Correlation of Pre- and Post-radio-chemotherapy MRI Texture Features With Tumor Response in Rectal Cancer.

BACKGROUND/AIM: The present study aimed to investigate radiomics features derived from magnetic resonance imaging (MRI) in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy (CRT). PATIENTS AND METHODS: We retrospectively evaluated data of 53 patients (32 males, 21 females) with T3/T4 or N+ rectal cancer who underwent MRI before and after CRT. Twenty-seven texture radiomics features were extracted from regions of interest, delimiting the tumor on T2-weighted images. RESULTS: All 27 radiomics features extracted before CRT showed a statistically significant association with the tumor regression grade (TRG) (p<0.05), whereas, after CRT, only the Cluster Prominence value was the only variable to predict TRG (p=0.037, r=0.291). CONCLUSION: All 27 features extracted before CRT were able to predict response to CRT and Cluster Prominence continued to be statistically significant even after CRT. The impact of radiomics features derived from MRI could be further investigated in patients with locally advanced rectal cancer.

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.