Wang F
Predicting recurrence in osteosarcoma via a quantitative histological image classifier derived from tumour nuclear morphological features
Recurrence is the key factor affecting the prognosis of osteosarcoma. Currently, there is a lack of clinically useful tools to predict osteosarcoma recurrence. The application of pathological images for artificial intelligence-assisted accurate prediction of tumour outcomes is increasing.
ReadHybrid AI assistive diagnostic model permits rapid TBS classification of cervical liquid-based thin-layer cell smears
Technical advancements significantly improve earlier diagnosis of cervical cancer, but accurate diagnosis is still difficult due to various factors. We develop an artificial intelligence assistive diagnostic solution, AIATBS, to improve cervical liquid-based thin-layer cell smear diagnosis according to clinical TBS criteria.
ReadExploring MRI based radiomics analysis of intratumoral spatial heterogeneity in locally advanced nasopharyngeal carcinoma treated with intensity modulated radiotherapy
We hypothesized that spatial heterogeneity exists between recurrent and non-recurrent regions within a tumor. The aim of this study was to determine if there is a difference between radiomics features derived from recurrent versus non recurrent regions within the tumor based on pre-treatment MRI.
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