Diagnosing B-cell acute lymphoblastic leukemia in 2 pediatric patients with recent SARS-CoV-2 infection.

COVID-19 infection is still a mystery in terms of its long-term effect on health and its consequences on hematological disorders. Prior studies including ours have shown the abnormal changes in hematopoietic cells in COVID-19 patients. In this article, we are presenting 2 cases of pediatric B-lymphoblastic leukemia (B-ALL) with a previous history of COVID-19 infection. The first case describes a 22-month-old boy presenting with lymphadenopathy, neutropenia, and anemia with concurrent COVID-19 infection without any evidence of a hematolymphoid neoplasm as per bone marrow and lymph node biopsy. However, he presented after 2 months with bone marrow biopsy confirming B-ALL. The second case is that of a 4-year-old girl presenting with B-ALL who has had asymptomatic COVID-19 infection 5 months before this current presentation. Both the cases had complete resolution of COVID-19 infection during the time of presentation with acute leukemia. There were notably 2 rare findings along the course of the patients' illnesses. First, the unusual plasmacytosis in the marrow during active COVID-19 infection in the first patient and the second, is predilection of development of B-ALL following COVID-19. In both the cases the fluorescence in situ hybridization (FISH) studies showed pathologic alteration of the RUNX1 gene. Overall, there are no literature to support a causal association between acute B-ALL and COVID-19. The diagnosis of B-ALL in these patients after COVID-19 infection may be totally unrelated. However, if we consider Greaves proposed 2-hit model for childhood acute leukemia, that an infectious agent can precipitate development of B-ALL in a genetically susceptible individual. Alteration of the RUNX1 gene in both the patients, opens a door for further exploration of the "second-hit" hypothesis regarding an infectious agent precipitating development of B-ALL in a genetically susceptible individual.

The Pancreatic Cancer Early Detection (PRECEDE) Study is a Global Effort to Drive Early Detection: Baseline Imaging Findings in High-Risk Individuals.

BACKGROUND: Pancreatic adenocarcinoma (PC) is a highly lethal malignancy with a survival rate of only 12%. Surveillance is recommended for high-risk individuals (HRIs), but it is not widely adopted. To address this unmet clinical need and drive early diagnosis research, we established the Pancreatic Cancer Early Detection (PRECEDE) Consortium. METHODS: PRECEDE is a multi-institutional international collaboration that has undertaken an observational prospective cohort study. Individuals (aged 18-90 years) are enrolled into 1 of 7 cohorts based on family history and pathogenic germline variant (PGV) status. From April 1, 2020, to November 21, 2022, a total of 3,402 participants were enrolled in 1 of 7 study cohorts, with 1,759 (51.7%) meeting criteria for the highest-risk cohort (Cohort 1). Cohort 1 HRIs underwent germline testing and pancreas imaging by MRI/MR-cholangiopancreatography or endoscopic ultrasound. RESULTS: A total of 1,400 participants in Cohort 1 (79.6%) had completed baseline imaging and were subclassified into 3 groups based on familial PC (FPC; n=670), a PGV and FPC (PGV+/FPC+; n=115), and a PGV with a pedigree that does not meet FPC criteria (PGV+/FPC-; n=615). One HRI was diagnosed with stage IIB PC on study entry, and 35.1% of HRIs harbored pancreatic cysts. Increasing age (odds ratio, 1.05; P<.001) and FPC group assignment (odds ratio, 1.57; P<.001; relative to PGV+/FPC-) were independent predictors of harboring a pancreatic cyst. CONCLUSIONS: PRECEDE provides infrastructure support to increase access to clinical surveillance for HRIs worldwide, while aiming to drive early PC detection advancements through longitudinal standardized clinical data, imaging, and biospecimen captures. Increased cyst prevalence in HRIs with FPC suggests that FPC may infer distinct biological processes. To enable the development of PC surveillance approaches better tailored to risk category, we recommend adoption of subclassification of HRIs into FPC, PGV+/FPC+, and PGV+/FPC- risk groups by surveillance protocols.

A Case Report of a Patient With Prostate Adenocarcinoma Metastatic to the Posterior Peritoneum Despite the Negative Preoperative Prostate-Specific Membrane Antigen (PSMA) Positron Emission Tomography/Computed Tomography (PET/CT) Scan.

Despite the role of prostate-specific antigen (PSA) screening and the multitude of therapies available, prostate cancer (PCa) remains a leading cause of cancer-related morbidity and mortality. For many patients diagnosed with PCa, clinical and radiographic staging are critical components for management decisions. PCa staging with the use of imaging modalities such as MRI and bone scintigraphy is recommended in patients with newly diagnosed intermediate or high-risk PCa and in patients with biochemical recurrence; it is also recommended for monitoring the patient's response to treatment for diagnosed PCa. Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT), recently approved in 2021, is an imaging modality that has been shown to have a greater sensitivity, specificity, and negative likelihood ratio than conventional imaging modalities such as CT, bone scintigraphy, and MRI in prostate cancer staging. Despite the improvement in staging that PSMA-PET/CT can provide, our current report details a false-negative result in detecting a rare PCa metastasis to the peritoneum, which was found at the time of an attempted radical prostatectomy. Although the patient had a negative preoperative PSMA-PET/CT and was presumed to be non-metastatic, the prostatectomy was aborted because the patient was unexpectedly found to have peritoneal metastasis.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathy (CAA). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that approximately correlated with gold-standard human CERAD-like WSI scoring (p = 0.07 ± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

Learning fast and fine-grained detection of amyloid neuropathologies from coarse-grained expert labels.

Precise, scalable, and quantitative evaluation of whole slide images is crucial in neuropathology. We release a deep learning model for rapid object detection and precise information on the identification, locality, and counts of cored plaques and cerebral amyloid angiopathies (CAAs). We trained this object detector using a repurposed image-tile dataset without any human-drawn bounding boxes. We evaluated the detector on a new manually-annotated dataset of whole slide images (WSIs) from three institutions, four staining procedures, and four human experts. The detector matched the cohort of neuropathology experts, achieving 0.64 (model) vs. 0.64 (cohort) average precision (AP) for cored plaques and 0.75 vs. 0.51 AP for CAAs at a 0.5 IOU threshold. It provided count and locality predictions that correlated with gold-standard CERAD-like WSI scoring (p=0.07± 0.10). The openly-available model can quickly score WSIs in minutes without a GPU on a standard workstation.

Leukoerythroblastosis in a Sickle Cell Patient With Pregnancy: An Interesting Peripheral Blood Smear Finding.

The global pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has shaken the entire world. The social, health and financial impacts of this pandemic are beyond words. We have learnt a lot about this new disease in a short period of time, but still a long road to go to fully determine its pathogenic effect. The primary target of this virus is angiotensin-converting enzyme 2 (ACE2) receptor, which is prevalent in endothelial cells throughout the body. Immunocompromised patients such as patients with sickle cell disease are more vulnerable to severe respiratory infections, including infection with SARS-CoV-2. In addition, sickle cell disease patients are prone to vaso-occlusive crisis, and theoretically SARS-CoV-2 can worsen the situation as it also can cause endothelial dysfunction and thrombosis. Herein, we are sharing an interesting peripheral blood smear finding of an asymptomatic 31-year-old multigravida pregnant female with a history of sickle cell disease and found to have a positive COVID-19 polymerase chain reaction (PCR) test during her third trimester of pregnancy at a routine clinic visit. Two weeks after the initial positive test, she developed nausea, vomiting, constipation and a pain crisis affecting her extremities while her COVID-19 PCR test was still positive. She was hemodynamically stable, and lab workup revealed chronic anemia, leukocytosis with neutrophilia and lymphopenia. Morphologic examination of the peripheral blood smear showed a marked leukoerythroblastosis: rare myeloblasts, sickle cells, markedly abundant nucleated red blood cells (RBCs), metamyelocytes, and many large and giant platelets were seen. In this context, her previous peripheral blood smears (prior to positive COVID-19 test) did not show leukoerythroblastosis. She was managed conservatively with hydration and pain control and delivered at 36 weeks via cesarean section due to pre-term labor and intrauterine growth retardation. The unusual finding of leukoerythroblastosis in a pregnant sickle cell disease patient with an asymptomatic COVID-19 infection indicates further studies to determine its effect on hematopoietic system and elucidate its clinical significance.

Intracranial myeloid sarcoma presentation in distant acute myeloid leukemia remission.

Intracranial myeloid sarcoma (IMS) is a rare central nervous system manifestation of hematopoietic neoplasms of myeloid origin. We report the first case of IMS treatment with an isocitrate dehydrogenase-2 (IDH-2) inhibitor, Enasidenib, following surgical resection, whole-brain radiation, and consolidation Etoposide/Cytarabine therapy. A 42-year-old female was diagnosed with IMS after a 10-year remission of her acute myeloid leukemia (AML). She underwent surgical debulking and had postoperative resolution of her visual symptoms. She received adjuvant radiation and medical management, and continues to show no evidence of recurrence or progression at 17 months postoperatively. This case is notable for an isolated IMS presentation in a patient with a very distant history of AML remission, and without evidence of concurrent bone marrow relapse. The goals of neurosurgical intervention should be symptomatic relief of mass effect and pathological diagnosis, due to the sensitivity of IMS to adjuvant radiation and medical management such as IDH-2 inhibitors.

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models.

AIMS: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. METHODS: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. RESULTS: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. CONCLUSIONS: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

Limited Number of Cases May Yield Generalizable Models, a Proof of Concept in Deep Learning for Colon Histology.

BACKGROUND: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited. METHODS: Using 10 colon histology slides (5 carcinoma and 5 benign), we were able to acquire 1000 partially overlapping images (Dataset A) that were then trained and tested on three convolutional neural networks (CNNs), ResNet50, AlexNet, and SqueezeNet, to build a large number of unique models for a simple task of classifying colon histopathology into benign and malignant. Different quantities of images (10-1000) from Dataset A were used to construct >200 unique CNN models whose performances were individually assessed. The performance of these models was initially assessed using 20% of Dataset A's images (not included in the training phase) to acquire their initial validation accuracy (internal accuracy) followed by their generalization accuracy on Dataset B (a very distinct secondary test set acquired from public domain online sources). RESULTS: All CNNs showed similar peak internal accuracies (>97%) from the Dataset A test set. Peak accuracies for the external novel test set (Dataset B), an assessment of the ability to generalize, showed marked variation (ResNet50: 98%; AlexNet: 92%; and SqueezeNet: 80%). The models with the highest accuracy were not generated using the largest training sets. Further, a model's internal accuracy did not always correlate with its generalization accuracy. The results were obtained using an optimized number of cases and controls. CONCLUSIONS: Increasing the number of images in a training set does not always improve model accuracy, and significant numbers of cases may not always be needed for generalization, especially for simple tasks. Different CNNs reach peak accuracy with different training set sizes. Further studies are required to evaluate the above findings in more complex ML models prior to using such ancillary tools in clinical settings.

Case report of metastatic prostate cancer to testicles: An ominous sign of advanced disease.

Prostate cancer is the most common cancer in men in the United States and the second leading cause of mortality in this population. Those diagnosed may undergo a variety of treatments ranging from radiation to chemotherapy to surgery. Although metastases commonly first appear in bone, it is important to consider rare locations of metastasis such as the testicles. We present the case of a 56 year old male who presented with diffusely worsening back pain along with scrotal swelling who was ultimately diagnosed with metastatic prostate cancer to the bilateral testicles.

Ureteral Polyp Managed by Endoscopic Techniques.

Fibroepithelial polyps (FEPs) are rare benign tumors of mesodermal origin. They are found in the ureters 85% of the time, with the remainder located in the renal pelvis and occasionally the bladder. FEPs can present as flank pain, lower abdominal pain, and/or gross hematuria. Previous literature reports management of these benign lesions using open surgical techniques, laparoscopic techniques, and endoscopic management. In this article, the authors present their pure endoscopic management of a large ureteral polyp and a review of the current literature outlining the etiology, clinical presentations, and management techniques for FEP of the ureter.

Impact of pre-analytical variables on deep learning accuracy in histopathology.

AIMS: Machine learning (ML) binary classification in diagnostic histopathology is an area of intense investigation. Several assumptions, including training image quality/format and the number of training images required, appear to be similar in many studies irrespective of the paucity of supporting evidence. We empirically compared training image file type, training set size, and two common convolutional neural networks (CNNs) using transfer learning (ResNet50 and SqueezeNet). METHODS AND RESULTS: Thirty haematoxylin and eosin (H&E)-stained slides with carcinoma or normal tissue from three tissue types (breast, colon, and prostate) were photographed, generating 3000 partially overlapping images (1000 per tissue type). These lossless Portable Networks Graphics (PNGs) images were converted to lossy Joint Photographic Experts Group (JPG) images. Tissue type-specific binary classification ML models were developed by the use of all PNG or JPG images, and repeated with a subset of 500, 200, 100, 50, 30 and 10 images. Eleven models were generated for each tissue type, at each quantity of training images, for each file type, and for each CNN, resulting in 924 models. Internal accuracies and generalisation accuracies were compared. There was no meaningful significant difference in accuracies between PNG and JPG models. Models trained with more images did not invariably perform better. ResNet50 typically outperformed SqueezeNet. Models were generalisable within a tissue type but not across tissue types. CONCLUSIONS: Lossy JPG images were not inferior to lossless PNG images in our models. Large numbers of unique H&E-stained slides were not required for training optimal ML models. This reinforces the need for an evidence-based approach to best practices for histopathological ML.

A Meta-analysis of Multiple Myeloma Risk Regions in African and European Ancestry Populations Identifies Putatively Functional Loci.

BACKGROUND: Genome-wide association studies (GWAS) in European populations have identified genetic risk variants associated with multiple myeloma. METHODS: We performed association testing of common variation in eight regions in 1,318 patients with multiple myeloma and 1,480 controls of European ancestry and 1,305 patients with multiple myeloma and 7,078 controls of African ancestry and conducted a meta-analysis to localize the signals, with epigenetic annotation used to predict functionality. RESULTS: We found that variants in 7p15.3, 17p11.2, 22q13.1 were statistically significantly (P < 0.05) associated with multiple myeloma risk in persons of African ancestry and persons of European ancestry, and the variant in 3p22.1 was associated in European ancestry only. In a combined African ancestry-European ancestry meta-analysis, variation in five regions (2p23.3, 3p22.1, 7p15.3, 17p11.2, 22q13.1) was statistically significantly associated with multiple myeloma risk. In 3p22.1, the correlated variants clustered within the gene body of ULK4 Correlated variants in 7p15.3 clustered around an enhancer at the 3' end of the CDCA7L transcription termination site. A missense variant at 17p11.2 (rs34562254, Pro251Leu, OR, 1.32; P = 2.93 × 10-7) in TNFRSF13B encodes a lymphocyte-specific protein in the TNF receptor family that interacts with the NF-κB pathway. SNPs correlated with the index signal in 22q13.1 cluster around the promoter and enhancer regions of CBX7 CONCLUSIONS: We found that reported multiple myeloma susceptibility regions contain risk variants important across populations, supporting the use of multiple racial/ethnic groups with different underlying genetic architecture to enhance the localization and identification of putatively functional alleles. IMPACT: A subset of reported risk loci for multiple myeloma has consistent effects across populations and is likely to be functional. Cancer Epidemiol Biomarkers Prev; 25(12); 1609-18. ©2016 AACR.

Concerns about cancer risk and experiences with genetic testing in a diverse population of patients with breast cancer.

PURPOSE: To evaluate preferences for and experiences with genetic testing in a diverse cohort of patients with breast cancer identified through population-based registries, with attention to differences by race/ethnicity. METHODS: We surveyed women diagnosed with nonmetastatic breast cancer from 2005 to 2007, as reported to the SEER registries of metropolitan Los Angeles and Detroit, about experiences with hereditary risk evaluation. Multivariable models evaluated correlates of a strong desire for genetic testing, unmet need for discussion with a health care professional, and receipt of testing. RESULTS: Among 1,536 patients who completed the survey, 35% expressed strong desire for genetic testing, 28% reported discussing testing with a health care professional, and 19% reported test receipt. Strong desire for testing was more common in younger women, Latinas, and those with family history. Minority patients were significantly more likely to have unmet need for discussion (failure to discuss genetic testing with a health professional when they had a strong desire for testing): odds ratios of 1.68, 2.44, and 7.39 for blacks, English-speaking Latinas, and Spanish-speaking Latinas compared with whites, respectively. Worry in the long-term survivorship period was higher among those with unmet need for discussion (48.7% v 24.9%; P <.001). Patients who received genetic testing were younger, less likely to be black, and more likely to have a family cancer history. CONCLUSION: Many patients, especially minorities, express a strong desire for genetic testing and may benefit from discussion to clarify risks. Clinicians should discuss genetic risk even with patients they perceive to be at low risk, as this may reduce worry.

Patient-reported Quality of Life and Satisfaction With Cosmetic Outcomes After Breast Conservation and Mastectomy With and Without Reconstruction: Results of a Survey of Breast Cancer Survivors.

OBJECTIVE: Although breast conservation is therapeutically equivalent to mastectomy for most patients with early-stage breast cancer, an increasing number of patients are pursuing mastectomy, which may be followed by breast reconstruction. We sought to evaluate long-term quality of life and cosmetic outcomes after different locoregional management approaches, as perceived by patients themselves. METHODS: We surveyed women with a diagnosis of nonmetastatic breast cancer from 2005 to 2007, as reported to the Los Angeles and Detroit population-based Surveillance, Epidemiology, and End Results registries. We received responses from 2290 women approximately 9 months after diagnosis (73% response rate) and from 1536 of these 4 years later. We evaluated quality of life and patterns and correlates of satisfaction with cosmetic outcomes overall and, more specifically, within the subgroup undergoing mastectomy with reconstruction, using multivariable linear regression. RESULTS: Of the 1450 patients who responded to both surveys and experienced no recurrence, 963 underwent breast-conserving surgery, 263 mastectomy without reconstruction, and 222 mastectomy with reconstruction. Cosmetic satisfaction was similar between those receiving breast conservation therapy and those receiving mastectomy with reconstruction. Among patients receiving mastectomy with reconstruction, reconstruction type and radiation receipt were associated with satisfaction (P < 0.001), with an adjusted scaled satisfaction score of 4.7 for patients receiving autologous reconstruction without radiation, 4.4 for patients receiving autologous reconstruction and radiation therapy, 4.1 for patients receiving implant reconstruction without radiation therapy, and 2.8 for patients receiving implant reconstruction and radiation therapy. CONCLUSIONS: Patient-reported cosmetic satisfaction was similar after breast conservation and after mastectomy with reconstruction. In patients undergoing postmastectomy radiation, the use of autologous reconstruction may mitigate the deleterious impact of radiation on cosmetic outcomes.