Survival After Transarterial Radioembolization in Patients with Unresectable Intrahepatic Cholangiocarcinoma: An Updated Meta-analysis and Meta-regression.

PURPOSE: Transarterial radioembolization (TARE) has emerged as a promising therapeutic approach for unresectable intrahepatic cholangiocarcinoma (ICCA). We updated our previous meta-analysis with meta-regression to explore the efficacy of TARE in the context of ICCA. METHODS: We searched PubMed and Scopus for studies published up to September 1, 2023. The primary outcome was overall survival. Secondary outcomes were tumor overall response rate, severe adverse events, and downstaging to surgery. Meta-analysis employed a random-effects model, and meta-regression was utilized to explore sources of heterogeneity. RESULTS: We included 27 studies, involving 1365 patients. Pooled survival estimates at 1, 2, and 3 years were 52.6%, 27%, and 16.8%, respectively. Meta-regression revealed that the proportion of patients naïve to treatment was the only pre-TARE predictor of survival (1-, 2-, and 3-year survival of 70%, 45%, and 36% for treatment-naïve patients, mean survival 19.7 months vs. 44%, 18%, and 7% for non-naïve patients, mean survival 12.2 months). Overall response according to RECIST 1.1 and mRECIST was 19.6% and 67%, respectively. Effective downstaging to surgery was possible in varying rates (3-54%); the mean survival in these patients was 34.8 months (1-, 2-, and 3-year survival of 100%, 87%, and 64%). About 45.7% of patients experienced adverse events, but only 5.9% were severe. CONCLUSIONS: Our study benchmarked the survival rates of patients undergoing TARE for unresectable ICCA and showed that this is a valid option in these patients, especially if naïve to previous treatments. Downstaging to surgery is feasible in selected patients with promising results.

An Apparent Diffusion Coefficient-Based Machine Learning Model Can Improve Prostate Cancer Detection in the Grey Area of the Prostate Imaging Reporting and Data System Category 3: A Single-Centre Experience.

The Prostate Imaging and Reporting Data System (PI-RADS) has a key role in the management of prostate cancer (PCa). However, the clinical interpretation of PI-RADS 3 score lesions may be challenging and misleading, thus postponing PCa diagnosis to biopsy outcome. Multiparametric magnetic resonance imaging (mpMRI) radiomic analysis may represent a stand-alone noninvasive tool for PCa diagnosis. Hence, this study aims at developing a mpMRI-based radiomic PCa diagnostic model in a cohort of PI-RADS 3 lesions. We enrolled 133 patients with 155 PI-RADS 3 lesions, 84 of which had PCa confirmation by fusion biopsy. Local radiomic features were generated from apparent diffusion coefficient maps, and the four most informative were selected using LASSO, the Wilcoxon rank-sum test (p < 0.001), and support vector machines (SVMs). The selected features where augmented and used to train an SVM classifier, externally validated on a holdout subset. Linear and second-order polynomial kernels were exploited, and their predictive performance compared through receiver operating characteristics (ROC)-related metrics. On the test set, the highest performance, equally for both kernels, was specificity = 76%, sensitivity = 78%, positive predictive value = 80%, and negative predictive value = 74%. Our findings substantially improve radiologist interpretation of PI-RADS 3 lesions and let us advance towards an image-driven PCa diagnosis.

Prognostic value of integrated morphofunctional imaging methods in inoperable intrahepatic cholangiocarcinoma.

Introduction and aim: Intrahepatic cholangiocarcinoma (iCCA) is a disease characterized by rarity, heterogeneity, and high mortality, where surgical resection is often not possible. Nowadays, due to the recent introduction of new therapeutic options such as trans-arterial radioembolization (TARE), it is increasingly important to define the role of morphofunctional imaging methods for the prognostic stratification of patients affected by iCCA. The aim of the study was to verify the prognostic value of morphofunctional imaging methods at the baseline in patients with inoperable iCCA. Methods: In total, 45 patients with iCCA were sent to our center between January 2016 and March 2021 for being evaluated to be treated with TARE. All of them underwent both [18F]-FDG-PET/CT and contrast-enhanced CT (ceCT) in a single procedure and were included in our study. The inclusion criteria were as follows: a diagnosis of inoperable iCCA; both [18F]-FDG-PET/CT and ceCT scans; and washout from therapy for at least 2 months before baseline [18F]-FDG-PET/CT and ceCT scans. Both clinical and laboratory data and baseline imaging data (ceCT and [18F]-FDG-PET/CT) were collected. In particular, regarding clinical and laboratory data, we collected overall survival (OS), gender, age, prior therapies, liver function indices, and tumor markers. Regarding ceCT, we collected TNM staging, lesion diameter, volume, vascularization, and presence of intravascular necrosis. Regarding [18F]-FDG-PET/CT, we collected TNM staging, Standard-Uptake-Value max (SUVmax), Metabolic-Tumor-Volume (MTV), and Total-Lesion-Glycolysis (TLG=MTV*lesions SUVmean). Philips-Vue-PACS software was used, setting hepatic SUVmean as TLG threshold. Results: A statistically significant correlation was found between some examined parameters at morphofunctional investigations at the baseline and OS. [18F]-FDG-PET/CT parameters statistically correlated with OS were the stage of disease greater than M0 (p = 0.037), major lesion SUVmax (p = 0.010), MTV (p ≤ 0.001), and TLG (p < 0.001). Other parameters at ceCT correlated with OS were the stage of disease greater than T2 (p = 0.038), maximum lesion diameter (p = 0.07), volume of the major lesion (p = 0.016), and total volume of lesions (p = 0. 009). Biochemical parameters correlated with OS were gamma glutamyl transferase (GGT, p = 0.014), alkaline phosphatase (ALP, p = 0.019), carcinoembryonic antigen (CEA, p = 0.004), and carbohydrate antigen 19-9 (CA 19-9, p < 0.001). From the parameters estimated by the multivariate model, we derived a four-variable score for OS combining nodal involvement and SUVmax at [18F]-FDG-PET/CT, GGT, and CA 19-9 levels. Conclusion: Considering our data, performing integrated pre-therapy imaging is critical for the prognostic stratification of patients with iCCA.

Uterine Artery Embolization for the Treatment of Symptomatic Uterine Fibroids of Different Sizes: A Single Center Experience.

The present study aimed to evaluate the clinical and radiological 1-year outcomes of uterine artery embolization (UAE) performed in a selected population of women with symptomatic myomas and who do not wish to conceive. Between January 2004 and January 2018, a total of 62 patients with pre-menopausal status and with no wish to conceive in the future underwent UAE for the treatment of symptomatic fibroids. All the patients underwent magnetic resonance imaging (MRI) and/or transvaginal ultrasonography (TV-US) before and after the procedure at 1-year follow-up. Clinical and radiological parameters were recorded, stratifying the population into 3 groups according to the size of the dominant myoma (group 1: <50 mm; group 2: ≥50 and ≤80 mm; group 3: >80 mm). Mean fibroid diameter was significantly reduced (42.6% ± 21.6%) at 1-year follow-up, with excellent improvements in terms of both symptoms and quality of life. No significant difference was observed regarding baseline dimension and the number of myomas. No major complications were reported (2.5%). The present study confirms the safety and efficacy of UAE in the treatment of symptomatic fibroids in pre-menopausal women with no desire to conceive.

Radiomic Features from Post-Operative 18F-FDG PET/CT and CT Imaging Associated with Locally Recurrent Rectal Cancer: Preliminary Findings.

Locally Recurrent Rectal Cancer (LRRC) remains a major clinical concern; it rapidly invades pelvic organs and nerve roots, causing severe symptoms. Curative-intent salvage therapy offers the only potential for cure but it has a higher chance of success when LRRC is diagnosed at an early stage. Imaging diagnosis of LRRC is very challenging due to fibrosis and inflammatory pelvic tissue, which can mislead even the most expert reader. This study exploited a radiomic analysis to enrich, through quantitative features, the characterization of tissue properties, thus favoring an accurate detection of LRRC by Computed Tomography (CT) and 18F-FDG-Positron Emission Tomography/CT (PET/CT). Of 563 eligible patients undergoing radical resection (R0) of primary RC, 57 patients with suspected LRRC were included, 33 of which were histologically confirmed. After manually segmenting suspected LRRC in CT and PET/CT, 144 Radiomic Features (RFs) were generated, and RFs were investigated for univariate significant discriminations (Wilcoxon rank-sum test, p < 0.050) of LRRC from NO LRRC. Five RFs in PET/CT (p < 0.017) and two in CT (p < 0.022) enabled, individually, a clear distinction of the groups, and one RF was shared by PET/CT and CT. As well as confirming the potential role of radiomics to advance LRRC diagnosis, the aforementioned shared RF describes LRRC as tissues having high local inhomogeneity due to the evolving tissue's properties.

Improved Survival after Transarterial Radioembolisation for Hepatocellular Carcinoma Gives the Procedure Added Value.

BACKGROUND: Transarterial Radioembolisation (TARE) requires multidisciplinary experience and skill to be effective. The aim of this study was to identify determinants of survival in patients with hepatocellular carcinoma (HCC), focusing on learning curves, technical advancements, patient selection and subsequent therapies. METHODS: From 2005 to 2020, 253 patients were treated. TARE results achieved in an initial period (2005-2011) were compared to those obtained in a more recent period (2012-2020). To isolate the effect of the treatment period, differences between the two periods were balanced using "entropy balance". RESULTS: Of the 253 patients, 68 were treated before 2012 and 185 after 2012. In the second period, patients had an Eastern Cooperative Oncology Group (ECOG) Performance Status (PS) score of 1 (p = 0.025) less frequently, less liver involvement (p = 0.006) and a lesser degree of vascular invasion (p = 0.019). The median overall survival (OS) of patients treated before 2012 was 11.2 months and that of patients treated beginning in 2012 was 25.7 months. After reweighting to isolate the effect of the treatment period, the median OS of patients before 2012 increased to 16 months. CONCLUSIONS: Better patient selection, refinement of technique and adoption of personalised dosimetry improved survival after TARE. Conversely, sorafenib after TARE did not impact life expectancy.

Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions.

The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.

Interventional Radiological Management and Prevention of Complications after Pancreatic Surgery: Drainage, Embolization and Islet Auto-Transplantation.

Pancreatic surgery still remains burdened by high levels of morbidity and mortality with a relevant incidence of complications, even in high volume centers. This review highlights the interventional radiological management of complications after pancreatic surgery. The current literature regarding the percutaneous drainage of fluid collections due to pancreatic fistulas, percutaneous transhepatic biliary drainage due to biliary leaks and transcatheter embolization (or stent-graft) due to arterial bleeding is analyzed. Moreover, also, percutaneous intra-portal islet auto-transplantation for the prevention of pancreatogenic diabetes in case of extended pancreatic resection is also examined. Moreover, a topic not usually treated in other similar reviewsas percutaneous intra-portal islet auto-transplantation for the prevention of pancreatogenic diabetes in case of extended pancreatic resection is also one of our areas of focus. In islet auto-transplantation, the patient is simultaneously donor and recipient. Differently from islet allo-transplantation, it does not require immunosuppression, has no risk of rejection and is usually efficient with a small number of transplanted islets.

Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer.

BACKGROUND: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. METHODS: Forty-three patients under treatment in the IRCCS Sant'Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. RESULTS: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. CONCLUSIONS: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.

Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT).

BACKGROUND: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist's tumour segmentation. METHODS: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. RESULTS: The original 89 HCC nodules (32 MVI+ and 57 MVI-) became 169 (62 MVI+ and 107 MVI-) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI-), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70-0.93), p∼10-5), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. CONCLUSIONS: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Human, All Too Human? An All-Around Appraisal of the "Artificial Intelligence Revolution" in Medical Imaging.

Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice.

While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

Our study aimed to investigate whether radiomics on MRI sequences can differentiate responder (R) and non-responder (NR) patients based on the tumour regression grade (TRG) assigned after surgical resection in locally advanced rectal cancer (LARC) treated with neoadjuvant chemoradiotherapy (nCRT). Eighty-five patients undergoing primary staging with MRI were retrospectively evaluated, and 40 patients were finally selected. The ROIs were manually outlined in the tumour site on T2w sequences in the oblique-axial plane. Based on the TRG, patients were grouped as having either a complete or a partial response (TRG = (0,1), n = 15). NR patients had a minimal or poor nCRT response (TRG = (2,3), n = 25). Eighty-four local first-order radiomic features (RFs) were extracted from tumour ROIs. Only single RFs were investigated. Each feature was selected using univariate analysis guided by a one-tailed Wilcoxon rank-sum. ROC curve analysis was performed, using AUC computation and the Youden index (YI) for sensitivity and specificity. The RF measuring the heterogeneity of local skewness of T2w values from tumour ROIs differentiated Rs and NRs with a p-value ≈ 10-5; AUC = 0.90 (95%CI, 0.73-0.96); and YI = 0.68, corresponding to 80% sensitivity and 88% specificity. In conclusion, higher heterogeneity in skewness maps of the baseline tumour correlated with a greater benefit from nCRT.