Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance.

Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study.

. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters.

Deep learning based joint fusion approach to exploit anatomical and functional brain information in autism spectrum disorders.

Background: The integration of the information encoded in multiparametric MRI images can enhance the performance of machine-learning classifiers. In this study, we investigate whether the combination of structural and functional MRI might improve the performances of a deep learning (DL) model trained to discriminate subjects with Autism Spectrum Disorders (ASD) with respect to typically developing controls (TD).

Material And Methods: We analyzed both structural and functional MRI brain scans publicly available within the ABIDE I and II data collections.

Deriving quantitative information from multiparametric MRI via Radiomics: Evaluation of the robustness and predictive value of radiomic features in the discrimination of low-grade versus high-grade gliomas with machine learning.

Purpose: Analysis pipelines based on the computation of radiomic features on medical images are widely used exploration tools across a large variety of image modalities. This study aims to define a robust processing pipeline based on Radiomics and Machine Learning (ML) to analyze multiparametric Magnetic Resonance Imaging (MRI) data to discriminate between high-grade (HGG) and low-grade (LGG) gliomas.

Methods: The dataset consists of 158 multiparametric MRI of patients with brain tumor publicly available on The Cancer Imaging Archive, preprocessed by the BraTS organization committee.

Multi-site harmonization of MRI data uncovers machine-learning discrimination capability in barely separable populations: An example from the ABIDE dataset.

Machine Learning (ML) techniques have been widely used in Neuroimaging studies of Autism Spectrum Disorders (ASD) both to identify possible brain alterations related to this condition and to evaluate the predictive power of brain imaging modalities. The collection and public sharing of large imaging samples has favored an even greater diffusion of the use of ML-based analyses. However, multi-center data collections may suffer the batch effect, which, especially in case of Magnetic Resonance Imaging (MRI) studies, should be curated to avoid confounding effects for ML classifiers and masking biases.

Distinct DNA Methylation Profiles in Ovarian Tumors: Opportunities for Novel Biomarkers.

Aberrant methylation of multiple promoter CpG islands could be related to the biology of ovarian tumors and its determination could help to improve treatment strategies. DNA methylation profiling was performed using the Methylation Ligation-dependent Macroarray (MLM), an array-based analysis. Promoter regions of 41 genes were analyzed in 102 ovarian tumors and 17 normal ovarian samples.

Promoter methylation and downregulated expression of the gene in ovarian carcinoma.

is a gene involved in the development of mesodermal derivatives. As the ovaries and the female reproductive system are of mesodermal origin, the aim of the present study was to determine the methylation status of the gene promoter and the expression levels of in ovarian carcinoma, which is the most lethal and aggressive type of gynecological tumor, in order to determine the role of in the pathogenesis of ovarian carcinoma. This alteration could be used to predict tumor development, progression, recurrence and therapeutic effects.

DNA methylation profiling of esophageal adenocarcinoma using Methylation Ligation-dependent Macroarray (MLM).

Most types of cancer cells are characterized by aberrant methylation of promoter genes. In this study, we described a rapid, reproducible, and relatively inexpensive approach allowing the detection of multiple human methylated promoter genes from many tissue samples, without the need of bisulfite conversion. The Methylation Ligation-dependent Macroarray (MLM), an array-based analysis, was designed in order to measure methylation levels of 58 genes previously described as putative biomarkers of cancer.

A pilot study evaluating serum pro-prostate-specific antigen in patients with rising PSA following radical prostatectomy.

[-2]pro-prostate-specific antigen (2pPSA), a proform of PSA, is a new marker in patients at risk of prostate cancer. We explored the potential role of 2pPSA in the identification of patients with metastatic progression following radical prostatectomy for prostate cancer. Seventy-six patients with biochemical (PSA) recurrence following radical prostatectomy were studied retrospectively.

Long-term exposure to dehydroepiandrosterone affects the transcriptional activity of the glucocorticoid receptor.

Although the antiglucocorticoid effects of dehydroepiandrosterone (DHEA) have been demonstrated in vivo in many systems, controversial results have been reported by in vitro studies. In order to elucidate the long-term antiglucocorticoid effects of DHEA in vitro in a context more physiological than what proposed by previous works, we set up a system consisting of a carcinoma cell line relying on endogenously produced glucocorticoid receptor (GR) and stably expressing a reporter gene ErbB-2 under the control of a GR-dependent MMTV promoter. These cells grown in presence of low levels of serum glucocorticoids (GC) showed a basal translocation and activity of endogenous GR.