Whole-slide imaging systems can generate full-color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi-line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single-scan multi-focal whole-slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi-focal image data are studied. First, 3D imaging of thick absorption stained slides (∼60µ) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick-tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features complementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at ∼/ throughputs.
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http://dx.doi.org/10.1364/AO.394290 | DOI Listing |
NPJ Precis Oncol
January 2025
Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.
Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E)-stained pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts.
View Article and Find Full Text PDFLab Invest
January 2025
Université de Caen Normandie, INSERM U1086 ANTICIPE, Caen, France; UNICANCER, Comprehensive Cancer Center François Baclesse, Caen, France; Université de Caen Normandie, US PLATON- ORGAPRED core facility, Caen, France; Université de Caen Normandie, US PLATON, UNICANCER, Comprehensive Cancer Center François Baclesse- Biological Resource Center 'OvaRessources', Caen, France. Electronic address:
PARP inhibitors (PARPi) have been shown to improve progression-free survival, particularly in homologous recombination deficient (HRD) ovarian cancers. Identifying patients eligible to PARPi is currently based on next-generation sequencing (NGS), but the persistence of genomic scars in tumors after restoration of HR or epigenetic changes can be a limitation. Functional assays could thus be used to improve this profiling and faithfully identify HRD tumors.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Ocular Oncology Service, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
Background: Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. The median overall survival time for patients who develop metastasis is approximately one year. In this study, we aim to leverage deep learning (DL) techniques to analyze digital cytopathology images and directly predict the 48 month survival status on a patient level.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Gynecologic Oncology and Reproductive Medicine, Unit 1362, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA.
Background: The ability to predict the prognosis of patients with ovarian cancer can greatly improve disease management. However, the knowledge on the mechanism of the prediction is limited. We sought to deconvolute the attention feature learnt by a deep learning convolutional neural networks trained with whole-slide images (WSIs) of hematoxylin-and-eosin (H&E)-stained tumor samples using spatial transcriptomic data.
View Article and Find Full Text PDFJ Eur Acad Dermatol Venereol
January 2025
Pathology Department, IHP Group, Nantes, France.
Background: There is a need to improve risk stratification of primary cutaneous melanomas to better guide adjuvant therapy. Taking into account that haematoxylin and eosin (HE)-stained tumour tissue contains a huge amount of clinically unexploited morphological informations, we developed a weakly-supervised deep-learning approach, SmartProg-MEL, to predict survival outcomes in stages I to III melanoma patients from HE-stained whole slide image (WSI).
Methods: We designed a deep neural network that extracts morphological features from WSI to predict 5-y overall survival (OS), and assign a survival risk score to each patient.
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