Visualization of WEEP chosen tiles for various modeling methods. Credit score: DOI: 10.69622/27291567.v1
Histopathological analysis of tumor specimens has lengthy been important in diagnosing breast most cancers and guiding scientific decision-making. Nevertheless, one of many key challenges in routine diagnostics contains the inter-observer and inter-lab variabilities current within the evaluation of prognostic markers that might result in under- and over-treatment of sufferers.
With the present ongoing digitization of pathology labs, it has enabled the development of computational pathology, which has proven the potential to enhance each routine and precision diagnostics and provide resolution assist to each pathologists and treating physicians to enhance breast most cancers care.
Deep studying falls below the broader umbrella of synthetic intelligence (AI) that has proven potential in advancing past conventional pathology by bettering threat evaluation, prognosis prediction, and response-to-treatment predictions. This method, referred to as AI-based precision pathology, affords new prospects for higher affected person care.
In his thesis, Abhinav Sharma on the Division of Medical Epidemiology and Biostatistics has developed and validated deep learning-based fashions for AI- primarily based precision pathology duties to enhance breast most cancers prognosis utilizing routinely stained tumor tissue specimens.
What are a very powerful ends in your thesis?
“My thesis contains 4 completely different research and with out getting too technical, listed below are a few of my key findings: In my first examine, we developed and validated a deep learning-based mannequin (predGrade) that mimics the scientific histological grade, for classifying invasive breast most cancers into three grades primarily based on H&E-stained entire slide photographs (WSIs). The mannequin confirmed potential in lowering inter-observer and inter-lab variability, providing a extra reproducible and sturdy scientific resolution assist instrument for breast most cancers histological grading.
“Within the second examine, we validated an AI-based resolution, Stratipath Breast, used for threat stratification in breast most cancers, in two impartial hospital websites in Sweden. On this retrospective validation examine, Stratipath Breast might considerably enhance prognostic threat stratification for intermediate-risk breast most cancers sufferers, which might additional enhance higher project of adjuvant chemotherapy in such sufferers and keep away from under- and over-treatment of the sufferers.
“In examine III, we launched a technique referred to as the Wsi rEgion sElection method (WEEP) to spatially interpret the deep learning-based weakly supervised fashions. This technique can present insights into resolution making of such AI fashions that may be helpful in each analysis and diagnostic functions.
“In study IV, we developed a deep learning-based multi-stain prognostic risk score prediction model using routinely stained WSIs for breast cancer patients. We saw an improvement in prognostic risk score prediction when using the combination of local and spatial alignment of multiple stains in comparison to individual stains that can potentially provide a solution for better risk-stratification of breast cancer patients.”
What do you assume must be achieved in future analysis?
“I have an interdisciplinary background in bioengineering and have always been passionate about working at the intersection of biology and technology. Recent advancements in applying artificial intelligence to health care, particularly in improving diagnostics and providing personalized treatments for cancer patients, have captured my attention.”
Extra data:
Abhinav Sharma, Improvement and validation of novel deep learning- primarily based fashions for most cancers histopathology picture evaluation (2024). DOI: 10.69622/27291567.v1
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Karolinska Institutet
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Exploring novel deep learning-based fashions for most cancers histopathology picture evaluation (2025, January 15)
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