Deep Learning for the detection of microsatellite instability from histology images in colorectal cancer: a systematic literature review

Microsatellite instability (MSI) or deficient mismatch repair (dMMR) is a clinically important genetic feature affecting 10-15% of colorectal cancer (CRC) patients. Patients with metastatic MSI/dMMR CRC are eligible for therapy with immune checkpoint inhibitors, making MSI/dMMR the most important immuno-oncological biomarker in CRC. Gold standard tests for detection of MSI/dMMR in CRC are based on wet laboratory tests such as immunohistochemistry (IHC) or DNA extraction with subsequent polymerase chain reaction (PCR). However, since 2019, advances in Deep Learning (DL), an Artificial Intelligence (AI) technology, have enabled the prediction of MSI/dMMR directly from digitized routine haematoxylin and eosin (H&E) histopathology slides with high accuracy. In addition to the initial proof-of-concept publication in 2019, twelve subsequent studies have refined, improved, and further validated this approach. At this moment, MSI/dMMR prediction using Deep Learning has become a widely used benchmark task for academic studies in the field of computational pathology. Beyond academic use, this assay has attracted commercial interest from companies with the possibility of approval as a diagnostic device in the near future. In this review, we summarize and quantitatively compare the existing evidence on Deep-Learning-based detection of MSI/dMMR in CRC and discuss the need for further improvement and potential for integration into routine pathological workflows. Ultimately, this DL-based method could facilitate the identification of patients eligible for treatment with immune checkpoint inhibitors by pre-screening or replacement of current methods

A highly diverse siliceous sponge fauna (Porifera: Hexactinellida, Demospongiae) from the Eocene of north-eastern Italy: systematics and palaeoecology

A siliceous sponge fauna, consisting of more than 900 specimens, is described from an early Lutetian tuffite horizon in the Chiampo Valley, Lessini Mountains, north-eastern Italy. Thirty-two taxa (15 Hexactinellida, 17 Demospongiae) are determined and illustrated, belonging to 24 genera, two of which are new (Rigonia gen. nov. and Coronispongia gen. nov.). Among these, 10 new species are proposed: Stauractinella eocenica sp. nov., Rigonia plicata gen. et sp. nov., Hexactinella clampensis sp. nov., Camerospongia visentinae sp. nov., C. tuberculata sp. nov., Toulminia italica sp. nov., Coronispongia confossa gen. et sp. nov., Cavispongia scarpai sp. nov., Corallistes multiosculata sp. nov. and Bolidium bertii sp. nov. Of the genera identified at Chiampo, 14 range back to the Cretaceous, three to the Jurassic and one to the Triassic, while six are still extant. The studied fauna shows affinities with sponges from the Eocene of Spain and the Cretaceous of Germany. The sponge fossils are uncompressed and bodily preserved, but the original siliceous skeleton is dissolved and substituted by calcite. Delicate attachments can be nevertheless documented: some sponges attached to a hard substrate by encrustation, while others were anchored on soft sediments by root-like structures. The presence of different modes of attachment suggests heterogeneous substrate conditions. Small, possibly young, sponges are recorded too. The sponge fauna is essentially autochthonous and lived in the middle-outer part of a carbonate ramp, where it formed clusters. This study extends the geographical and stratigraphical range of many sponge taxa, including Camerospongia, Toulminia, Ozotrachelus and Bolidium, previously documented only from the Cretaceous. The Recent calcified demosponge genus Astrosclera is reported here in the Cenozoic for the first time, having been reported previously in the Triassic only. Additionally, this study documents the second worldwide occurrence of the Recent sphinctozoan genus Vaceletia in the Palaeogene, formerly recorded exclusively in Australia. http://zoobank.org/urn:lsid:zoobank.org:pub:B3466955-8E20-429A-89BE-42BAEB4002E8 \ua9 2016, \ua9 The Trustees of the Natural History Museum, London 2016. All Rights Reserved

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhütung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts

Weakly supervised annotation-free cancer detection and prediction of genotype in routine histopathology

Deep learning is a powerful tool in computational pathology: it can be used for tumor detection and for predicting genetic alterations based on histopathology images alone. Conventionally, tumor detection and prediction of genetic alterations are two separate workflows. Newer methods have combined them, but require complex, manually engineered computational pipelines, restricting reproducibility and robustness. To address these issues, we present a new method for simultaneous tumor detection and prediction of genetic alterations: The Slide-Level Assessment Model (SLAM) uses a single off-the-shelf neural network to predict molecular alterations directly from routine pathology slides without any manual annotations, improving upon previous methods by automatically excluding normal and non-informative tissue regions. SLAM requires only standard programming libraries and is conceptually simpler than previous approaches. We have extensively validated SLAM for clinically relevant tasks using two large multicentric cohorts of colorectal cancer patients, Darmkrebs: Chancen der Verhutung durch Screening (DACHS) from Germany and Yorkshire Cancer Research Bowel Cancer Improvement Programme (YCR-BCIP) from the UK. We show that SLAM yields reliable slide-level classification of tumor presence with an area under the receiver operating curve (AUROC) of 0.980 (confidence interval 0.975, 0.984; n = 2,297 tumor and n = 1,281 normal slides). In addition, SLAM can detect microsatellite instability (MSI)/mismatch repair deficiency (dMMR) or microsatellite stability/mismatch repair proficiency with an AUROC of 0.909 (0.888, 0.929; n = 2,039 patients) and BRAF mutational status with an AUROC of 0.821 (0.786, 0.852; n = 2,075 patients). The improvement with respect to previous methods was validated in a large external testing cohort in which MSI/dMMR status was detected with an AUROC of 0.900 (0.864, 0.931; n = 805 patients). In addition, SLAM provides human-interpretable visualization maps, enabling the analysis of multiplexed network predictions by human experts. In summary, SLAM is a new simple and powerful method for computational pathology that could be applied to multiple disease contexts. (c) 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland