Artificial intelligence-based, semi-automated segmentation for the extraction of ultrasound-derived radiomics features in breast cancer: a prospective multicenter study.

PURPOSE: To investigate the feasibility of an artificial intelligence (AI)-based semi-automated segmentation for the extraction of ultrasound (US)-derived radiomics features in the characterization of focal breast lesions (FBLs). MATERIAL AND METHODS: Two expert radiologists classified according to US BI-RADS criteria 352 FBLs detected in 352 patients (237 at Center A and 115 at Center B). An AI-based semi-automated segmentation was used to build a machine learning (ML) model on the basis of B-mode US of 237 images (center A) and then validated on an external cohort of B-mode US images of 115 patients (Center B). RESULTS: A total of 202 of 352 (57.4%) FBLs were benign, and 150 of 352 (42.6%) were malignant. The AI-based semi-automated segmentation achieved a success rate of 95.7% for one reviewer and 96% for the other, without significant difference (p = 0.839). A total of 15 (4.3%) and 14 (4%) of 352 semi-automated segmentations were not accepted due to posterior acoustic shadowing at B-Mode US and 13 and 10 of them corresponded to malignant lesions, respectively. In the validation cohort, the characterization made by the expert radiologist yielded values of sensitivity, specificity, PPV and NPV of 0.933, 0.9, 0.857, 0.955, respectively. The ML model obtained values of sensitivity, specificity, PPV and NPV of 0.544, 0.6, 0.416, 0.628, respectively. The combined assessment of radiologists and ML model yielded values of sensitivity, specificity, PPV and NPV of 0.756, 0.928, 0.872, 0.855, respectively. CONCLUSION: AI-based semi-automated segmentation is feasible, allowing an instantaneous and reproducible extraction of US-derived radiomics features of FBLs. The combination of radiomics and US BI-RADS classification led to a potential decrease of unnecessary biopsy but at the expense of a not negligible increase of potentially missed cancers.

MRI Insights in Breast Imaging.

In the world, breast cancer is the most commonly diagnosed cancer among women. Currently, MRI is the most sensitive breast imaging method for detecting breast cancer, although false positive rates are still an issue. To date, the accuracy of breast MRI is widely recognized across various clinical scenarios, in particular, staging of known cancer, screening for breast cancer in high-risk women, and evaluation of response to neoadjuvant chemotherapy. Since technical development and further clinical indications have expanded over recent years, dedicated breast radiologists need to constantly update their knowledge and expertise to remain confident and maintain high levels of diagnostic performance in breast MRI. This review aims to detail current and future applications of breast MRI, from technological requirements and advances to new multiparametric and abbreviated protocols, and ultrafast imaging, as well as current and future indications.

MR-guided focused ultrasound-induced blood-brain barrier opening for brain metastasis: a review.

Although the therapeutic armamentarium for brain metastases (BMs) has been expanded from innovative surgical techniques and radiotherapy to include targeted therapies and immunotherapy, the prognosis of BMs remains poor. Despite the proven efficacy of numerous compounds in preclinical studies, the limited penetration of promising therapeutic agents across the blood-brain barrier (BBB) remains an unaddressed issue. Recently, low-intensity magnetic resonance-guided focused ultrasound (MRgFUS) in combination with microbubbles has been shown to overcome vascular and cellular transport barriers in the brain and tumor microenvironment, resulting in increased drug diffusion and preliminary effective results. Preclinical studies have investigated the increased penetration of many therapeutic agents including doxorubicin, trastuzumab, and ipilimumab into the CNS with promising results. Furthermore, anticancer drugs combined with MRgFUS-induced BBB opening have been demonstrated to improve animal survival and slow tumor progression. Accordingly, the first clinical trial has recently been launched and hopefully the results will provide evidence for the safety and efficacy of drug delivery enhanced by MRgFUS-induced BBB opening in BMs. This review aims to provide an overview of transcranial low-intensity MRgFUS application for BBB disruption and a comprehensive overview of the most relevant evidence in the treatment of BMs.

A mutation-based radiomics signature predicts response to imatinib in Gastrointestinal Stromal Tumors (GIST).

Objectives: To develop a mutation-based radiomics signature to predict response to imatinib in Gastrointestinal Stromal Tumors (GISTs). Methods: Eighty-two patients with GIST were enrolled in this retrospective study, including 52 patients from one center that were used to develop the model, and 30 patients from a second center to validate it. Reference standard was the mutational status of tyrosine-protein kinase (KIT) and platelet-derived growth factor α (PDGFRA). Patients were dichotomized in imatinib sensitive (group 0 - mutation in KIT or PDGFRA, different from exon 18-D842V), and imatinib non-responsive (group 1 - PDGFRA exon 18-D842V mutation or absence of mutation in KIT/PDGFRA). Initially, 107 texture features were extracted from the tumor masks of baseline computed tomography scans. Different machine learning methods were then implemented to select the best combination of features for the development of the radiomics signature. Results: The best performance was obtained with the 5 features selected by the ANOVA model and the Bayes classifier, using a threshold of 0.36. With this setting the radiomics signature had an accuracy and precision for sensitive patients of 82 % (95 % CI:60-95) and 90 % (95 % CI:73-97), respectively. Conversely, a precision of 80 % (95 % CI:34-97) was obtained in non-responsive patients using a threshold of 0.9. Indeed, with the latter setting 4 patients out of 5 were correctly predicted as non-responders. Conclusions: The results are a first step towards using radiomics to improve the management of patients with GIST, especially when tumor tissue is unavailable for molecular analysis or when molecular profiling is inconclusive.

New microvascular ultrasound techniques: abdominal applications.

Microvascular ultrasound (MVUS) is a new ultrasound technique that allows the detection of slow-velocity flow, providing the visualization of the blood flow in small vessels without the need of intravenous contrast agent administration. This technology has been integrated in the most recent ultrasound equipment and applied for the assessment of vascularization. Compared to conventional color Doppler and power Doppler imaging, MVUS provides higher capability to detect intralesional flow. A growing number of studies explored the potential applications in hepatobiliary, genitourinary, and vascular pathologies. Different flow patterns can be observed in hepatic and renal focal lesions providing information on tumor vascularity and improving the differential diagnosis. This article aims to provide a detailed review on the current evidences and applications of MVUS in abdominal imaging.

Diagnostic performance of 2D-shear wave elastography in the diagnosis of breast cancer: a clinical appraisal of cutoff values.

PURPOSE: To assess the role of 2D-shear wave elastography (2D-SWE) in differentiating benign from malignant focal breast lesions (FBLs), providing new vendor-specific cutoff values. METHODS: 158 FBLs (size: 3.5-50 mm) detected in 151 women (age: 21-87 years) were prospectively evaluated by means 2D-SWE. For each lesion, an expert radiologist assessed US BI-RADS category and calculated the following four 2D-SWE parameters: (1) elasticity maximum (Emax); (2) mean elasticity (Emean); (3) minimum elasticity (Emin); (4) elasticity ratio (Eratio). US-guided core-biopsy was considered as standard of reference for all the FBLs classified as BI-RADS 4 or 5. For each 2D-SWE parameter, the optimal cutoff value for a diagnostic test was calculated using the Youden method. Diagnostic performance of the US BI-RADS and 2D-SWE parameters was calculated accordingly. RESULTS: 83/158 (52.5%) FBLs were benign and 75/158 (47.5%) were malignant. Statistically significant higher stiffness values were observed in malignant FBLs for all 2D-SWE parameters than in benign ones (p < 0.001). 2D-SWE cutoff values were 82.6 kPa, 66.0 kPa and 53.6 kPa, respectively, for Emax, Emean, Emin and 330.8% for Eratio. The 2D-SWE parameter showing the best diagnostic accuracy was Emax (85.44%). Considering US BI-RADS 3 (n = 60) and 4a (n = 32) FBLs, Emax and Emean showed the best diagnostic accuracy (85.87% for both), without a statistically significant decrease in sensitivity (p = 0.7003 and p = 1, respectively). CONCLUSION: Our study provides new vendor-specific cutoff values for 2D-SWE, suggesting its possible clinical use in the adjunctive assessment of category US-BI-RADS 3 and 4a breast masses.

Focal liver lesions in cirrhosis: Role of contrast-enhanced ultrasonography.

Contrast-enhanced ultrasound (CEUS) represents a great innovation for the evaluation of focal liver lesions (FLLs). The main advantage of CEUS is the real-time imaging examination and the very low toxicity in patients with renal failure. Liver cirrhosis has been recognized as a major risk factor for the onset of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). HCC in liver cirrhosis develops as the last step of a complex that leads to the gradual transformation from regenerative nodule through dysplastic nodule to HCC. In patients with liver cirrhosis, a surveillance program is recommended consisting of ultrasound (US) for detecting small focal lesions. A wide spectrum of benign and malignant lesions other than HCC may be found in the cirrhotic liver and their differentiation is important to avoid errors in staging diseases that may preclude potentially curative therapies. Several published studies have explored the value of CEUS in liver cirrhosis and they have been shown to have excellent diagnostic and prognostic performances for the evaluation of non-invasive and efficient diagnosis of FLLs in patients at high risk for liver malignancies. The purpose of this article is to describe and discuss CEUS imaging findings of FLLs including HCC and ICC, all of which occur in cirrhotic livers with varying prevalence.

Loco-regional treatment of hepatocellular carcinoma: Role of contrast-enhanced ultrasonography.

Hepatocellular carcinoma (HCC) is one of the few cancers for which locoregional treatments (LRTs) are included in international guidelines and are considered as a valid alternative to conventional surgery. According to Barcelona Clinic Liver Cancer classification, percutaneous treatments such as percutaneous ethanol injection, radiofrequency ablation and microwave ablation are the therapy of choice among curative treatments in patients categorized as very early and early stage, while transcatheter arterial chemoembolization is considered the better option for intermediate stage HCC. A precise assessment of treatment efficacy and surveillance is essential to optimize survival rate, whereas residual tumor requires additional treatment. Imaging modalities play a key role in this task. Currently, contrast-enhanced computed tomography/magnetic resonance imaging are considered the standard imaging modalities for this purpose. Contrast enhanced ultrasound (CEUS), using second generation contrast agents, plays an increasingly important role in detecting residual disease after LRTs. CEUS is a straightforward to perform, repeatable and cost-effective imaging modality for patients with renal failure or iodine allergies. Due to the ability to focus on single regions, CEUS can also provide high temporal resolution. Moreover, several studies have reported the same or better diagnostic accuracy as contrast-enhanced computed tomography for assessing tumor vascularity 1 mo after LRTs, and recently three-dimensional (3D)-CEUS has been reported as a promising technique to improve the evaluation of tumor response to therapy. Furthermore, CEUS could be used early after procedures in monitoring HCC treatments, but nowadays this indication is still debated, and data from literature are conflicting, especially after transcatheter arterial chemoembolization procedure.

Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients' risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these "big data" in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Diagnostic Performance of an Artificial Intelligence System in Breast Ultrasound.

OBJECTIVES: We study the performance of an artificial intelligence (AI) program designed to assist radiologists in the diagnosis of breast cancer, relative to measures obtained from conventional readings by radiologists. METHODS: A total of 10 radiologists read a curated, anonymized group of 299 breast ultrasound images that contained at least one suspicious lesion and for which a final diagnosis was independently determined. Separately, the AI program was initialized by a lead radiologist and the computed results compared against those of the radiologists. RESULTS: The AI program's diagnoses of breast lesions had concordance with the 10 radiologists' readings across a number of BI-RADS descriptors. The sensitivity, specificity, and accuracy of the AI program's diagnosis of benign versus malignant was above 0.8, in agreement with the highest performing radiologists and commensurate with recent studies. CONCLUSION: The trained AI program can contribute to accuracy of breast cancer diagnoses with ultrasound.

3D DCE-MRI Radiomic Analysis for Malignant Lesion Prediction in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: To develop and validate a radiomic model, with radiomic features extracted from breast Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) from a 1.5T scanner, for predicting the malignancy of masses with enhancement. Images were acquired using an 8-channel breast coil in the axial plane. The rationale behind this study is to show the feasibility of a radiomics-powered model that could be integrated into the clinical practice by exploiting only standard-of-care DCE-MRI with the goal of reducing the required image pre-processing (ie, normalization and quantitative imaging map generation). MATERIALS AND METHODS: 107 radiomic features were extracted from a manually annotated dataset of 111 patients, which was split into discovery and test sets. A feature calibration and pre-processing step was performed to find only robust non-redundant features. An in-depth discovery analysis was performed to define a predictive model: for this purpose, a Support Vector Machine (SVM) was trained in a nested 5-fold cross-validation scheme, by exploiting several unsupervised feature selection methods. The predictive model performance was evaluated in terms of Area Under the Receiver Operating Characteristic (AUROC), specificity, sensitivity, PPV and NPV. The test was performed on unseen held-out data. RESULTS: The model combining Unsupervised Discriminative Feature Selection (UDFS) and SVMs on average achieved the best performance on the blinded test set: AUROC = 0.725±0.091, sensitivity = 0.709±0.176, specificity = 0.741±0.114, PPV = 0.72±0.093, and NPV = 0.75±0.114. CONCLUSION: In this study, we built a radiomic predictive model based on breast DCE-MRI, using only the strongest enhancement phase, with promising results in terms of accuracy and specificity in the differentiation of malignant from benign breast lesions.

Advances in liver US, CT, and MRI: moving toward the future.

Over the past two decades, the epidemiology of chronic liver disease has changed with an increase in the prevalence of nonalcoholic fatty liver disease in parallel to the advent of curative treatments for hepatitis C. Recent developments provided new tools for diagnosis and monitoring of liver diseases based on ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI), as applied for assessing steatosis, fibrosis, and focal lesions. This narrative review aims to discuss the emerging approaches for qualitative and quantitative liver imaging, focusing on those expected to become adopted in clinical practice in the next 5 to 10 years. While radiomics is an emerging tool for many of these applications, dedicated techniques have been investigated for US (controlled attenuation parameter, backscatter coefficient, elastography methods such as point shear wave elastography [pSWE] and transient elastography [TE], novel Doppler techniques, and three-dimensional contrast-enhanced ultrasound [3D-CEUS]), CT (dual-energy, spectral photon counting, extracellular volume fraction, perfusion, and surface nodularity), and MRI (proton density fat fraction [PDFF], elastography [MRE], contrast enhancement index, relative enhancement, T1 mapping on the hepatobiliary phase, perfusion). Concurrently, the advent of abbreviated MRI protocols will help fulfill an increasing number of examination requests in an era of healthcare resource constraints.

New frontiers in liver ultrasound: From mono to multi parametricity.

Modern liver ultrasonography (US) has become a "one-stop shop" able to provide not only anatomic and morphologic but also functional information about vascularity, stiffness and other various liver tissue properties. Modern US techniques allow a quantitative assessment of various liver diseases. US scanning is no more limited to the visualized plane, but three-dimensional, volumetric acquisition and consequent post-processing are also possible. Further, US scan can be consistently merged and visualized in real time with Computed Tomography and Magnetic Resonance Imaging examinations. Effective and safe microbubble-based contrast agents allow a real time, dynamic study of contrast kinetic for the detection and characterization of focal liver lesions. Ultrasound can be used to guide loco-regional treatment of liver malignancies and to assess tumoral response either to interventional procedures or medical therapies. Microbubbles may also carry and deliver drugs under ultrasound exposure. US plays a crucial role in diagnosing, treating and monitoring focal and diffuse liver disease. On the basis of personal experience and literature data, this paper is aimed to review the main topics involving recent advances in the field of liver ultrasound.

Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature.

Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images.

Ultrasonographic Detection of Vascularity of Focal Breast Lesions: Microvascular Imaging Versus Conventional Color and Power Doppler Imaging.

To compare microvascular flow imaging (MVFI) to conventional Color-Doppler (CDI) and Power-Doppler (PDI) imaging in the detection of vascularity of Focal Breast Lesions (FBLs). A total of 180 solid FBLs (size: 3.5-45.2 mm) detected in 180 women (age: 21-87 years) were evaluated by means of CDI, PDI, and MVFI. Two blinded reviewers categorized lesion vascularity in absent or present, and vascularity pattern as (a) internal; (b) vessels in rim; (c) combined. The presence of a "penetrating vessel" was assessed separately. Differences in vascularization patterns (chi2 test) and intra- and inter-observer agreement (Fleiss method) were calculated. ROC analysis was performed to assess performance of each technique in differentiating benign from malignant lesions. About 103/180 (57.2%) FBLs were benign and 77/180 (42.8%) were malignant. A statistically significant (p < .001) increase in blood flow detection was observed for both readers with MVFI in comparison to either CDI or PDI. Benign FBLs showed mainly absence of vascularity (p = .02 and p = .01 for each reader, respectively), rim pattern (p < .001 for both readers) or combined pattern (p = .01 and p = .04). Malignant lesions showed a statistically significant higher prevalence of internal flow pattern (p < .001 for both readers). The prevalence of penetrating vessels was significantly higher with MVFI in comparison to either CDI or PDI (p < .001 for both readers) and in the malignant FBLs (p < .001). ROC analysis showed MVFI (AUC = 0.70, 95%CI = [0.64-0.77]) more accurate than CDI (AUC = 0.67, 95%CI = [0.60-0.74]) and PDI (AUC = 0.67, 95%CI = [0.60-0.74]) though not significantly (p = .5436). Sensitivity/Specificity values for MVFI, PDI, and CDI were 76.6%/64.1%, 59.7%/73.8% and 58.4%/74.8%, respectively. Inter-reader agreement with MVFI was always very good (k-score 0.85-0.96), whereas with CDI and PDI evaluation ranged from good to very good. No differences in intra-observer agreement were noted. MVFI showed a statistically significant increase in the detection of the vascularization of FBLs in comparison to Color and Power-Doppler.

Focal Pancreatic Lesions: Role of Contrast-Enhanced Ultrasonography.

The introduction of contrast-enhanced ultrasonography (CEUS) has led to a significant improvement in the diagnostic accuracy of ultrasound in the characterization of a pancreatic mass. CEUS, by using a blood pool contrast agent, can provide dynamic information concerning macro- and micro-circulation of focal lesions and of normal parenchyma, without the use of ionizing radiation. On the basis of personal experience and literature data, the purpose of this article is to describe and discuss CEUS imaging findings of the main solid and cystic pancreatic lesions with varying prevalence.

Assessment of morphological CT imaging features for the prediction of risk stratification, mutations, and prognosis of gastrointestinal stromal tumors.

OBJECTIVES: To investigate the correlation between CT imaging features and risk stratification of gastrointestinal stromal tumors (GISTs), prediction of mutation status, and prognosis. METHODS: This retrospective dual-institution study included patients with pathologically proven GISTs meeting the following criteria: (i) preoperative contrast-enhanced CT performed between 2008 and 2019; (ii) no treatments before imaging; (iii) available pathological analysis. Tumor risk stratification was determined according to the National Institutes of Health (NIH) 2008 criteria. Two readers evaluated the CT features, including enhancement patterns and tumor characteristics in a blinded fashion. The differences in distribution of CT features were assessed using univariate and multivariate analyses. Survival analyses were performed by using the Cox proportional hazard model, Kaplan-Meier method, and log-rank test. RESULTS: The final population included 88 patients (59 men and 29 women, mean age 60.5 ± 11.1 years) with 45 high-risk and 43 low-to-intermediate-risk GISTs (median size 6.3 cm). At multivariate analysis, lesion size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) were independently associated with the high-risk GISTs. Hyperenhancement was significantly more frequent in PDGFRα-mutated/wild-type GISTs compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). Ill-defined margins were associated with shorter progression-free survival (HR 9.66) at multivariate analysis, while ill-defined margins and hemorrhage remained independently associated with shorter overall survival (HR 44.41 and HR 30.22). Inter-reader agreement ranged from fair to almost perfect (k: 0.32-0.93). CONCLUSIONS: Morphologic contrast-enhanced CT features are significantly different depending on the risk status or mutations and may help to predict prognosis. KEY POINTS: • Lesions size ≥ 5 cm (OR: 10.52, p = 0.009) and enlarged feeding vessels (OR: 12.08, p = 0.040) are independent predictors of high-risk GISTs. • PDGFRα-mutated/wild-type GISTs demonstrate more frequently hyperenhancement compared to GISTs with KIT mutations (59.3% vs 23.0%, p = 0.004). • Ill-defined margins (hazard ratio 9.66) were associated with shorter progression-free survival at multivariate analysis, while ill-defined margins (hazard ratio 44.41) and intralesional hemorrhage (hazard ratio 30.22) were independently associated with shorter overall survival.

Type and Gene Location of KIT Mutations Predict Progression-Free Survival to First-Line Imatinib in Gastrointestinal Stromal Tumors: A Look into the Exon.

In previous studies on localized GISTs, KIT exon 11 deletions and mutations involving codons 557/558 showed an adverse prognostic influence on recurrence-free survival. In the metastatic setting, there are limited data on how mutation type and codon location might contribute to progression-free survival (PFS) variability to first-line imatinib treatment. We analyzed the type and gene location of KIT and PDGFRA mutations for 206 patients from a GIST System database prospectively collected at an Italian reference center between January 2005 and September 2020. By describing the mutational landscape, we focused on clinicopathological characteristics according to the critical mutations and investigated the predictive role of type and gene location of the KIT exon 11 mutations in metastatic patients treated with first-line imatinib. Our data showed a predictive impact of KIT exon 11 pathogenic variant on PFS to imatinib treatment: patients with deletion or insertion/deletion (delins) in 557/558 codons had a shorter PFS (median PFS: 24 months) compared to the patients with a deletion in other codons, or duplication/insertion/SNV (median PFS: 43 and 49 months, respectively) (p < 0.001). These results reached an independent value in the multivariate model, which showed that the absence of exon 11 deletions or delins 557/558, the female gender, primitive tumor diameter (≤5 cm) and polymorphonuclear leucocytosis (>7.5 109/L) were significant prognostic factors for longer PFS. Analysis of the predictive role of PDGFRA PVs showed no significant results. Our results also confirm the aggressive biology of 557/558 deletions/delins in the metastatic setting and allow for prediction at the baseline which GIST patients would develop resistance to first-line imatinib treatment earlier.

Liver stiffness quantification in biopsy-proven nonalcoholic fatty liver disease patients using shear wave elastography in comparison with transient elastography.

PURPOSE: This study prospectively assessed the performance of liver stiffness measurements using point shear-wave elastography (p-SWE) in comparison with transient elastography (TE) in patients with biopsy-proven nonalcoholic fatty liver disease (NAFLD). METHODS: Fifty-six consecutive adult patients with a histological diagnosis of NAFLD prospectively underwent TE and p-SWE on the same day. The median of 10 measurements (SWE-10), the first five (SWE-5), and the first three (SWE-3) measurements were analyzed for p-SWE. Liver biopsy was considered as the reference standard for liver fibrosis grade. Receiver operating characteristic (ROC) curves and areas under the ROC curves (AUROCs) were calculated to assess the performance of TE and p-SWE for the diagnosis of significant (F2-F4) and advanced fibrosis (F3-F4). RESULTS: Forty-six patients (27 men, 19 women; mean age, 54.7±9.1 years) had valid p-SWE and TE measurements. Twenty-seven patients (58.7%) had significant fibrosis and 18 (39.1%) had advanced fibrosis. For significant fibrosis, both SWE-10 (AUROC, 0.787; P=0.002) and SWE- 5 (AUROC, 0.809; P=0.001) provided higher diagnostic performance than TE (AUROC, 0.719; P=0.016) and SWE-3 (AUROC, 0.714; P=0.021), albeit without statistical significance (P=0.301). For advanced fibrosis, SWE-5 showed higher diagnostic performance (AUROC, 0.809; P<0.001) than TE (AUROC, 0.799; P<0.001), SWE-10 (AUROC, 0.797; P<0.001), and SWE-3 (AUROC, 0.736; P=0.003), although the differences were not statistically significant (P=0.496). The optimal SWE-10 and SWE-5 cutoff values were ≥8.4 and ≥7.8 for significant fibrosis, and ≥9.1 and ≥8.8 for advanced fibrosis, respectively. CONCLUSION: TE and p-SWE showed similar performance for the diagnosis of significant and advanced fibrosis in NAFLD patients.

Radiomics analysis of 18F-Choline PET/CT in the prediction of disease outcome in high-risk prostate cancer: an explorative study on machine learning feature classification in 94 patients.

OBJECTIVE: The aim of this study was (1) to investigate the application of texture analysis of choline PET/CT images in prostate cancer (PCa) patients and (2) to propose a machine-learning radiomics model able to select PET features predictive of disease progression in PCa patients with a same high-risk class at restaging. MATERIAL AND METHODS: Ninety-four high-risk PCa patients who underwent restaging Cho-PET/CT were analyzed. Follow-up data were recorded for a minimum of 13 months after the PET/CT scan. PET images were imported in LIFEx toolbox to extract 51 features from each lesion. A statistical system based on correlation matrix and point-biserial-correlation coefficient has been implemented for features reduction and selection, while Discriminant analysis (DA) was used as a method for features classification in a whole sample and sub-groups for primary tumor or local relapse (T), nodal disease (N), and metastatic disease (M). RESULTS: In the whole group, 2 feature (HISTO_Entropy_log10; HISTO_Energy_Uniformity) results were able to discriminate the occurrence of disease progression at follow-up, obtaining the best performance in DA classification (sensitivity 47.1%, specificity 76.5%, positive predictive value (PPV) 46.7%, and accuracy 67.6%). In the sub-group analysis, the best performance in DA classification for T was obtained by selecting 3 features (SUVmin; SHAPE_Sphericity; GLCM_Correlation) with a sensitivity of 91.6%, specificity 84.1%, PPV 79.1%, and accuracy 87%; for N by selecting 2 features (HISTO = _Energy Uniformity; GLZLM_SZLGE) with a sensitivity of 68.1%, specificity 91.4%, PPV 83%, and accuracy 82.6%; and for M by selecting 2 features (HISTO_Entropy_log10 - HISTO_Entropy_log2) with a sensitivity 64.4%, specificity 74.6%, PPV 40.6%, and accuracy 72.5%. CONCLUSION: This machine learning model demonstrated to be feasible and useful to select Cho-PET features for T, N, and M with valuable association with high-risk PCa patients' outcomes. KEY POINTS: • Artificial intelligence applications are feasible and useful to select Cho-PET features. • Our model demonstrated the presence of specific features for T, N, and M with valuable association with high-risk PCa patients' outcomes. • Further prospective studies are necessary to confirm our results and to develop the application of artificial intelligence in PET imaging of PCa.