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Abstract Introduction Over the past five years, there has been a rise in digital pathology services. As a result, healthcare providers deduce that this may enable them to save time and costs. More importantly, there is a conception that digital pathology may rectify the hindrance of slide storage and sharing abilities. However, this is fraught with peril, as companies have become aware of the need for digital pathology services, and as a result, have drastically changed the manner in which the services are rendered, from via a pure user license to via a scan lease license, which has a fixed number of slide scans that can be performed. Materials and Methods Pricing of various scan lease licenses was analyzed and compared to the costs of maintaining slide storage at an institution. Considering that the cost to scan slides via a scan lease license is approximately one US dollar per slide (eg, scanning 40,000 slides would cost approximately $40,000), in addition to the expenses for specimen processing and slide preparation, the scan lease license does not appear to be practical for digitizing images for a large, high-volume university practice. Results and Conclusion The implementation of digital pathology appears most feasible for small, lower-volume pathology services. Given the analysis, we have deemed the cost of approximately $4,000 a year to maintain slide storage at our institution, in comparison to that of a scan lease license, which would take approximately 20 years to reach a break-even point economically. In conclusion, while digital pathology is highly sought after, most high-volume medical centers would find this method cost-prohibitive. Therefore, if the availability were made in the future, a single-cost slide scanner would likely alleviate this cost-prohibitive interference.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

Diagnostic histopathology is a gold standard for diagnosing hematopoietic malignancies. Pathologic diagnosis requires labor-intensive reading of a large number of tissue slides with high diagnostic accuracy equal or close to 100 percent to guide treatment options, but this requirement is difficult to meet. Although artificial intelligence (AI) helps to reduce the labor of reading pathologic slides, diagnostic accuracy has not reached a clinically usable level. Establishment of an AI model often demands big datasets and an ability to handle large variations in sample preparation and image collection. Here, we establish a highly accurate deep learning platform, consisting of multiple convolutional neural networks, to classify pathologic images by using smaller datasets. We analyze human diffuse large B-cell lymphoma (DLBCL) and non-DLBCL pathologic images from three hospitals separately using AI models, and obtain a diagnostic rate of close to 100 percent (100% for hospital A, 99.71% for hospital B and 100% for hospital C). The technical variability introduced by slide preparation and image collection reduces AI model performance in cross-hospital tests, but the 100% diagnostic accuracy is maintained after its elimination. It is now clinically practical to utilize deep learning models for diagnosis of DLBCL and ultimately other human hematopoietic malignancies. Replacing diagnostic histopathology with AI-based tools requires large training datasets and robustness to sample variability. Here, the authors present a deep learning platform with high accuracy in large diffuse B-cell lymphoma diagnosis across multiple hospitals, trained on small datasets.

Objectives: Composite frozen section turnaround time has limited value, precluding assessment of certain processes: slide preparation (technical) and diagnosis (interpretation). We examined whether measuring these elements could identify delays, hypothesizing that longer times were related to (1) inefficient technical processes and (2) case-specific diagnostic challenges. Methods: Technical and interpretive times were determined for 1,992 specimens submitted for frozen section in 2017; the data were sorted by surgical specialty. Mean and quartile times were determined for each category with all specimens assessed equally, including those with multiple frozen section blocks. Results: Technical times were significantly longer than interpretive times. Specialty grouping facilitated trend identification and enabled assessment of technical and interpretation challenges. We identified technical issues with certain gross specimens involving overdissection and interpretation delays for specific neoplasms and margins. Conclusions: Measuring technical and interpretative times and subcategorizing by specialty has aided the assessment of frozen section processing in our laboratory, enabling case isolation for process improvement. Key Words: Frozen section; Turnaround time; Quality improvement

The prognostic value of mitotic figures in tumor tissue is well-established for many tumor types and automating this task is of high research interest. However, especially deep learning-based methods face performance deterioration in the presence of domain shifts, which may arise from different tumor types, slide preparation and digitization devices. We introduce the MIDOG++ dataset, an extension of the MIDOG 2021 and 2022 challenge datasets. We provide region of interest images from 503 histological specimens of seven different tumor types with variable morphology with in total labels for 11,937 mitotic figures: breast carcinoma, lung carcinoma, lymphosarcoma, neuroendocrine tumor, cutaneous mast cell tumor, cutaneous melanoma, and (sub)cutaneous soft tissue sarcoma. The specimens were processed in several laboratories utilizing diverse scanners. We evaluated the extent of the domain shift by using state-of-the-art approaches, observing notable differences in single-domain training. In a leave-one-domain-out setting, generalizability improved considerably. This mitotic figure dataset is the first that incorporates a wide domain shift based on different tumor types, laboratories, whole slide image scanners, and species.

Abstract Introduction/Objective Deep learning algorithms on digital images of physical tissue slides have shown potential improvements in accuracy and precision of diagnostic interpretation of neoplastic histology. Clustering-constrained- attention multiple-instance learning (CLAM) is one such method that identifies diagnostic sub-regions to accurately classify whole slides. Often, algorithm performance degrades when deployed on datasets that differ from the original set and it is subject to physical slide preparation variability. New multiphoton imaging modalities have potential workflow and quality advantages over physical slides, producing images analogous to whole slide imaging (WSI) without cutting artifacts, but performance of existing algorithms trained on digitized physical slides and applied to multiphoton images remains completely unknown. Given this, we aimed to test the performance of CLAM algorithms for subtyping renal cell carcinoma (RCC) and lung cancer (LC) applied to pseudo-colored multiphoton WSI. Methods/Case Report Clinical RCC and LC surgical resection samples were processed and imaged by Clearing Histology with MulitPhoton microscopy (CHiMP, Applikate Technologies, Fairfield, CT), producing digital images of un- cut, un-embeded tissue to generate H&E-like optical slices. Multiphoton images were downscaled to 0.5 um/px to match algorithm target resolution. CLAM models for subtyping RCC (chromophobe, clear cell, papillary) and LC (squamous & adenocarcinoma) previously trained using TCGA and CPTAC whole slide images of physical slides were applied directly to CHiMP multiphoton images without adjustment. Reference cancer subtype classifications were provided from physical and digital slides. Results (if a Case Study enter NA) For the subtypes included during training, multiphoton WSIs of RCC and LC were accurately subtyped by the CLAM models without stain normalization nor network fine tuning producing high prediction levels. Subtypes not included during the training (namely oncocytoma for RCC) resulted in low scoring model predictions (below 0.85), indicating specificity of identification. Multiple slide levels improved interpretation of several difficult cases for CLAM predictions. Conclusion This preliminary data suggests that CLAM models trained on standard H&E WSIs for RCC and LC subtyping are applicable to pseudo-H&E multiphoton WSIs without domain adaptations. This implies that diagnostic histologic features have been learned by these CLAM models and are efficiently recognized in digital histology images produced via CHiMP.

Histopathology often serves as the gold standard in medical diagnosis but lacks a spatial three-dimensional axis of information due to the inherent process of two-dimensional tissue slide preparation. Recovering this third dimension would enable correlation with in vivo imaging, improve multimodal data integration, and open new doors for three-dimensional quantitative tissue analysis. This study presents the RAPID framework, which tackles this task of registering whole slide images of whole-mount histopathology specimens into a three-dimensional stack. Our proposed framework leverages a DINOv2-pretrained ViT-L14 foundational model to consistently detect anatomical features, which are used to align a stack of unregistered whole slide images (WSIs) to obtain a three-dimensional reconstruction. RAPID is optimized to work with high-resolution WSIs at 0.25 m/pixel, leading to gigapixel reconstructions that can be used for cell-level downstream tasks. We validate our framework on various external validation sets and find that RAPID obtains an accurate reconstruction in 91.7–93.5% of cases, thereby significantly outperforming a current state-of-the-art reconstruction method. Unlike existing methods limited to serial sections, RAPID handles the sparse sampling intervals (3000–4000 m) routinely used in clinical pathology. We demonstrate its utility for three-dimensional radiology-pathology correlation, enabling direct volumetric comparison between histopathology and pre-operative imaging.

., collected from China, is described and illustrated. The species belongs to the tribe Boreoheptagyiini (Diamesinae). The genus is newly recorded from China. Phylogenetic analyses based on five molecular markers (18S, 28S, CAD, COI-5p, and COI-3p) confirm the new species belongs to a highly supported clade together with the known species . Morphologically, the two species are somewhat similar but can be distinguished by the number of antennal flagellomeres, and the structure of the hypopygium. Through careful slide preparation of the holotype and integrated morphological and molecular cross-validation, this study revealed that variations in slide-mounting techniques can produce morphological artifacts, thereby directly affecting taxonomic conclusions. These findings highlight that for taxa characterized by fine morphological structures, meticulous slide preparation and thorough verification are essential for ensuring robust taxonomic outcomes.

To implement and evaluate a large-scale online cervical cancer screening programme in Hubei Province, China, supported by artificial intelligence and delivered by trained health workers. The screening programme, which started in 2017, used four types of health worker: sampling health workers, slide preparation technicians, diagnostic health workers and cytopathologists. Sampling health workers took samples from the women on site; slide preparation technicians prepared slides for liquid-based cytology; diagnostic health workers identified negative samples and classified positive samples based on the Bethesda System after cytological assessment using online artificial intelligence; and cytopathologists reviewed positive samples and signed reports of the results online. The programme used fully automated scanners, online artificial intelligence, an online screening management platform, and mobile telephone devices to provide screening services. We evaluated the sustainability, performance and cost of the programme. From 2017 to 2021, 1 518 972 women in 16 cities in Hubei Province participated in the programme, of whom 1 474 788 (97.09%) had valid samples for the screening. Of the 86 648 women whose samples were positive, 30 486 required a biopsy but only 19 495 had one. The biopsy showed that 2785 women had precancerous lesions and 191 had invasive cancers. The cost of screening was 6.31 United States dollars (US$) per woman for the public payer: US$ 1.03 administrative costs and US$ 5.28 online screening costs. Cervical cancer screening using artificial intelligence in Hubei Province provided a low-cost, accessible and effective service, which will contribute to achieving universal cervical cancer screening coverage in China.

Maceration technique allows isolating and studying the structure of plant cells, providing essential insights into plant physiology and responses to environmental factors. Despite its importance, the methods sections of publications often lack sufficient detail to properly apply maceration technique, preventing broader applications beyond industrial and wood identification studies. Here we describe maceration protocols (P), as guidelines, to allow successful cell separation, in woody and herbaceous plants, for further studying the structure of vascular tissues (xylem and phloem), using Franklin’s solution instead of the traditional, more toxic Jeffrey’s. We included sample preparation, equipment and chemicals, recommended stains and means of slide preparation that ensure clear observation and imaging, and identified potential pitfalls and safety practices. P1 provides a simple xylem and phloem maceration technique, with non-permanent slides preparation, suitable for observation under light, fluorescence or confocal microscopy. P2 and P3 are suitable for producing permanent slides of macerated xylem cells. P2 has been successfully used for macerating xylem of angiosperms and gymnosperms from at least 34 families and 25 orders, with densities ranging widely. P3 uses minimal substances and time for dehydration and staining. These flexible protocols may contribute to unlocking the potential of maceration technique to advance research in ecology and evolution.