AIMC Topic: Neoplasm Recurrence, Local

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Deep learning-based 3D quantitative total tumor burden predicts early recurrence of BCLC A and B HCC after resection.

European radiology
OBJECTIVES: This study aimed to evaluate the potential of deep learning (DL)-assisted automated three-dimensional quantitative tumor burden at MRI to predict postoperative early recurrence (ER) of hepatocellular carcinoma (HCC).

Novel Artificial Intelligence Combining Convolutional Neural Network and Support Vector Machine to Predict Colorectal Cancer Prognosis and Mutational Signatures From Hematoxylin and Eosin Images.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Reducing recurrence following radical resection of colon cancer without overtreatment or undertreatment remains a challenge. Postoperative adjuvant chemotherapy (Adj) is currently administered based solely on pathologic TNM stage. However, prognosis ...

Enhanced deep learning model for precise nodule localization and recurrence risk prediction following curative-intent surgery for lung cancer.

PloS one
PURPOSE: Radical surgery is the primary treatment for early-stage resectable lung cancer, yet recurrence after curative surgery is not uncommon. Identifying patients at high risk of recurrence using preoperative computed tomography (CT) images could ...

Ultrasound-based nomogram to predict the recurrence in papillary thyroid carcinoma using machine learning.

BMC cancer
BACKGROUND AND AIMS: The recurrence of papillary thyroid carcinoma (PTC) is not unusual and associated with risk of death. This study is aimed to construct a nomogram that combines clinicopathological characteristics and ultrasound radiomics signatur...

Interpretable artificial intelligence to optimise use of imatinib after resection in patients with localised gastrointestinal stromal tumours: an observational cohort study.

The Lancet. Oncology
BACKGROUND: Current guidelines recommend use of adjuvant imatinib therapy for many patients with gastrointestinal stromal tumours (GISTs); however, its optimal treatment duration is unknown and some patient groups do not benefit from the therapy. We ...

Deep Learning Classification and Quantification of Pejorative and Nonpejorative Architectures in Resected Hepatocellular Carcinoma from Digital Histopathologic Images.

The American journal of pathology
Liver resection is one of the best treatments for small hepatocellular carcinoma (HCC), but post-resection recurrence is frequent. Biotherapies have emerged as an efficient adjuvant treatment, making the identification of patients at high risk of rec...

Prediction of the short-term efficacy and recurrence of photodynamic therapy in the treatment of oral leukoplakia based on deep learning.

Photodiagnosis and photodynamic therapy
BACKGROUND: The treatment of oral leukoplakia (OLK) with aminolaevulinic acid photodynamic therapy (ALA-PDT) is widespread. Nonetheless, there is variation in efficacy. Therefore, this study constructed a model for predicting the short-term efficacy ...

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

Human genomics
BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with rec...