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  • Ovid Technologies (Wolters Kluwer Health)  (2)
  • Koike, Kazuhiko  (2)
  • Okanoue, Takeshi  (2)
  • 2020-2024  (2)
Material
Publisher
  • Ovid Technologies (Wolters Kluwer Health)  (2)
Language
Years
  • 2020-2024  (2)
Year
Subjects(RVK)
  • 1
    In: Hepatology, Ovid Technologies (Wolters Kluwer Health)
    Abstract: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide images of biopsy-proven steatotic liver disease. Approach and Results: We included 639 patients who did not develop HCC for ≥7 years after biopsy (non-HCC class) and 46 patients who developed HCC 〈 7 years after biopsy (HCC class). Paired cases of the HCC and non-HCC classes matched by biopsy date and institution were used for training, and the remaining nonpaired cases were used for validation. The DL model was trained using deep convolutional neural networks with 28,000 image tiles cropped from whole-slide images of the paired cases, with an accuracy of 81.0% and an AUC of 0.80 for predicting HCC development. Validation using the nonpaired cases also demonstrated a good accuracy of 82.3% and an AUC of 0.84. These results were comparable to the predictive ability of logistic regression model using fibrosis stage. Notably, the DL model also detected the cases of HCC development in patients with mild fibrosis. The saliency maps generated by the DL model highlighted various pathological features associated with HCC development, including nuclear atypia, hepatocytes with a high nuclear-cytoplasmic ratio, immune cell infiltration, fibrosis, and a lack of large fat droplets. Conclusions: The ability of the DL model to capture subtle pathological features beyond fibrosis suggests its potential for identifying early signs of hepatocarcinogenesis in patients with steatotic liver disease.
    Type of Medium: Online Resource
    ISSN: 0270-9139
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2024
    detail.hit.zdb_id: 604603-4
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  • 2
    In: American Journal of Gastroenterology, Ovid Technologies (Wolters Kluwer Health), Vol. 118, No. 8 ( 2023-08), p. 1365-1372
    Abstract: Management of nonalcoholic steatohepatitis (NASH) is a currently unmet clinical need. Digital therapeutics (DTx) is an emerging class of medicine that delivers evidence-based therapeutic interventions. This study was aimed at investigating the efficacy of DTx in patients with NASH. METHODS: We conducted a multicenter, single-arm, 48-week trial in 19 patients with biopsy-confirmed NASH. All patients received a DTx intervention with a newly developed smartphone application. The primary endpoint was change in the nonalcoholic fatty liver disease activity score (NAS) without worsening of liver fibrosis. The secondary endpoints included improvement of the NAS by ≥2 points without worsening of liver fibrosis, change in the body weight, and regression of fibrosis. RESULTS: After the 48-week DTx intervention, improvement of the NAS was observed in 68.4% (13/19) of patients. The mean change in the NAS from baseline to the end of the intervention was −2.05 ± 1.96 ( P 〈 0.001 when compared with the threshold of −0.7). A decrease in the NAS by ≥ 2 points was achieved in 11 (57.9%). The average weight loss at the end of the intervention was 8.3% ( P 〈 0.001). Reduction of the fibrosis stage was observed in 58.3% when the analysis was limited to patients with stage F2/3 fibrosis. There were no serious adverse events that could be considered as being related to the DTx intervention. DISCUSSION: DTx for NASH was found to be highly efficacious and well-tolerated. Further evaluation of the DTx intervention for NASH in a phase 3 trial is warranted.
    Type of Medium: Online Resource
    ISSN: 0002-9270 , 1572-0241
    RVK:
    Language: English
    Publisher: Ovid Technologies (Wolters Kluwer Health)
    Publication Date: 2023
    detail.hit.zdb_id: 390122-1
    detail.hit.zdb_id: 2003227-4
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