Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT.

. Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages.o develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early, overcoming the challenges associated with low contrast intensities and complex anatomical structures present in NCCT images.

Computed tomography and guidelines-based human-machine fusion model for predicting resectability of the pancreatic cancer.

Background And Aim: The study aims to develop a hybrid machine learning model for predicting resectability of the pancreatic cancer, which is based on computed tomography (CT) and National Comprehensive Cancer Network (NCCN) guidelines.

Method: We retrospectively studied 349 patients. One hundred seventy-one cases from Center 1 and 92 cases from Center 2 were used as the primary training cohort, and 66 cases from Center 3 and 20 cases from Center 4 were used as the independent test dataset.

A multimodal model fusing multiphase contrast-enhanced CT and clinical characteristics for predicting lymph node metastases of pancreatic cancer.

To develop a multimodal model that combines multiphase contrast-enhanced computed tomography (CECT) imaging and clinical characteristics, including experts' experience, to preoperatively predict lymph node metastasis (LNM) in pancreatic cancer patients.We proposed a new classifier fusion strategy (CFS) based on a new evidential reasoning (ER) rule (CFS-nER) by combining nomogram weights into a previous ER rule-based CFS. Three kernelled support tensor machine-based classifiers with plain, arterial, and venous phases of CECT as the inputs, respectively, were constructed.