| 1. |
- Bulten, W, et al.
(författare)
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Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge
- 2022
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Ingår i: Nature medicine. - : Springer Science and Business Media LLC. - 1546-170X .- 1078-8956. ; 28:1, s. 154-
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Tidskriftsartikel (refereegranskat)abstract
- Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95% confidence interval (CI), 0.840–0.884) and 0.868 (95% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.
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| 2. |
- van der Kamp, Ananda, et al.
(författare)
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Artificial Intelligence in Pediatric Pathology: The Extinction of a Medical Profession or the Key to a Bright Future?
- 2022
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Ingår i: Pediatric and Developmental Pathology. - : SAGE PUBLICATIONS INC. - 1093-5266 .- 1615-5742. ; 25:4, s. 380-387
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Forskningsöversikt (refereegranskat)abstract
- Artificial Intelligence (AI) has become of increasing interest over the past decade. While digital image analysis (DIA) is already being used in radiology, it is still in its infancy in pathology. One of the reasons is that large-scale digitization of glass slides has only recently become available. With the advent of digital slide scanners, that digitize glass slides into whole slide images, many labs are now in a transition phase towards digital pathology. However, only few departments worldwide are currently fully digital. Digital pathology provides the ability to annotate large datasets and train computers to develop and validate robust algorithms, similar to radiology. In this opinionated overview, we will give a brief introduction into AI in pathology, discuss the potential positive and negative implications and speculate about the future role of AI in the field of pediatric pathology.
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| 3. |
- Pinckaers, Hans, et al.
(författare)
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Predicting biochemical recurrence of prostate cancer with artificial intelligence
- 2022
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Ingår i: Communications Medicine. - : Nature Portfolio. - 2730-664X. ; 2:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: The first sign of metastatic prostate cancer after radical prostatectomy is rising PSA levels in the blood, termed biochemical recurrence. The prediction of recurrence relies mainly on the morphological assessment of prostate cancer using the Gleason grading system. However, in this system, within-grade morphological patterns and subtle histopathological features are currently omitted, leaving a significant amount of prognostic potential unexplored.Methods: To discover additional prognostic information using artificial intelligence, we trained a deep learning system to predict biochemical recurrence from tissue in H&E-stained microarray cores directly. We developed a morphological biomarker using convolutional neural networks leveraging a nested case-control study of 685 patients and validated on an independent cohort of 204 patients. We use concept-based explainability methods to interpret the learned tissue patterns.Results: The biomarker provides a strong correlation with biochemical recurrence in two sets (n = 182 and n = 204) from separate institutions. Concept-based explanations provided tissue patterns interpretable by pathologists.Conclusions: These results show that the model finds predictive power in the tissue beyond the morphological ISUP grading.
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| 4. |
- Smit, Marloes A., et al.
(författare)
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Deep learning based tumor–stroma ratio scoring in colon cancer correlates with microscopic assessment
- 2023
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Ingår i: Journal of Pathology Informatics. - : Elsevier B.V.. - 2229-5089 .- 2153-3539. ; 14
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Tidskriftsartikel (refereegranskat)abstract
- Background: The amount of stroma within the primary tumor is a prognostic parameter for colon cancer patients. This phenomenon can be assessed using the tumor–stroma ratio (TSR), which classifies tumors in stroma-low (≤50% stroma) and stroma-high (>50% stroma). Although the reproducibility for TSR determination is good, improvement might be expected from automation. The aim of this study was to investigate whether the scoring of the TSR in a semi- and fully automated method using deep learning algorithms is feasible. Methods: A series of 75 colon cancer slides were selected from a trial series of the UNITED study. For the standard determination of the TSR, 3 observers scored the histological slides. Next, the slides were digitized, color normalized, and the stroma percentages were scored using semi- and fully automated deep learning algorithms. Correlations were determined using intraclass correlation coefficients (ICCs) and Spearman rank correlations. Results: 37 (49%) cases were classified as stroma-low and 38 (51%) as stroma-high by visual estimation. A high level of concordance between the 3 observers was reached, with ICCs of 0.91, 0.89, and 0.94 (all P < .001). Between visual and semi-automated assessment the ICC was 0.78 (95% CI 0.23–0.91, P-value 0.005), with a Spearman correlation of 0.88 (P < .001). Spearman correlation coefficients above 0.70 (N=3) were observed for visual estimation versus the fully automated scoring procedures. Conclusion: Good correlations were observed between standard visual TSR determination and semi- and fully automated TSR scores. At this point, visual examination has the highest observer agreement, but semi-automated scoring could be helpful to support pathologists. © 2023 The Authors
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| 5. |
- Hartman, Douglas Joseph, et al.
(författare)
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Value of Public Challenges for the Development of Pathology Deep Learning Algorithms
- 2020
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Ingår i: Journal of Pathology Informatics. - : Medknow Publications. - 2229-5089 .- 2153-3539. ; 11
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Tidskriftsartikel (refereegranskat)abstract
- The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools.
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| 7. |
- Sherman, Mark E., et al.
(författare)
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Serum hormone levels and normal breast histology among premenopausal women
- 2022
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Ingår i: Breast Cancer Research and Treatment. - New York, NY, United States : Springer. - 0167-6806 .- 1573-7217. ; 194, s. 149-158
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Tidskriftsartikel (refereegranskat)abstract
- Purpose Breast terminal duct lobular units (TDLUs) are the main source of breast cancer (BC) precursors. Higher serum concentrations of hormones and growth factors have been linked to increased TDLU numbers and to elevated BC risk, with variable effects by menopausal status. We assessed associations of circulating factors with breast histology among premenopausal women using artificial intelligence (AI) and preliminarily tested whether parity modifies associations.Methods Pathology AI analysis was performed on 316 digital images of H&E-stained sections of normal breast tissues from Komen Tissue Bank donors ages ≤ 45 years to assess 11 quantitative metrics. Associations of circulating factors with AI metrics were assessed using regression analyses, with inclusion of interaction terms to assess effect modification.Results Higher prolactin levels were related to larger TDLU area (p<0.001) and increased presence of adipose tissue proximate to TDLUs (p<0.001), with less significant positive associations for acini counts (p = 0.012), dilated acini (p = 0.043), capillary area (p = 0.014), epithelial area (p = 0.007), and mononuclear cell counts (p = 0.017). Testosterone levels were associated with increased TDLU counts (p<0.001), irrespective of parity, but associations differed by adipose tissue content. AI data for TDLU counts generally agreed with prior visual assessments.Conclusion Among premenopausal women, serum hormone levels linked to BC risk were also associated with quantitative features of normal breast tissue. These relationships were suggestively modified by parity status and tissue composition. We conclude that the microanatomic features of normal breast tissue may represent a marker of BC risk.
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