Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely

Skip-SCSE Multi-scale Attention and Co-learning Method for Oropharyngeal Tumor Segmentation on Multi-modal PET-CT Images

One of the primary treatment options for head and neck cancer is (chemo)radiation. Accurate delineation of the contour of the tumors is of great importance in the successful treatment of the tumor and in the prediction of patient outcomes. With this paper we take part in the HECKTOR 2021 challenge and we propose our methods for automatic tumor segmentation on PET and CT images of oropharyngeal cancer patients. To achieve this goal, we investigated different deep learning methods with the purpose of highlighting relevant image and modality related features, to refine the contour of the primary tumor. More specifically, we tested a Co-learning method [1] and a 3D Skip Spatial and Channel Squeeze and Excitation Multi-Scale Attention method (Skip-scSE-M), on the challenge dataset. The best results achieved on the test set were 0.762 mean Dice Similarity Score and 3.143 median of the Hausdorf Distance at 95 %.</p

Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.</p

Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Artificial Intelligence (AI) algorithms for automatic lung nodule detection and classification can assist radiologists in their daily routine of chest CT evaluation. Even though many AI algorithms for these tasks have already been developed, their implementation in the clinical workflow is still largely lacking. Apart from the significant number of false-positive findings, one of the reasons for that is the bias that these algorithms may contain. In this review, different types of biases that may exist in chest CT AI nodule detection and classification algorithms are listed and discussed. Examples from the literature in which each type of bias occurs are presented, along with ways to mitigate these biases. Different types of biases can occur in chest CT AI algorithms for lung nodule detection and classification. Mitigation of them can be very difficult, if not impossible to achieve completely

Standardization of Artificial Intelligence Development in Radiotherapy

Application of Artificial Intelligence (AI) tools has recently gained interest in the fields of medical imaging and radiotherapy. Even though there have been many papers published in these domains in the last few years, clinical assessment of the proposed AI methods is limited due to the lack of standardized protocols that can be used to validate the performance of the developed tools. Moreover, each stakeholder uses their own methods, tools, and evaluation criteria. Communication between different stakeholders is limited or absent, which makes it hard to easily exchange models between different clinics. These issues are not limited to radiotherapy but exist in every AI application domain. To deal with these issues, methods like the Machine Learning Canvas, Datasets for Datasheets, and Model cards have been developed. They aim to provide information of the whole creation pipeline of AI solutions, of the datasets used to develop AI, along with their biases, as well as to facilitate easier collaboration/communication between different stakeholders and facilitate the clinical introduction of AI. This work introduces the concepts of these 3 open-source solutions including the author's experiences applying them to AI applications for radiotherapy