS and 77 with MSI; 479 patients) from The Cancer Genome Atlas, containing WSIs scanned at 40× and 20× magnification. Performance was primarily evaluated using the sensitivity, specificity, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC). We compared the model's performance with that of five gastrointestinal pathologists on a class-balanced, randomly selected subset of 40× magnification WSIs from the external dataset (20 with MSS and 20 with MSI).The MSINet model achieved an AUROC of 0·931 (95% CI 0·771-1·000) on the holdout test set from the internal dataset and 0·779 (0·720-0·838) on the external dataset. On the external dataset, using a sensitivity-weighted operating point, the model achieved an NPV of 93·7% (95% CI 90·3-96·2), sensitivity of 76·0% (64·8-85·1), and specificity of 66·6% (61·8-71·2). On the reader experiment (40 cases), the model achieved an AUROC of 0·865 (95% CI 0·735-0·995). The mean AUROC performance of the five pathologists was 0·605 (95% CI 0·453-0·757).Our deep learning model exceeded the performance of experienced gastrointestinal pathologists at predicting MSI on HE-stained WSIs. Within the current universal MSI testing paradigm, such a model might contribute value as an automated screening tool to triage patients for confirmatory testing, potentially reducing the number of tested patients, thereby resulting in substantial test-related labour and cost savings.Stanford Cancer Institute and Stanford Departments of Pathology and Biomedical Data Science.

Early identification of gastric precancerous lesions, including atrophic gastritis (AG) and intestinal metaplasia (IM), may improve gastric cancer detection and prevention. Because AG and IM are generally asymptomatic, many of the estimated 15 million Americans who carry these lesions remain undiagnosed.1 AG and IM are associated with either active or prior Helicobacter pylori (Hp) infection. Hp infection leads to perturbations in the serum concentration of gastric hormones pepsinogen I (PGI), pepsinogen II, the pepsinogen I/II ratio (PGR), gastrin-17 (G-17), and Hp IgG.2,3 In East Asia and other regions with high burden of Hp infection and gastric cancer, these biomarkers have been used as screening tools for AG and IM.4 However, there exists limited data on the sensitivity and discrimination of these serologic markers in low-Hp-prevalence populations, such as the United States.

Helicobacter pylori organisms have been observed deep within the stomach mucosa with an "intracellular" appearance, although the clinicopathological characteristics of such cases remain poorly understood. We analyzed 18 cases of deep mucosal H pylori and associated clinical (sex, age, history of H pylori infection, or proton pump inhibitor [PPI] use, medications, smoking, alcohol use, comorbidities, treatment response) and pathological (presence of lymphoid aggregates, intestinal metaplasia, PPI effect, active and/or chronic inflammation, quantity of invasive versus surface H pylori) characteristics. Electron microscopy was performed on 6 cases with the highest burden of invasive H pylori. Within our sample, 3 of 16 had a history of H pylori infection, 10 of 15 were receiving PPIs at the time of biopsy, and 12 of 13 had a negative posttreatment follow-up. Histology revealed that invasive H pylori were more commonly associated with chronic inflammation, in both the antrum (15/15 chronic, 8/15 acute) and fundus (17/18 chronic, 8/18 acute). Electron microscopy showed organisms within intercellular and luminal spaces, but no intracellular organisms. Deep mucosal H pylori often have an intracellular appearance but are contained within intercellular and luminal spaces and are responsive to standard therapy.

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model’s prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model’s prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.

Hispanic/Latino patients have a higher incidence of gastric cancer and worse cancer-related outcomes as compared to patients of other backgrounds. Whether there is a molecular basis for these disparities is unknown, as very few Hispanic/Latino patients were included in previous studies. We performed a large, integrated genomic analysis of gastric cancer samples from Hispanic/Latino patients. Whole-exome sequencing (WES) and RNA sequencing were performed on 57 Hispanic/Latino gastric cancer patient samples. Germline analysis was conducted on 83 patients. Functional testing of CDH1 germline mutations was performed in Chinese hamster ovary cells. Tumors from Hispanic/Latino patients were significantly enriched for the genomically-stable subtype (as defined by The Cancer Genome Atlas), compared to Asians and Whites (65% vs 21% vs 20%, P < 0.001). Transcriptomic analysis identified molecular signatures that were prognostic. Of the 43 Hispanic/Latino patients with diffuse-type gastric cancer, 7 (16%) had germline mutations in CDH1. Mutation carriers were significantly younger than non-carriers (41 vs 50 years, P < 0.05). E-cadherin expression was reduced in 5 of 6 mutation carrier tumor samples available for analysis. In silico algorithms predicted 5 variants were deleterious. For the two variants that were predicted to be benign, we demonstrated that the mutations conferred increased migratory capability, suggesting pathogenicity. Hispanic/Latino gastric cancer patients possess unique genomic landscapes. This includes a high rate of CDH1 germline mutations that may partially explain their aggressive clinical phenotypes. Individualized screening, genetic counseling, and treatment protocols based on patient ethnicity and race may be necessary.

Aims Deep learning (DL), a sub-area of artificial intelligence, has demonstrated great promise at automating diagnostic tasks in pathology, yet its translation into clinical settings has been slow. Few studies have examined its impact on pathologist performance, when embedded into clinical workflows. The identification of H. pylori on HE 47 and 126 WSI were respectively used to train and optimize the DL assistant to detect H. pylori, and 130 were used in a clinical experiment in which 3 experienced GI pathologists reviewed the same test set with and without assistance. On the test set, the assistant achieved high performance, with a WSI-level area under the receiver-operating-characteristic curve (AUROC) of 0.965 (95% CI 0.934–0.987). On H. pylori-positive cases, assisted diagnoses were faster ( β ˆ , the fixed effect size for assistance ​= ​−0.557, p ​= ​0.003) and much more accurate (OR ​= ​13.37, p ​ β ˆ for assistance ​= ​0.010, p ​= ​0.860). Conclusions DL can assist pathologists with H. pylori diagnosis, but its integration into clinical workflows requires optimization to mitigate diagnostic uncertainty as a potential consequence of assistance.

Hispanics/Latinos in the U.S. have higher incidence and mortality from gastric cancer as compared to non-Hispanic Whites. However, few Hispanic/Latino patients have been included in basic science or clinical studies in gastric cancer We analyzed information from the National Cancer Database and found that compared to Asian and White patients, Hispanics/Latino patients with gastroesophageal junction (GEJ) or gastric adenocarcinomas had a higher proportion of Stage 3 or 4 disease (64% of Hispanic/Latino patients, 58% of White patients and 54% of Asians (P < 0.001)) and were younger at the time of diagnosis (median age in years (interquartile range): Hispanic/Latino 62 (50-73), Asian: 66 (56-76), and White: 68 (59-77), P < 0.001). To determine if there were genomic factors associated with these clinical outcomes, we performed whole-exome and RNA sequencing on GEJ and GC samples from 36 Hispanic/Latino patients treated in North Texas. Using the Locating Ancestry from SEquence Reads algorithm, we analyzed the sequencing data from our cohort and the publicly available information of White and Asian patients sequenced by The Cancer Genome Atlas (TCGA) to determine ancestry. We found that each group bundled within distinct clusters. Next, we classified the tumors based on the molecular categorization scheme established by the TCGA, which defines four groups using a step-wise algorithm. Tumors are first categorized by Epstein-Barr virus (EBV) infection status and then by microsatellite instability (MSI). The remaining samples undergo somatic copy number alteration (SCNA) analysis, with high rates defining the chromosomal instability (CIN) group and low rates designating the genomically stable (GS) tumors. In our cohort of Hispanic/Latino patients, we found no EBV, two MSI (6%), ten CIN (28%), and 24 GS (66%) subtype tumors. This is a much a higher proportion of GS subtype tumors as compared to White and Asian patients sequenced by the TCGA (P < 0.001). In addition, we found a relatively high rate of germline CDH1 mutations, which are known to cause diffuse-type gastric cancer, that may explain the younger presentation age of Hispanic/Latino patients. Finally, we identified a molecular signature related to activated immune response that was prognostic for overall survival in a subset of patients. Our findings have significant clinical implications in terms of screening, genetic counseling, and treatment for Hispanic/Latino patients. Citation Format: Sam C. Wang, Yunku Yeu, Suntrea T.G. Hammer, Min Zhu, Ibrahim Nassour, Jeanne Shen, Deepak Agarwal, Scott I. Reznick, John C. Mansour, Adam C. Yopp, Hao Zhu, Tae Hyun Hwang, Matthew R. Porembka. Molecular subtyping of gastroesophageal junction and gastric adenocarcinomas from American Hispanic/Latino patients [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A107.

Introduction Gastric intestinal metaplasia (GIM) is a precursor to gastric cancer (GC). It is not cost effective to survey the general American population for progression from GIM onto GC; however, development of risk-stratification models may allow for targeted surveillance. There exist very limited data regarding GIM epidemiology or risk derived from North American populations. The Stanford Gastric Precancerous Conditions Study (GAPS) is an ongoing, prospective study incorporating both 1) a cross-sectional, case-control study of subjects with GIM compared to controls, and 2) a longitudinal evaluation of subjects with GIM to evaluate risk factors for progression. The purpose of GAPS is to both improve the detection of GIM, and to predict risk for progression of GIM onto dysplasia or GC. Methods At time of enrollment in GAPS, all patients complete a standardized questionnaire inquiring about medical, family, dietary, and exposure history. Biopsies are performed from both antrum and body, and bio-specimens from the gastric mucosa, blood, and saliva are collected. Subjects are assigned an operative link for GIM (OLGIM) score based on adjudication by an expert pathologist. Demographic, clinical, and environmental covariates are compared between cases of GIM and controls. Subgroup analysis is performed to compare cases of advanced GIM (defined as OLGIM >=2) and controls. Continuous variables are analyzed using Student’s T-test, and categorical variables are analyzed using the Chi-squared test. Results As of July 2019, 44 cases and 49 controls have undergone questionnaire administration, endoscopy, and bio-specimen collection. Of cases, 23 demonstrate advanced GIM. Subjects with GIM were older (65 vs 56 years, p Citation Format: Robert J Huang, Sungho Park, Tanvi Chitre, Jeanne Shen, Teri Longacre, Joo Ha Hwang. A case-control study of risk factors for advanced gastric intestinal metaplasia in a multiethnic United States population (The Stanford GAPS Study) [abstract]. In: Proceedings of the Twelfth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2019 Sep 20-23; San Francisco, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl_2):Abstract nr C059.

Colorectal cancer (CRC) is a common disease that affects men and women of all racial and ethnic groups. This year, it is estimated that 136,830 people will be diagnosed with CRC and approximately 50,310 people will die of the disease in the United States. In both men and women, CRC is the third most commonly diagnosed cancer and the third leading cause of cancer-related death. This chapter introduces the anatomy of the colon and rectum, including its embryology, development and normal histology, and continues with a summary of colorectal lesions and their pathology. Finally, the molecular pathogenesis of CRC is discussed, focusing on the most commonly mutated pathways in CRC. While the genetics are well defined, CRC is a heterogeneous disease that develops via multiple pathways, each of which will likely require specialized therapeutic approaches.

There is evidence that some cancers in patients with inflammatory bowel disease (IBD) develop via the serrated pathway of carcinogenesis. This study examined the clinicopathological features and outcome of 115 IBD patients (65 with ulcerative colitis, 50 with Crohn disease), all with at least 1 serrated polyp at endoscopy or colon resection, including the presence of synchronous and metachronous conventional neoplastic lesions (dysplasia or adenocarcinoma), over an average follow-up period of 56.4 months. Conventional neoplasia was categorized as flat dysplasia (low or high grade), sporadic adenoma, adenoma-like dysplasia-associated lesion or mass, or adenocarcinoma. Overall, 97% of patients had at least 1 hyperplastic polyp (HP), 6% had a sessile serrated adenoma/polyp, and none had a traditional serrated adenoma. Eight patients (7%) had a synchronous conventional neoplastic lesion; only 1 had flat dysplasia (1%) and 2 had adenocarcinoma (2%). Thirteen patients developed a metachronous conventional neoplastic lesion, with 8 developing their conventional neoplasm within an area of previous or concurrent colitis; only 1 patient developed flat dysplasia (1%), and none developed adenocarcinoma. A higher proportion of patients with both an HP and a synchronous conventional neoplastic lesion at index developed a metachronous conventional neoplastic lesion, compared with those with an index HP only (25% versus 7%). These results suggest that IBD patients (both ulcerative colitis and Crohn disease patients) with HP have a very low risk of developing a conventional neoplastic lesion (flat dysplasia or adenocarcinoma) that would warrant surgical resection.

Diffusion weighted magnetic resonance imaging has risen to the forefront of imaging for acute stroke. However, the differential diagnosis of restricted diffusion is wide and includes ischemia, metabolic derangements, infections, and highly-cellular masses. We present a case of central nervous system (CNS) candidiasis presenting radiographically as bilateral punctate areas of restricted magnetic resonance (MR) diffusion in the basal ganglia. This case illustrates the value of carefully considering the causes of restricted diffusion in the brain, notably to be broader than acute stroke and to include invasive fungal infections.

70 Background: Previous gastric cancer (GC) patient derived xenograft (PDX) studies reported engraftment rates inversely proportional to the immunocompromised states of recipient mice (nudes 17%, scid 27%, and NOD/ scid 34%). We tested highly immunocompromised NOD scidgamma (NSG) mice, which lack mature T cells, B cells, or functional natural killer cells, as recipients for low volume biopsies and post-treatment GC samples. Methods: Consent was obtained from GC patients who were scheduled to undergo esophagogastroduodenoscopy (EGD) and diagnostic laparoscopy as part of their disease management strategies. The following amounts of tumor were coated with Matrigel and inoculated into the flanks of NSG mice: EGD biopsies ~10 mg, post-treatment samples ~100 mm3, and peritoneal metastasis biopsies ~100 mm3. Tumors were serially measured and passaged when the greatest dimension reached 1.5 cm, or if there was overlying skin necrosis. Results: The engraftment rates were: EGD biopsies 48% (13 of 27 samples), post-resection samples 58% (7 of 12), and peritoneal metastases 11% (1 of 9). Median time to first passage was 10.1 weeks (range: 8.1 to 12.4) for EGD biopsies and 22.1 weeks (9.6 to 33.0) for post-treatment samples. Two (15%) engrafted EGD mice developed liver metastases, and one (7.7%) had axillary lymph node metastasis (AxLN). Three (47%) engrafted post-treatment mice had liver metastases and two (29%) had AxLN. Histology was generally maintained through passages and metastases with some loss of mucinous components. There were no associations between engraftment rate and any evaluated clinical or pathologic characteristics, including tumor response and overall survival. The tumor from the one patient who had a complete pathologic response after NAT did not engraft. Conclusions: Using highly immunocompromised NSG mice improved engraftment rates of GC PDX, even for challenging specimens such as endoscopic biopsies and post-treatment resection samples. Peritoneal biopsies did not engraft well. Tumor histology was generally maintained through passages. Studies comparing the expression profiles of serially passaged tumors to the original clinical samples are ongoing.

For two decades, the cut-and-sew Cox-Maze III procedure was the gold standard for the surgical treatment of atrial fibrillation (AF), and proved to be effective at eliminating AF. The incidence of late stroke was also very low. However, this procedure was not widely adopted due to its complexity and technical difficulty. Over the last 5-10 years, the introduction of new ablation technology has led to the development of the Cox-Maze IV procedure, as well as, more limited lesion sets, with the ultimate goal of performing a minimally-invasive lesion set on the beating heart, without the need for cardiopulmonary bypass. This review summarizes the current state of the art and future directions in the stand-alone surgical treatment of atrial fibrillation. The hope is that as more is learned about the mechanisms of AF and with better preoperative diagnostic technologies capable of precisely locating the areas responsible for AF, it will become possible to tailor specific lesion sets and ablation modalities to individual patients, making the surgical treatment of AF available to a larger population of patients.

Hermansky–Pudlak syndrome (HPS) is a rare autosomal recessive disorder characterized by oculocutaneous hypopigmentation, platelet dysfunction, and in many cases, life-threatening pulmonary fibrosis. We report the clinical course, imaging, and postmortem findings of a 38-year-old female with HPS-related progressive pulmonary fibrosis, highlighting the role of imaging in assessment of disease severity and prognosis.