Development and deployment of a histopathology-based deep learning algorithm for patient prescreening in a clinical trial.

Accurate identification of genetic alterations in tumors, such as Fibroblast Growth Factor Receptor, is crucial for treating with targeted therapies; however, molecular testing can delay patient care due to the time and tissue required. Successful development, validation, and deployment of an AI-based, biomarker-detection algorithm could reduce screening cost and accelerate patient recruitment. Here, we develop a deep-learning algorithm using >3000 H&E-stained whole slide images from patients with advanced urothelial cancers, optimized for high sensitivity to avoid ruling out trial-eligible patients. The algorithm is validated on a dataset of 350 patients, achieving an area under the curve of 0.75, specificity of 31.8% at 88.7% sensitivity, and projected 28.7% reduction in molecular testing. We successfully deploy the system in a non-interventional study comprising 89 global study clinical sites and demonstrate its potential to prioritize/deprioritize molecular testing resources and provide substantial cost savings in the drug development and clinical settings.

Clinical Validation of Artificial Intelligence-Augmented Pathology Diagnosis Demonstrates Significant Gains in Diagnostic Accuracy in Prostate Cancer Detection.

CONTEXT.­: Prostate cancer diagnosis rests on accurate assessment of tissue by a pathologist. The application of artificial intelligence (AI) to digitized whole slide images (WSIs) can aid pathologists in cancer diagnosis, but robust, diverse evidence in a simulated clinical setting is lacking. OBJECTIVE.­: To compare the diagnostic accuracy of pathologists reading WSIs of prostatic biopsy specimens with and without AI assistance. DESIGN.­: Eighteen pathologists, 2 of whom were genitourinary subspecialists, evaluated 610 prostate needle core biopsy WSIs prepared at 218 institutions, with the option for deferral. Two evaluations were performed sequentially for each WSI: initially without assistance, and immediately thereafter aided by Paige Prostate (PaPr), a deep learning-based system that provides a WSI-level binary classification of suspicious for cancer or benign and pinpoints the location that has the greatest probability of harboring cancer on suspicious WSIs. Pathologists' changes in sensitivity and specificity between the assisted and unassisted modalities were assessed, together with the impact of PaPr output on the assisted reads. RESULTS.­: Using PaPr, pathologists improved their sensitivity and specificity across all histologic grades and tumor sizes. Accuracy gains on both benign and cancerous WSIs could be attributed to PaPr, which correctly classified 100% of the WSIs showing corrected diagnoses in the PaPr-assisted phase. CONCLUSIONS.­: This study demonstrates the effectiveness and safety of an AI tool for pathologists in simulated diagnostic practice, bridging the gap between computational pathology research and its clinical application, and resulted in the first US Food and Drug Administration authorization of an AI system in pathology.

Phenogenomic heterogeneity of post-transplant plasmablastic lymphomas.

Plasmablastic lymphoma (PBL) is a rare and clinically aggressive neoplasm that typically occurs in immunocompromised individuals, including those with HIV infection and solid organ allograft recipients. Most prior studies have focused on delineating the clinicopathologic features and genetic attributes of HIV-related PBLs, where MYC deregulation and EBV infection, and more recently, mutations in JAK/STAT, MAP kinase, and NOTCH pathway genes have been implicated in disease pathogenesis. The phenotypic spectrum of post-transplant (PT)-PBLs is not well characterized and data on underlying genetic alterations are limited. Hence, we performed comprehensive histopathologic and immunophenotypic evaluation and targeted sequencing of 18 samples from 11 patients (8 males, 3 females, age range 12-76 years) with PT-PBL; 8 de novo and 3 preceded by other types of PTLDs. PT-PBLs displayed morphologic and immunophenotypic heterogeneity and some features overlapped those of plasmablastic myeloma. Six (55%) cases were EBV+ and 5 (45%) showed MYC rearrangement by fluorescence in situ hybridization. Recurrent mutations in epigenetic regulators (KMT2/MLL family, TET2) and DNA damage repair and response (TP53, mismatch repair genes, FANCA, ATRX), MAP kinase (KRAS, NRAS, HRAS, BRAF), JAK/STAT (STAT3, STAT6, SOCS1), NOTCH (NOTCH1, NOTCH3, SPEN), and immune surveillance (FAS, CD58) pathway genes were observed, with EBV+ and EBV- cases exhibiting similarities and differences in their mutational profiles. Clinical outcomes also varied, with survival ranging from 0-15.9 years postdiagnosis. Besides uncovering the biological heterogeneity of PT-PBL, our study highlights similarities and distinctions between PT-PBLs and PBLs occurring in other settings and reveals potentially targetable oncogenic pathways in disease subsets.

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation of challenges posed by diagnostic variability, histopathology caseload, and shortage of pathologists. We sought to define the performance of an AI-based automated prostate cancer detection system, Paige Prostate, when applied to independent real-world data. The algorithm was employed to classify slides into two categories: benign (no further review needed) or suspicious (additional histologic and/or immunohistochemical analysis required). We assessed the sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) of a local pathologist, two central pathologists, and Paige Prostate in the diagnosis of 600 transrectal ultrasound-guided prostate needle core biopsy regions ('part-specimens') from 100 consecutive patients, and to ascertain the impact of Paige Prostate on diagnostic accuracy and efficiency. Paige Prostate displayed high sensitivity (0.99; CI 0.96-1.0), NPV (1.0; CI 0.98-1.0), and specificity (0.93; CI 0.90-0.96) at the part-specimen level. At the patient level, Paige Prostate displayed optimal sensitivity (1.0; CI 0.93-1.0) and NPV (1.0; CI 0.91-1.0) at a specificity of 0.78 (CI 0.64-0.89). The 27 part-specimens considered by Paige Prostate as suspicious, whose final diagnosis was benign, were found to comprise atrophy (n = 14), atrophy and apical prostate tissue (n = 1), apical/benign prostate tissue (n = 9), adenosis (n = 2), and post-atrophic hyperplasia (n = 1). Paige Prostate resulted in the identification of four additional patients whose diagnoses were upgraded from benign/suspicious to malignant. Additionally, this AI-based test provided an estimated 65.5% reduction of the diagnostic time for the material analyzed. Given its optimal sensitivity and NPV, Paige Prostate has the potential to be employed for the automated identification of patients whose histologic slides could forgo full histopathologic review. In addition to providing incremental improvements in diagnostic accuracy and efficiency, this AI-based system identified patients whose prostate cancers were not initially diagnosed by three experienced histopathologists. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy.

Prostate cancer is a leading cause of morbidity and mortality for adult males in the US. The diagnosis of prostate carcinoma is usually made on prostate core needle biopsies obtained through a transrectal approach. These biopsies may account for a significant portion of the pathologists' workload, yet variability in the experience and expertise, as well as fatigue of the pathologist may adversely affect the reliability of cancer detection. Machine-learning algorithms are increasingly being developed as tools to aid and improve diagnostic accuracy in anatomic pathology. The Paige Prostate AI-based digital diagnostic is one such tool trained on the digital slide archive of New York's Memorial Sloan Kettering Cancer Center (MSKCC) that categorizes a prostate biopsy whole-slide image as either "Suspicious" or "Not Suspicious" for prostatic adenocarcinoma. To evaluate the performance of this program on prostate biopsies secured, processed, and independently diagnosed at an unrelated institution, we used Paige Prostate to review 1876 prostate core biopsy whole-slide images (WSIs) from our practice at Yale Medicine. Paige Prostate categorizations were compared to the pathology diagnosis originally rendered on the glass slides for each core biopsy. Discrepancies between the rendered diagnosis and categorization by Paige Prostate were each manually reviewed by pathologists with specialized genitourinary pathology expertise. Paige Prostate showed a sensitivity of 97.7% and positive predictive value of 97.9%, and a specificity of 99.3% and negative predictive value of 99.2% in identifying core biopsies with cancer in a data set derived from an independent institution. Areas for improvement were identified in Paige Prostate's handling of poor quality scans. Overall, these results demonstrate the feasibility of porting a machine-learning algorithm to an institution remote from its training set, and highlight the potential of such algorithms as a powerful workflow tool for the evaluation of prostate core biopsies in surgical pathology practices.

Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies.

Prostate cancer (PrCa) is the second most common cancer among men in the United States. The gold standard for detecting PrCa is the examination of prostate needle core biopsies. Diagnosis can be challenging, especially for small, well-differentiated cancers. Recently, machine learning algorithms have been developed for detecting PrCa in whole slide images (WSIs) with high test accuracy. However, the impact of these artificial intelligence systems on pathologic diagnosis is not known. To address this, we investigated how pathologists interact with Paige Prostate Alpha, a state-of-the-art PrCa detection system, in WSIs of prostate needle core biopsies stained with hematoxylin and eosin. Three AP-board certified pathologists assessed 304 anonymized prostate needle core biopsy WSIs in 8 hours. The pathologists classified each WSI as benign or cancerous. After ~4 weeks, pathologists were tasked with re-reviewing each WSI with the aid of Paige Prostate Alpha. For each WSI, Paige Prostate Alpha was used to perform cancer detection and, for WSIs where cancer was detected, the system marked the area where cancer was detected with the highest probability. The original diagnosis for each slide was rendered by genitourinary pathologists and incorporated any ancillary studies requested during the original diagnostic assessment. Against this ground truth, the pathologists and Paige Prostate Alpha were measured. Without Paige Prostate Alpha, pathologists had an average sensitivity of 74% and an average specificity of 97%. With Paige Prostate Alpha, the average sensitivity for pathologists significantly increased to 90% with no statistically significant change in specificity. With Paige Prostate Alpha, pathologists more often correctly classified smaller, lower grade tumors, and spent less time analyzing each WSI. Future studies will investigate if similar benefit is yielded when such a system is used to detect other forms of cancer in a setting that more closely emulates real practice.

Myelodysplastic Syndrome and Sweet's Syndrome Are Associated with a Mutation in Isocitrate Dehydrogenase 1.

BACKGROUND: Sweet's syndrome (SS) is a febrile neutrophilic dermatosis that has been clinically linked to hematological malignancies, particularly myelodysplastic syndrome (MDS), in a number of case series. Many epigenetic changes underlying MDS have been identified, such as a mutation in the isocitrate dehydrogenase 1 (IDH1) gene, which causes DNA hypermethylation and alteration of a number of genes that lead to leukemogenesis. However, the pathogenesis of malignancy-associated SS is unknown. CASE REPORT: We present two patients who were diagnosed with SS and concomitant IDH1-mutated MDS. Immunohistochemical staining of their skin lesions showed neutrophils diffusely positive for the IDH1 mutation. CONCLUSION: These cases demonstrate that IDH1 mutation may be implicated in the pathogenesis of malignancy-associated SS. Future investigation to elucidate this pathway is warranted. Establishing this molecular link can provide an earlier identification of patients with SS who are also at increased risk for developing MDS.

Metastatic Colon Cancer to the Sphenoid Wing and Ethmoid Sinus Mimicking Orbital Cellulitis: A Rare Presentation.

Metastatic lesions to the orbit are most commonly seen with breast, lung, and prostate cancer, but are less commonly seen with colon cancer. Furthermore, the presence of metastatic colon cancer involving the sphenoid wing has only been reported once previously. The authors present a case of a 68-year-old woman with right upper and lower eyelid edema and erythema along with decreased vision, relative afferent pupillary defect, limitation of extraocular movements, and chemosis suggestive of orbital cellulitis. Imaging revealed an erosive lesion of the sphenoid wing along with unilateral ethmoid sinusitis. Biopsies taken from both lesions revealed metastatic adenocarcinoma, consistent with colonic primary. The extensive inflammatory component of her disease required life-long high-dose steroids to maintain quiescence and preserve vision.

Biologically distinct subsets of nevi.

Melanocytic nevi (MN) encompass a range of benign tumors with varying microscopic and macroscopic features. Their development is a multifactorial process under genetic and environmental influences. The clinical importance of MN lies in distinguishing them from melanoma and in recognizing their associations with melanoma risk and cancer syndromes. Historically, the distinction between the different types of MN, as well as between MN and melanoma, was based on clinical history, gross morphology, and histopathological features. While histopathology with clinical correlation remains the gold standard for differentiating and diagnosing melanocytic lesions, in some cases, this may not be possible. The use of dermoscopy has allowed for the assessment of subsurface skin structures and has contributed to the clinical evaluation and classification of MN. Genetic profiling, while still in its early stages, has the greatest potential to refine the classification of MN by clarifying their developmental processes, biological behaviors, and relationships to melanoma. Here we review the most salient clinical, dermoscopic, histopathological, and genetic features of different MN subgroups.