For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage. However, Gleason scoring is based on subjective microscopic examination of tumor morphology and suffers from poor reproducibility. Here we present a deep learning system (DLS) for Gleason scoring whole-slide images of prostatectomies. Our system was developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides. Compared to a reference standard provided by genitourinary pathology experts, the mean accuracy among 29 general pathologists was 0.61 on the validation set. The DLS achieved a significantly higher diagnostic accuracy of 0.70 ( p = 0.002) and trended towards better patient risk stratification in correlations to clinical follow-up data. Our approach could improve the accuracy of Gleason scoring and subsequent therapy decisions, particularly where specialist expertise is unavailable. The DLS also goes beyond the current Gleason system to more finely characterize and quantitate tumor morphology, providing opportunities for refinement of the Gleason system itself.

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Human stem-cell derivatives are likely to play an important role in the future of regenerative medicine. Evaluation and comparison to their in vivo counterparts is critical for assessment of their therapeutic potential. Transcriptomics was used to compare a new differentiation derivative of human embryonic stem (hES) cells--retinal pigment epithelium (RPE)--to human fetal RPE. Several hES cell lines were differentiated into putative RPE, which expressed RPEspecific molecular markers and was capable of phagocytosis, an important RPE function. Isolated hES cell-derived RPE was able to transdifferentiate into cells of neuronal lineage and redifferentiate into RPE-like cells through multiple passages (>30 Population doublings). Gene expression profiling demonstrated their higher similarity to primary RPE tissue than of existing human RPE cell lines D407 and ARPE-19, which has been shown to attenuate loss of visual function in animals. This is the first report of the isolation and characterization of putative RPE cells from hES cells, as well as the first application of transcriptomics to assess embryonic stem-cell derivatives and their in vivo counterparts--a "differentiomics" outlook. We describe for the first time, a differentiation system that does not require coculture with animal cells or factors, thus allowing the production of zoonoses-free RPE cells suitable for subretinal transplantation in patients with retinal degenerative diseases. With the further development of therapeutic cloning, or the creation of the banks of homozygous human leucocyte antigen (HLA) hES cells using parthenogenesis, RPE lines could be generated to overcome the problem of immune rejection and could be one of the nearest term applications of stem-cell technology.

Context.-Nodal metastasis of a primary tumor influences therapy decisions for a variety of cancers. Histologic identification of tumor cells in lymph nodes can be laborious and error-prone, especially for small tumor foci.Objective.-To evaluate the application and clinical implementation of a state-of-the-art deep learning-based artificial intelligence algorithm (LYmph Node Assistant or LYNA) for detection of metastatic breast cancer in sentinel lymph node biopsies.Design.-Whole slide images were obtained from hematoxylin-eosin-stained lymph nodes from 399 patients (publicly available Camelyon16 challenge dataset). LYNA was developed by using 270 slides and evaluated on the remaining 129 slides. We compared the findings to those obtained from an independent laboratory (108 slides from 20 patients/86 blocks) using a different scanner to measure reproducibility.Results.-LYNA achieved a slide-level area under the receiver operating characteristic (AUC) of 99% and a tumor-level sensitivity of 91% at 1 false positive per patient on the Camelyon16 evaluation dataset. We also identified 2 ''normal'' slides that contained micrometastases. When applied to our second dataset, LYNA achieved an AUC of 99.6%. LYNA was not affected by common histology artifacts such as overfixation, poor staining, and air bubbles.Conclusions.-Artificial intelligence algorithms can exhaustively evaluate every tissue patch on a slide, achieving higher tumor-level sensitivity than, and comparable slidelevel performance to, pathologists. These techniques may improve the pathologist's productivity and reduce the number of false negatives associated with morphologic detection of tumor cells. We provide a framework to aid practicing pathologists in assessing such algorithms for adoption into their workflow (akin to how a pathologist assesses immunohistochemistry results).

Telomerase is a ribonucleoprotein enzyme which has been linked to malignant transformation in human cells. Telomerase activity is increased in the vast majority of human tumors, making its gene product the first molecule common to all human tumors. The generation of endogenously processed telomerase peptides bound to Class I MHC molecules could therefore target cytotoxic T lymphocytes (CTL) to tumors of different origins. This could advance vaccine therapy against cancer provided that precursor CTL recognizing telomerase peptides in normal adults and cancer patients can be expanded through immunization. We demonstrate here that the majority of normal individuals and patients with prostate cancer immunized in vitro against two HLA-A2.1 restricted peptides from telomerase reverse transcriptase (hTRT) develop hTRT-specific CTL. This suggests the existence of precursor CTL for hTRT in the repertoire of normal individuals and in cancer patients. Most importantly, the CTL of cancer patients specifically lysed a variety of HLA-A2 ؉ cancer cell lines, demonstrating immunological recognition of endogenously processed hTRT peptides. Moreover, in vivo immunization of HLA-A2.1 transgenic mice generated a specific CTL response against both hTRT peptides. Based on the induction of CTL responses in vitro and in vivo, and the susceptibility to lysis of tumor cells of various origins by hTRT CTL, we suggest that hTRT could serve as a universal cancer vaccine for humans.

Machin e lear nin g (ML) is incr easingly being use d in image retrieval systems for medical decision making. On e app lication of ML is to retrieve visually similar medical images from pas t patients (e.g. tissue from biops ies) to reference whe n making a medical decision with a new pat ient. Howeve r, no algorithm can perfectly captu re an expert ' s ideal notion of similarity for every case: an image th at is algorithmi cally determin ed to be similar may not be medically relevant to a doctor' s specific diagnostic needs. In this pape r, we identified the needs of patho logists whe n searchin g for similar images retrieved usin g a deep lear nin g algorithm , and develope d tools that empower use rs to cope with the search algorithm on-the -fly, communi cating what types of similarity are most import ant at different moment s in time. In two evaluations with path ologists, we found th at th ese refinement tools increased the diagnos tic utility of images found and increased user trus t in the algorithm. Th e tools we re preferred over a traditi onal interface, without a loss in diagnostic accuracy. We also observe d that users adopted new str ategies whe n using refinement tools, re-purpos ing th em to test and understand the underlying algorithm and to disambiguate ML errors from their own errors. Taken togethe r, these findings inform futur e hum an-ML collabo rative systems for expe rt decision-m aking. CCS CONCEPTS• Human-centered computing --> Human computer interaction (HCI); KEYWORDS Human -AI int eraction ; machin e learnin g; clinical healthPermission to mak e digital or har d copies of part or all of this work for personal or classroom use is grant ed without fee provi ded that copies are not made or distributed for profit or commercial advanta ge and that copies bear this notice an d the full citation on the first page. Figure 1: Medical images contain a wide range of clinical features , such as cellular (1) and glandular morphology (2), interaction between components (3), processing artifacts (4), and many more. It can be difficult for a similar -image search algorithm to perfectly capture an expert's notion of similarity ,

Each year, the treatment decisions for more than 230, 000 breast cancer patients in the U.S. hinge on whether the cancer has metastasized away from the breast. Metastasis detection is currently performed by pathologists reviewing large expanses of biological tissues. This process is labor intensive and error-prone. We present a framework to automatically detect and localize tumors as small as 100 × 100 pixels in gigapixel microscopy images sized 100, 000×100, 000 pixels. Our method leverages a convolutional neural network (CNN) architecture and obtains state-of-the-art results on the Camelyon16 dataset in the challenging lesion-level tumor detection task. At 8 false positives per image, we detect 92.4% of the tumors, relative to 82.7% by the previous best automated approach. For comparison, a human pathologist attempting exhaustive search achieved 73.2% sensitivity. We achieve image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides. In addition, we discover that two slides in the Came-lyon16 training set were erroneously labeled normal. Our approach could considerably reduce false negative rates in metastasis detection.

These authors contributed equally to this work.The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy 1 . However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability 2,3 . Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel 4 . In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare 5-7 . However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions 8 , 9 . Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences 10 and beyond 11,12 .Microscopic examination of samples is the gold standard for the diagnosis of cancer, autoimmune diseases, infectious diseases, and more. In cancer, the microscopic examination of stained tissue sections is critical for diagnosing and staging the patient's tumor, which informs treatment decisions and prognosis. In cancer, microscopy analysis faces three major challenges. As a form of image interpretation, these examinations are inherently subjective, exhibiting considerable inter-observer and intra-observer variability 2,3 . Moreover, clinical guidelines 1 and studies 13 have begun to require quantitative assessments as part of the effort towards better patient risk stratification 1 . For example, breast cancer staging requires counting mitotic cells and quantification of the tumor burden in lymph nodes by measuring the largest tumor focus. However, despite being helpful in treatment planning, quantification is laborious and error-prone. Lastly, access to disease experts can be limited in both developed and developing countries 4 , exacerbating the problem.