Expert system classifier for adaptive radiation therapy in prostate cancer

Abstract

A classifier-based expert system was developed to compare delivered and planned radiation therapy in prostate cancer patients. Its aim is to automatically identify patients that can benefit from an adaptive treatment strategy. The study predominantly addresses dosimetric uncertainties and critical issues caused by motion of hollow organs. 1200 MVCT images of 38 prostate adenocarcinoma cases were analyzed. An automatic daily re-contouring of structures (i.e. rectum, bladder and femoral heads), rigid/deformable registration and dose warping was carried out to simulate dose and volume variations during therapy. Support vector machine, K-means clustering algorithms and similarity index analysis were used to create an unsupervised predictive tool to detect incorrect setup and/or morphological changes as a consequence of inadequate patient preparation due to stochastic physiological changes, supporting clinical decision-making. After training on a dataset that was considered sufficiently dosimetrically stable, the system identified two equally sized macro clusters with distinctly different volumetric and dosimetric baseline properties and defined thresholds for these two clusters. Application to the test cohort resulted in 25% of the patients located outside the two macro clusters thresholds and which were therefore suspected to be dosimetrically unstable. In these patients, over the treatment course, mean volumetric changes of 30 and 40% for rectum and bladder were detected which possibly represents values justifying adjustment of patient preparation, frequent re-planning or a plan-of-the-day strategy. Based on our research, by combining daily IGRT images with rigid/deformable registration and dose warping, it is possible to apply a machine learning approach to the clinical setting obtaining useful information for a decision regarding an individualized adaptive strategy. Especially for treatments influenced by the movement of hollow organs, this could reduce inadequate treatments and possibly reduce toxicity, thereby increasing overall RT efficacy.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

“…Out of the 75 retrieved papers, 49 can be categorized as data-driven decision support approaches 10 – 58 . As regards to the addressed task, a large part of these contributions deals with prediction, intended as classification or regression 10 – 15 , 17 – 19 , 21 – 36 , 38 – 42 , 44 – 46 , 48 , 49 , 52 – 54 , 57 . One work is focused on association rule mining 20 , and one adopts statistics for risk analysis 55 .…”

Hierarchical clustering applied to automatic atlas based segmentation of 25 cardiac sub-structures

“…A further substantial improvement in delineation precision can finally be expected from using multimodality images in the same atlas (potentially on an integrated treatment platform such as the MR-LINAC) and machine learning approaches, as they are already being implemented and tested at various points along the RT treatment planning chain [34][35][36]. Machine learning could, in a first step for the software platform used here, be used to eliminate the manual intervention in the hierarchical clustering approach.…”

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

“…Recently, an expert system based on machine learning technic was developed to address dosimetric uncertainties caused by motion of hollow organs (e.g. rectum, bladder) for prostate cancer radiation therapy (Guidi et al 2017). These limited but innovative studies have demonstrated the great potential of employing RSDM for rectum dose-toxicity relationship analysis.…”