Due to the complex biological and physical mechanisms, the correlations between the classification objects of clinical tasks and the medical imaging phenotype are always ambiguous and implied, which makes it difficult to train a powerful diagnostic convolutional neural network (CNN) model efficiently. In this study, we propose a generic multi-task learning (MTL) CNN framework to achieve higher classification accuracy and better generalization. The proposed framework is designed to carry out the major diagnostic task and several auxiliary tasks simultaneously. It encourages the models to learn more beneficial representation following the underlying relation among patients’ clinical characteristics, obvious imaging findings and quantitative imaging phenotype. We evaluate our approach on two clinical applications, namely advanced gastric cancer (AGC) serosa invasion diagnosis and discrimination of lung invasive adenocarcinoma manifesting as ground-glass nodule (GGN). Two datasets are utilized, which contain 357 AGC patients’ venous phase contrast-enhanced CT volumes and 236 GGN patients’ non-contrast CT volumes respectively. Several subjective CT morphology characteristics and common clinical characteristics are collected and used as the auxiliary tasks. To evaluate the generality of our strategy, CNNs with and without natural image-based pre-training are successively incorporated into the framework. The experimental results demonstrate that the proposed MTL CNN framework is able to improve the diagnostic performance significantly (7.4%-12.8% AUC increase and 3.5%-7.9% accuracy increase).
The Locally advanced rectal cancer (LARC) patients were routinely treated with neoadjuvant chemoradiotherapy (CRT)
firstly and received total excision afterwards. While, the LARC patients might relieve to T1N0M0/T0N0M0 stage after
the CRT, which would enable the patients be qualified for local excision. However, accurate pathological TNM stage
could only be obtained by the pathological examination after surgery. We aimed to conduct a Radiomics analysis of
Diffusion weighted Imaging (DWI) data to identify the patients in T1N0M0/T0N0M0 stages before surgery, in hope of
providing clinical surgery decision support. 223 routinely treated LARC patients in Beijing Cancer Hospital were
enrolled in current study. DWI data and clinical characteristics were collected after CRT. According to the pathological
TNM stage, the patients of T1N0M0 and T0N0M0 stages were labelled as 1 and the other patients were labelled as 0.
The first 123 patients in chronological order were used as training set, and the rest patients as validation set. 563 image
features extracted from the DWI data and clinical characteristics were used as features. Two-sample T test was conducted to pre-select the top 50% discriminating features. Least absolute shrinkage and selection operator
(Lasso)-Logistic regression model was conducted to further select features and construct the classification model. Based
on the 14 selected image features, the area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8781,
classification Accuracy (ACC) of 0.8432 were achieved in the training set. In the validation set, AUC of 0.8707, ACC
(ACC) of 0.84 were observed.
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