Role of diffusion-weighted magnetic resonance imaging in the differentiation of benign and malignant pulmonary lesions

Purpose: To evaluate the role of magnetic resonance (MRI) diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) measurement of solid and cystic pulmonary masses in differentiating benign from malignant lesions. Material and methods: The study included 41 patients with pulmonary masses, who underwent conventional MRI and DWI (b value 0, 500, and 1000 s/mm²) examinations with 1.5-T MRI. The diffusion signal and the mean ADC values of the solid and cystic lesions were obtained. Statistical analyses were performed with the Mann-Whitney U test (z), Pearson's chi-square test, and receiver operating characteristic (ROC) analysis. Results: Thirty-three lesions were malignant, and eight lesions were benign. The malignant masses showed significantly higher signal intensity on DWI than benign masses (p = 0.006), and the mean ADC value of malignant solid lesions was significantly lower than that of benign lesions (p = 0.02). By ROC analysis, an ADC cut-off value of 1.4 × 10-3 mm2/s was considered the threshold value, and the sensitivity and specificity were 93.8% and 75%, respectively. There was no significant difference between the ADC value of the cystic parts inside the benign and the malignant lesions. Conclusions: Diffusion-weighted MRI and measurement of ADC value can significantly differentiate between solid benign and malignant pulmonary masses

Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support

A Novel Machine Learning Approach for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer: Integration of Multimodal Radiomics With Clinical and Molecular Subtype Markers

The primary objective of this paper is to develop a machine learning-based approach capable of predicting the treatment response of neoadjuvant chemotherapy (NAC) to enhance breast cancer treatment management. The proposed system aims to predict NAC outcomes across three categories: pathological complete response (CR), partial response (PR), and stable disease (SD), by analyzing multimodal magnetic resonance images with clinical and molecular subtype markers. To ensure the comprehensiveness of our system design, texture radiomics were extracted from T1, T2, and STIR MRI modalities, along with functional radiomics from diffusion-weighted MRI at various b-values. The main rationale behind employing multiple b-values in collecting DW-MRI is to effectively capture the complexities of blood diffusion within the tumor microstructure. The proposed system comprises several key steps: (i) extracting texture and functional radiomics from T1, T2, STIR MRI, and DW-MRI data; (ii) identifying the most significant radiomics correlated with NAC treatment using a genetic algorithm; (iii) initially predicting the PR from alternative treatment responses utilizing the extracted textures and functional radiomics; and (iv) subsequently integrating clinical and molecular subtype markers with imaging radiomics to differentiate between CR and SD. Our proposed system is trained and validated through the utilization of a leave-one-subject-out (LOSO) cross-validation approach on various MRI scans from 109 subjects, of whom 27 had complete responses, 54 had partial responses, and 28 had no responses. The performance of the proposed system was assessed through the utilization of Cohen’s Kappa and accuracy metrics, achieving 81.31% and 88.07%, respectively. Our various experiments showed that integrating clinical and molecular subtype markers with radiomics highlights the proposed system’s efficiency in evaluating the tumor’s response to NAC efficiently, outperforming predictions based solely on individual radiomics.INDEX TERMS Breast cancer, neoadjuvant chemotherapy, MRI, DW-MRI, radiomics, tumor clinical markers, machine learning, treatment response prediction