Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification.

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

Computed tomography-based radiomic to predict resectability in locally advanced pancreatic cancer treated with chemotherapy and radiotherapy.

BACKGROUND: Surgical resection after neoadjuvant treatment is the main driver for improved survival in locally advanced pancreatic cancer (LAPC). However, the diagnostic performance of computed tomography (CT) imaging to evaluate the residual tumour burden at restaging after neoadjuvant therapy is low due to the difficulty in distinguishing neoplastic tissue from fibrous scar or inflammation. In this context, radiomics has gained popularity over conventional imaging as a complementary clinical tool capable of providing additional, unprecedented information regarding the intratumor heterogeneity and the residual neoplastic tissue, potentially serving in the therapeutic decision-making process. AIM: To assess the capability of radiomic features to predict surgical resection in LAPC treated with neoadjuvant chemotherapy and radiotherapy. METHODS: Patients with LAPC treated with intensive chemotherapy followed by ablative radiation therapy were retrospectively reviewed. One thousand six hundred and fifty-five radiomic features were extracted from planning CT inside the gross tumour volume. Both extracted features and clinical data contribute to create and validate the predictive model of resectability status. Patients were repeatedly divided into training and validation sets. The discriminating performance of each model, obtained applying a LASSO regression analysis, was assessed with the area under the receiver operating characteristic curve (AUC). The validated model was applied to the entire dataset to obtain the most significant features. RESULTS: Seventy-one patients were included in the analysis. Median age was 65 years and 57.8% of patients were male. All patients underwent induction chemotherapy followed by ablative radiotherapy, and 19 (26.8%) ultimately received surgical resection. After the first step of variable selections, a predictive model of resectability was developed with a median AUC for training and validation sets of 0.862 (95%CI: 0.792-0.921) and 0.853 (95%CI: 0.706-0.960), respectively. The validated model was applied to the entire dataset and 4 features were selected to build the model with predictive performance as measured using AUC of 0.944 (95%CI: 0.892-0.996). CONCLUSION: The present radiomic model could help predict resectability in LAPC after neoadjuvant chemotherapy and radiotherapy, potentially integrating clinical and morphological parameters in predicting surgical resection.

Mechanical Properties of Nanoporous Metallic Ultrathin Films: A Paradigmatic Case.

Nanoporous ultrathin films, constituted by a slab less than 100 nm thick and a certain void volume fraction provided by nanopores, are emerging as a new class of systems with a wide range of possible applications, including electrochemistry, energy storage, gas sensing and supercapacitors. The film porosity and morphology strongly affect nanoporous films mechanical properties, the knowledge of which is fundamental for designing films for specific applications. To unveil the relationships among the morphology, structure and mechanical response, a comprehensive and non-destructive investigation of a model system was sought. In this review, we examined the paradigmatic case of a nanoporous, granular, metallic ultrathin film with comprehensive bottom-up and top-down approaches, both experimentals and theoreticals. The granular film was made of Ag nanoparticles deposited by gas-phase synthesis, thus providing a solvent-free and ultrapure nanoporous system at room temperature. The results, bearing generality beyond the specific model system, are discussed for several applications specific to the morphological and mechanical properties of the investigated films, including bendable electronics, membrane separation and nanofluidic sensing.

3T DCE-MRI Radiomics Improves Predictive Models of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVES: To test whether 3T MRI radiomics of breast malignant lesions improves the performance of predictive models of complete response to neoadjuvant chemotherapy when added to other clinical, histological and radiological information. METHODS: Women who consecutively had pre-neoadjuvant chemotherapy (NAC) 3T DCE-MRI between January 2016 and October 2019 were retrospectively included in the study. 18F-FDG PET-CT and histological information obtained through lesion biopsy were also available. All patients underwent surgery and specimens were analyzed. Subjects were divided between complete responders (Pinder class 1i or 1ii) and non-complete responders to NAC. Geometric, first order or textural (higher order) radiomic features were extracted from pre-NAC MRI and feature reduction was performed. Five radiomic features were added to other available information to build predictive models of complete response to NAC using three different classifiers (logistic regression, support vector machines regression and random forest) and exploring the whole set of possible feature selections. RESULTS: The study population consisted of 20 complete responders and 40 non-complete responders. Models including MRI radiomic features consistently showed better performance compared to combinations of other clinical, histological and radiological information. The AUC (ROC analysis) of predictors that did not include radiomic features reached up to 0.89, while all three classifiers gave AUC higher than 0.90 with the inclusion of radiomic information (range: 0.91-0.98). CONCLUSIONS: Radiomic features extracted from 3T DCE-MRI consistently improved predictive models of complete response to neo-adjuvant chemotherapy. However, further investigation is necessary before this information can be used for clinical decision making.

CT radiomic models to distinguish COVID-19 pneumonia from other interstitial pneumonias.

PURPOSE: To classify COVID-19, COVID-19-like and non-COVID-19 interstitial pneumonia using lung CT radiomic features. MATERIAL AND METHODS: CT data of 115 patients with respiratory symptoms suspected for COVID-19 disease were retrospectively analyzed. Based on the results of nasopharyngeal swab, patients were divided into two main groups, COVID-19 positive (C +) and COVID-19 negative (C-), respectively. C- patients, however, presented with interstitial lung involvement. A subgroup of C-, COVID-19-like (CL), were considered as highly suggestive of COVID pneumonia at CT. Radiomic features were extracted from the whole lungs. A dual machine learning (ML) model approach was used. The first one excluded CL patients from the training set, eventually included on the test set. The second model included the CL patients also in the training set. RESULTS: The first model classified C + and C- pneumonias with AUC of 0.83. CL median response (0.80) was more similar to C + (0.92) compared to C- (0.17). Radiomic footprints of CL were similar to the C + ones (possibly false negative swab test). The second model, however, merging C + with CL patients in the training set, showed a slight decrease in classification performance (AUC = 0.81). CONCLUSION: Whole lung ML models based on radiomics can classify C + and C- interstitial pneumonia. This may help in the correct management of patients with clinical and radiological stigmata of COVID-19, however presenting with a negative swab test. CL pneumonia was similar to C + pneumonia, albeit with slightly different radiomic footprints.

18F-FDG PET/CT Metrics Are Correlated to the Pathological Response in Esophageal Cancer Patients Treated With Induction Chemotherapy Followed by Neoadjuvant Chemo-Radiotherapy.

BACKGROUND AND OBJECTIVE: The aim of this study was to assess the ability of Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography (18F-FDG PET/CT) to provide functional information useful in predicting pathological response to an intensive neoadjuvant chemo-radiotherapy (nCRT) protocol for both esophageal squamous cell carcinoma (SCC) and adenocarcinoma (ADC) patients. MATERIAL AND METHODS: Esophageal carcinoma (EC) patients, treated in our Center between 2014 and 2018, were retrospectively reviewed. The nCRT protocol schedule consisted of an induction phase of weekly administered docetaxel, cisplatin, and 5-fluorouracil (TCF) for 3 weeks, followed by a concomitant phase of weekly TCF for 5 weeks with concurrent radiotherapy (50-50.4 Gy in 25-28 fractions). Three 18F-FDG PET/CT scans were performed: before (PET1) and after (PET2) induction chemotherapy (IC), and prior to surgery (PET3). Correlation between PET parameters [maximum and mean standardized uptake value (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG)], radiomic features and tumor regression grade (TGR) was investigated. RESULTS: Fifty-four patients (35 ADC, 19 SCC; 48 cT3/4; 52 cN+) were eligible for the analysis. Pathological response to nCRT was classified as major (TRG1-2, 41/54, 75.9%) or non-response (TRG3-4, 13/54, 24.1%). A major response was statistically correlated with SCC subtype (p = 0.02) and smaller tumor length (p = 0.03). MTV and TLG measured prior to IC (PET1) were correlated to TRG1-2 response (p = 0.02 and p = 0.02, respectively). After IC (PET2), SUVmean and TLG correlated with major response (p = 0.03 and p = 0.04, respectively). No significance was detected when relative changes of metabolic parameters between PET1 and PET2 were evaluated. At textural quantitative analysis, three independent radiomic features extracted from PET1 images ([JointEnergy and InverseDifferenceNormalized of GLCM and LowGrayLevelZoneEmphasis of GLSZM) were statistically correlated with major response (p < 0.0002). CONCLUSIONS: 18F-FDG PET/CT traditional metrics and textural features seem to predict pathologic response (TRG) in EC patients treated with induction chemotherapy followed by neoadjuvant chemo-radiotherapy. Further investigations are necessary in order to obtain a reliable predictive model to be used in the clinical practice.

Antimicrobial Nanostructured Coatings: A Gas Phase Deposition and Magnetron Sputtering Perspective.

Antimicrobial coatings are a promising strategy to counteract the spreading of multi-drug-resistant pathogens through cross-contamination of surfaces. Coatings with nanostructured characteristics can exploit the different antimicrobial mechanisms of nanomaterials provided the composition, the morphology and the mechanical properties of the film can be tuned by the specific synthesis methods. This review addresses the synthesis of antibacterial nanostructured coatings with a focus on physical synthesis methods. After a short description of the bacteria-NP interaction mechanism, leading to the killing of cells, paradigmatic examples of coatings, obtained by magnetron sputtering and supersonic cluster beam deposition, are discussed, with an emphasis on the possibility of combining different elements into the coating to widen the bactericidal spectrum.

Tailored Ag-Cu-Mg multielemental nanoparticles for wide-spectrum antibacterial coating.

Bactericidal nanoparticle coatings are very promising for hindering the indirect transmission of pathogens through cross-contaminated surfaces. The challenge, limiting their employment in nosocomial environments, is the ability of tailoring the coating's physicochemical properties, namely, composition, cytotoxicity, bactericidal spectrum, adhesion to the substrate, and consequent nanoparticles release into the environment. We have engineered a new family of nanoparticle-based bactericidal coatings comprising Ag, Cu, and Mg and synthesized by a green gas-phase technique. These coatings present wide-spectrum bactericidal activity on both Gram-positive and Gram-negative reference strains and tunable physicochemical properties of relevance in view of their "on-field" deployment. The link between material and functional properties is rationalized based on a multidisciplinary and multitechnique approach. Our results pave the way for engineering biofunctional, fully tunable nanoparticle coatings, exploiting an arbitrarily wide number of elements in a straightforward, eco-friendly, high-throughput, one-step process.

Photoacoustic Sensing of Trapped Fluids in Nanoporous Thin Films: Device Engineering and Sensing Scheme.

Accessing fluid infiltration in nanogranular coatings is an outstanding challenge, of relevance for applications ranging from nanomedicine to catalysis. A sensing platform, allowing quantifying the amount of fluid infiltrated in a nanogranular ultrathin coating, with thickness in the 10-40 nm range, is here proposed and theoretically investigated by multiscale modeling. The scheme relies on impulsive photoacoustic excitation of hypersonic mechanical breathing modes in engineered gas-phase-synthesized nanogranular metallic ultrathin films and time-resolved acousto-optical read-out of the breathing modes frequency shift upon liquid infiltration. A superior sensitivity, exceeding 26 × 103 cm2/g, is predicted upon equivalent areal mass loading of a few ng/mm2. The capability of the present scheme to discriminate among different infiltration patterns is discussed. The platform is an ideal tool to investigate nanofluidics in granular materials and naturally serves as a distributed nanogetter coating, integrating fluid sensing capabilities. The proposed scheme is readily extendable to other nanoscale and mesoscale porous materials.

Exploring the Optical and Morphological Properties of Ag and Ag/TiO2 Nanocomposites Grown by Supersonic Cluster Beam Deposition.

Nanocomposite systems and nanoparticle (NP) films are crucial for many applications and research fields. The structure-properties correlation raises complex questions due to the collective structure of these systems, often granular and porous, a crucial factor impacting their effectiveness and performance. In this framework, we investigate the optical and morphological properties of Ag nanoparticles (NPs) films and of Ag NPs/TiO2 porous matrix films, one-step grown by supersonic cluster beam deposition. Morphology and structure of the Ag NPs film and of the Ag/TiO2 (Ag/Ti 50-50) nanocomposite are related to the optical properties of the film employing spectroscopic ellipsometry (SE). We employ a simple Bruggeman effective medium approximation model, corrected by finite size effects of the nano-objects in the film structure to gather information on the structure and morphology of the nanocomposites, in particular porosity and average NPs size for the Ag/TiO2 NP film. Our results suggest that SE is a simple, quick and effective method to measure porosity of nanoscale films and systems, where standard methods for measuring pore sizes might not be applicable.