22 research outputs found
Variability of segmented prostate volume on MRI: impact on PSA density for prostate cancer diagnosis
No full textPurpose PSA density (PSAd), based on prostate volume (PV), is a decision-making parameter for prostate cancer (PCa) diagnosis and risk stratification. We assessed variability in prostate manual segmentation on MRI and its impact on PV and PSAd. Materials and Methods We retrospectively analyzed 68 treatment-na & iuml;ve patients, aged 66.2 +/- 6.9 years, with increased PSA and/or positive digital endorectal examination who underwent MRI, with available biopsy/follow-up. Three radiologists (R1, R2, R3) manually segmented the gland on T2-weighted images slice-by-slice. Dice similarity coefficient (DSC), Welch's t-test, and 95% confidence intervals (CIs) were used. Results Of 68 patients with a PSA of 7.59 +/- 4.80 ng/mL, 38 had biopsy-confirmed PCa, and the remaining 30 were negative on biopsy/follow-up. The segmentation time per patient ranged from 4 to 7 min. Pairs R1-R2, R1-R3, and R2-R3 showed a different number of segmented slices (p= 0.15 ng/mL/mL, variations in segmented PV impacted PSAd-based classification, resulting in 1 false negative for R1 and another false negative for R2 (false-negative rate for both 1/38, 2.63%, 95% CI 0.10-13.8%).Conclusion Segmentation of PV is a time-intensive task. Inter-reader variability can impact PSAd-based diagnosis of PCa. Automated prostate segmentation methods are welcome
Giovanna Garzoni Miniaturist at the Savoy Court: Imaging and Materials Investigations to Discover the Painting Technique
No full textThe exhibition “The Ladies of Art”, held at the Palazzo Reale in Milan in 2021, focused on the history of women artists during the 16th and 17th centuries. As part of the exhibition, a series of diagnostic analyses have been carried out on two paintings, thanks to the collaboration of several research institutions. The considered artworks consist of two paintings on parchments, realized by Giovanna Garzoni at the Savoy Court in the 17th century. Diagnostic analyses were performed using different, complementary, and non-invasive techniques: high-resolution multiband imaging, hyperspectral imaging, optical microscopy, X-ray fluorescence, and FORS spectrometry, combined with advanced post-processing techniques, in order to map and distinguish the pigments and the underdrawing of both the paintings. This research is the first conducted on these paintings and one of the few on the entire works of this important miniaturist. The results showed an incredibly meticulous painting technique, with a detailed metal point underdrawing and painstaking brushstrokes to describe the details with a high degree of realism. Precious materials, such as gold and lapislazuli, were identified and mapped. The findings of this work represent a new contribution of knowledge, which helps to lessen the lack of information for systematic studies on the artistic production of G. Garzoni
Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer
No full textObjective The objectives of our study were to assess the association of radiomic and genomic data with histology and patient outcome in non-small cell lung cancer (NSCLC). Methods In this retrospective single-centre observational study, we selected 151 surgically treated patients with adenocarcinoma or squamous cell carcinoma who performed baseline [18F] FDG PET/CT. A subgroup of patients with cancer tissue samples at the Institutional Biobank (n = 74/151) was included in the genomic analysis. Features were extracted from both PET and CT images using an in-house tool. The genomic analysis included detection of genetic variants, fusion transcripts, and gene expression. Generalised linear model (GLM) and machine learning (ML) algorithms were used to predict histology and tumour recurrence. Results Standardised uptake value (SUV) and kurtosis (among the PET and CT radiomic features, respectively), and the expression of TP63, EPHA10, FBN2, and IL1RAP were associated with the histotype. No correlation was found between radiomic features/genomic data and relapse using GLM. The ML approach identified several radiomic/genomic rules to predict the histotype successfully. The ML approach showed a modest ability of PET radiomic features to predict relapse, while it identified a robust gene expression signature able to predict patient relapse correctly. The best-performing ML radiogenomic rule predicting the outcome resulted in an area under the curve (AUC) of 0.87. Conclusions Radiogenomic data may provide clinically relevant information in NSCLC patients regarding the histotype, aggressiveness, and progression. Gene expression analysis showed potential new biomarkers and targets valuable for patient management and treatment. The application of ML allows to increase the efficacy of radiogenomic analysis and provides novel insights into cancer biology
Using Radiomics and Machine Learning Applied to MRI to Predict Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer
No full textNeoadjuvant chemotherapy plus radical surgery could be a safe alternative to chemo-radiation in cervical cancer patients who are not willing to receive radiotherapy. The response to neoadjuvant chemotherapy is the main factor influencing the need for adjunctive treatments and survival. In the present paper we aim to develop a machine learning model based on cervix magnetic resonance imaging (MRI) images to stratify the single-subject risk of cervical cancer. We collected MRI images from 72 subjects. Among these subjects, 28 patients (38.9%) belonged to the “Not completely responding” class and 44 patients (61.1%) belonged to the ’Completely responding‘ class according to their response to treatment. This image set was used for the training and cross-validation of different machine learning models. A robust radiomic approach was applied, under the hypothesis that the radiomic features could be able to capture the disease heterogeneity among the two groups. Three models consisting of three ensembles of machine learning classifiers (random forests, support vector machines, and k-nearest neighbor classifiers) were developed for the binary classification task of interest (“Not completely responding” vs. “Completely responding”), based on supervised learning, using response to treatment as the reference standard. The best model showed an ROC-AUC (%) of 83 (majority vote), 82.3 (mean) [79.9–84.6], an accuracy (%) of 74, 74.1 [72.1–76.1], a sensitivity (%) of 71, 73.8 [68.7–78.9], and a specificity (%) of 75, 74.2 [71–77.5]. In conclusion, our preliminary data support the adoption of a radiomic-based approach to predict the response to neoadjuvant chemotherapy
Advanced Imaging Analysis in Prostate MRI: Building a Radiomic Signature to Predict Tumor Aggressiveness
No full textRadiomics allows the extraction quantitative features from imaging, as imaging biomarkers of disease. The objective of this exploratory study is to implement a reproducible radiomic-pipeline for the extraction of a magnetic resonance imaging (MRI) signature for prostate cancer (PCa) aggressiveness. One hundred and two consecutive patients performing preoperative prostate multiparametric magnetic resonance imaging (mpMRI) and radical prostatectomy were enrolled. Multiparametric images, including T2-weighted (T2w), diffusion-weighted and dynamic contrast-enhanced images, were acquired at 1.5 T. Ninety-three imaging features (Ifs) were extracted from segmentation of index lesion. Ifs were ranked based on a stability rank and redundant Ifs were excluded. Using unsupervised hierarchical clustering, patients were grouped on the basis of similar radiomic patterns, whose association with Gleason Grade Group (GGG), extracapsular extension (ECE), and nodal involvement (pN) was tested. Signatures composed by IFs from T2w-images and Apparent Diffusion Coefficient (ADC) maps were tested for the prediction of GGG, ECE, and pN. T2w radiomic pattern was associated with pN, ECE, and GGG (p = 0.027, 0.05, 0.03) and ADC radiomic pattern was associated with GGG (p = 0.004). The best performance was reached by the signature combing IFs from multiparametric images (0.88, 0.89, and 0.84 accuracy for GGG, pN, and ECE). A reliable multiparametric MRI radiomic signature was extracted, potentially able to predict PCa aggressiveness, to be further validated on an independent sample
Artificial intelligence applied on chest X-ray can aid in the diagnosis of {COVID}-19 infection: a first experience from Lombardy, Italy
No full textBackground: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods: We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results: At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions: This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance
Radiomics and Molecular Classification in Endometrial Cancer (The ROME Study): A Step Forward to a Simplified Precision Medicine
No full textMolecular/genomic profiling is the most accurate method to assess prognosis of endometrial cancer patients. Radiomic profiling allows for the extraction of mineable high-dimensional data from clinical radiological images, thus providing noteworthy information regarding tumor tissues. Interestingly, the adoption of radiomics shows important results for screening, diagnosis and prognosis, across various radiological systems and oncologic specialties. The central hypothesis of the prospective trial is that combining radiomic features with molecular features might allow for the identification of various classes of risks for endometrial cancer, e.g., predicting unfavorable molecular/genomic profiling. The rationale for the proposed research is that once validated, radiomics applied to ultrasonographic images would be an effective, innovative and inexpensive method for tailoring operative and postoperative treatment modalities in endometrial cancer. Patients with newly diagnosed endometrial cancer will have ultrasonographic evaluation and radiomic analysis of the ultrasonographic images. We will correlate radiomic features with molecular/genomic profiling to classify prognosis
AI applications to medical images: From machine learning to deep learning
No full textPurpose: Artificial intelligence (AI) models are playing an increasing role in biomedical research and healthcare services. This review focuses on challenges points to be clarified about how to develop AI applications as clinical decision support systems in the real-world context. Methods: A narrative review has been performed including a critical assessment of articles published between 1989 and 2021 that guided challenging sections. Results: We first illustrate the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches. For ML/radiomics, the phases of feature selection and of training, validation, and testing are described. DL models are presented as multi-layered artificial/convolutional neural networks, allowing us to directly process images. The data curation section includes technical steps such as image labelling, image annotation (with segmentation as a crucial step in radiomics), data harmonization (enabling compensation for differences in imaging protocols that typically generate noise in non-AI imaging studies) and federated learning. Thereafter, we dedicate specific sections to: sample size calculation, considering multiple testing in AI approaches; procedures for data augmentation to work with limited and unbalanced datasets; and the interpretability of AI models (the so-called black box issue). Pros and cons for choosing ML versus DL to implement AI applications to medical imaging are finally presented in a synoptic way. Conclusions: Biomedicine and healthcare systems are one of the most important fields for AI applications and medical imaging is probably the most suitable and promising domain. Clarification of specific challenging points facilitates the development of such systems and their translation to clinical practic
Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia
No full textWe assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia
Giotto Unveiled: New Developments in Imaging and Elemental Analysis Techniques for Cultural Heritage
No full textThe Giotto’s masterpiece God the Father with Angels, never investigated till now, was studied by our team of local researchers, involved in application of scientific methods for cultural heritage since many years. Exploiting the integration of different knowledges, technologies and resources of our team, we were able to provide data to understand the painting technique, the pigment used and the underdrawing of this Giotto’s painting. We performed the following non-invasive analyses: Macro-XRF scanning (MA-XRF), Fiber optic reflectance spectroscopy (FORS), high resolution IR scanning reflectography, infrared false color (IRFC). Only portable instrumentations were used, with operating times compatible with the opening hours of exhibition. In particular, the analytical campaign was the opportunity to test the portable IR scanning prototype based on a peculiar spherical scanning system characterized by light weight and low cost motorized head. The analytical results revealed a painting technique already used by Giotto and based on different superimposed pigment layers. By combining the effectiveness of scanning portable-XRF (pXRF) with the responsive of image spectroscopic analysis, we move step by step toward the discovery of Giotto’s palette, with particular attention to the flesh tones in God the Father with Angels. The imaging data support the hypothesis of a detailed underlying sketch that includes also a drawing characterized by larger brush signs; the use of patrones for the face of “God” was supposed thanks to comparison with other Giotto masterpieces
