Predicting Lung Cancer Patients' Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework.

Background: Selection of the best treatment modalities for lung cancer depends on many factors, like survival time, which are usually determined by imaging.

Objectives: To predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.

Material And Methods: Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired.

Design of a remote-control drug delivery implantable chip for cancer local on demand therapy using ionic polymer metal composite actuator.

Since the local, on demand, cancer therapy is a challenging clinical issue today, this paper presents the design, fabrication and characterization of a remotely controlled single reservoir drug delivery chip using Ionic Polymer Metal Composite (IPMC) actuator. Here, Drug release was externally programmed and controlled wirelessly on demand by a communication circuit. The transmitter and receiver circuits were designed to control the release/sealed status remotely even from 7 cm distance while the transmitter and receiver were coupled magnetically.