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- Strom, Peter, et al.
(författare)
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Artificial intelligence for diagnosis and grading of prostate cancer in biopsies : a population-based, diagnostic study
- 2020
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Ingår i: The Lancet Oncology. - : Elsevier. - 1470-2045 .- 1474-5488. ; 21:2, s. 222-232
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Tidskriftsartikel (refereegranskat)abstract
- BackgroundAn increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.MethodsWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50–69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.FindingsThe AI achieved an area under the receiver operating characteristics curve of 0·997 (95% CI 0·994–0·999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0·986 (0·972–0·996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0·96 (95% CI 0·95–0·97) for the independent test dataset and 0·87 (0·84–0·90) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0·62, which was within the range of the corresponding values for the expert pathologists (0·60–0·73).InterpretationAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.
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| 2. |
- Wang, Gang, et al.
(författare)
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Tumour extracellular vesicles and particles induce liver metabolic dysfunction
- 2023
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Ingår i: Nature. - : NATURE PORTFOLIO. - 0028-0836 .- 1476-4687. ; 618:7964, s. 374-382
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Tidskriftsartikel (refereegranskat)abstract
- Cancer alters the function of multiple organs beyond those targeted by metastasis(1,2). Here we show that inflammation, fatty liver and dysregulated metabolism are hallmarks of systemically affected livers in mouse models and in patients with extrahepatic metastasis. We identified tumour-derived extracellular vesicles and particles (EVPs) as crucial mediators of cancer-induced hepatic reprogramming, which could be reversed by reducing tumour EVP secretion via depletion of Rab27a. All EVP subpopulations, exosomes and principally exomeres, could dysregulate hepatic function. The fatty acid cargo of tumour EVPs-particularly palmitic acid-induced secretion of tumour necrosis factor (TNF) by Kupffer cells, generating a pro-inflammatory microenvironment, suppressing fatty acid metabolism and oxidative phosphorylation, and promoting fatty liver formation. Notably, Kupffer cell ablation or TNF blockade markedly decreased tumour-induced fatty liver generation. Tumour implantation or pre-treatment with tumour EVPs diminished cytochrome P450 gene expression and attenuated drug metabolism in a TNF-dependent manner. We also observed fatty liver and decreased cytochrome P450 expression at diagnosis in tumour-free livers of patients with pancreatic cancer who later developed extrahepatic metastasis, highlighting the clinical relevance of our findings. Notably, tumour EVP education enhanced side effects of chemotherapy, including bone marrow suppression and cardiotoxicity, suggesting that metabolic reprogramming of the liver by tumour-derived EVPs may limit chemotherapy tolerance in patients with cancer. Our results reveal how tumour-derived EVPs dysregulate hepatic function and their targetable potential, alongside TNF inhibition, for preventing fatty liver formation and enhancing the efficacy of chemotherapy.
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