Prediction of placenta accreta spectrum in patients with placenta previa using a clinical, US and MRI combined model: A retrospective study with external validation.

PURPOSE: To build and validate a predictive model of placental accreta spectrum (PAS) in patients with placenta previa (PP) combining clinical risk factors (CRF) with US and MRI signs. METHOD: Our retrospective study included patients with PP from two institutions. All patients underwent US and MRI examinations for suspicion of PAS. CRF consisting of maternal age, cesarean section number, smoking and hypertension were retrieved. US and MRI signs suggestive of PAS were evaluated. Logistic regression analysis was performed to identify CRF and/or US and MRI signs associated with PAS considering histology as the reference standard. A nomogram was created using significant CRF and imaging signs at multivariate analysis, and its diagnostic accuracy was measured using the area under the binomial ROC curve (AUC), and the cut-off point was determined by Youden's J statistic. RESULTS: A total of 171 patients were enrolled from two institutions. Independent predictors of PAS included in the nomogram were: 1) smoking and number of previous CS among CRF; 2) loss of the retroplacental clear space at US; 3) intraplacental dark bands, focal interruption of the myometrial border and placental bulging at MRI. A PAS-prediction nomogram was built including these parameters and an optimal cut-off of 14.5 points was identified, showing the highest sensitivity (91%) and specificity (88%) with an AUC value of 0.95 (AUC of 0.80 in the external validation cohort). CONCLUSION: A nomogram-based model combining CRF with US and MRI signs might help to predict PAS in PP patients, with MRI contributing more than US as imaging evaluation.

Role of advanced imaging techniques in the evaluation of oncological therapies in patients with colorectal liver metastases.

In patients with colorectal liver metastasis (CRLMs) unsuitable for surgery, oncological treatments, such as chemotherapy and targeted agents, can be performed. Cross-sectional imaging [computed tomography (CT), magnetic resonance imaging (MRI), 18-fluorodexoyglucose positron emission tomography with CT/MRI] evaluates the response of CRLMs to therapy, using post-treatment lesion shrinkage as a qualitative imaging parameter. This point is critical because the risk of toxicity induced by oncological treatments is not always balanced by an effective response to them. Consequently, there is a pressing need to define biomarkers that can predict treatment responses and estimate the likelihood of drug resistance in individual patients. Advanced quantitative imaging (diffusion-weighted imaging, perfusion imaging, molecular imaging) allows the in vivo evaluation of specific biological tissue features described as quantitative parameters. Furthermore, radiomics can represent large amounts of numerical and statistical information buried inside cross-sectional images as quantitative parameters. As a result, parametric analysis (PA) translates the numerical data contained in the voxels of each image into quantitative parameters representative of peculiar neoplastic features such as perfusion, structural heterogeneity, cellularity, oxygenation, and glucose consumption. PA could be a potentially useful imaging marker for predicting CRLMs treatment response. This review describes the role of PA applied to cross-sectional imaging in predicting the response to oncological therapies in patients with CRLMs.

David Against Goliath: Direct Comparison of Handheld Bowel Sonography and Magnetic Resonance Enterography for Diagnosis of Crohn's Disease.

BACKGROUND: Diagnosis of Crohn's disease (CD) requires ileo-colonoscopy (IC) and cross-sectional evaluation. Recently, "echoscopy" has been used effectively in several settings, although data about its use for CD diagnosis are still limited. Our aim was to evaluate the diagnostic accuracy of handheld bowel sonography (HHBS) in comparison with magnetic resonance enterography (MRE) for CD diagnosis. METHODS: From September 2019 to June 2021, we prospectively recruited consecutive subjects attending our third level IBD Unit for suspected CD. Patients underwent IC, HHBS, and MRE in random order with operators blinded about the result of the other procedures. Bivariate correlation between MRE and HHBS was calculated by Spearman coefficient (r). To test the consistency between MRE and HHBS for CD location and complications, the Cohen's k measure was applied. RESULTS: Crohn's disease diagnosis was made in 48 out of 85 subjects (56%). Sensitivity, specificity, positive predictive values, and negative predictive values for CD diagnosis were 87.50%, 91.89%, 93.33%, and 85% for HHBS; and 91.67%, 94.59%, 95.65%, and 89.74% for MRE, without significant differences in terms of diagnostic accuracy (89.41% for HHBS vs 92.94% for MRE, P = NS). Magnetic resonance enterography was superior to HHBS in defining CD extension (r = 0.67; P < .01) with a better diagnostic performance than HHBS for detecting location (k = 0.81; P < .01), strictures (k = 0.75; P < .01), abscesses (k = 0.68; P < .01), and fistulas (k = 0.65; P < .01). CONCLUSION: Handheld bowel sonography and MRE are 2 accurate and noninvasive procedures for diagnosis of CD, although MRE is more sensitive in defining extension, location, and complications. Handheld bowel sonography could be used as effective ambulatory (or out-of-office) screening tool for identifying patients to refer for MRE examination due to high probability of CD diagnosis.

Artificial Intelligence and Radiomics for Endometrial Cancer MRI: Exploring the Whats, Whys and Hows.

Endometrial cancer (EC) is intricately linked to obesity and diabetes, which are widespread risk factors. Medical imaging, especially magnetic resonance imaging (MRI), plays a major role in EC assessment, particularly for disease staging. However, the diagnostic performance of MRI exhibits variability in the detection of clinically relevant prognostic factors (e.g., deep myometrial invasion and metastatic lymph nodes assessment). To address these challenges and enhance the value of MRI, radiomics and artificial intelligence (AI) algorithms emerge as promising tools with a potential to impact EC risk assessment, treatment planning, and prognosis prediction. These advanced post-processing techniques allow us to quantitatively analyse medical images, providing novel insights into cancer characteristics beyond conventional qualitative image evaluation. However, despite the growing interest and research efforts, the integration of radiomics and AI to EC management is still far from clinical practice and represents a possible perspective rather than an actual reality. This review focuses on the state of radiomics and AI in EC MRI, emphasizing risk stratification and prognostic factor prediction, aiming to illuminate potential advancements and address existing challenges in the field.

MRI radiomics: A machine learning approach for the risk stratification of endometrial cancer patients.

PURPOSE: To investigate radiomics and machine learning (ML) as possible tools to enhance MRI-based risk stratification in patients with endometrial cancer (EC). METHOD: From two institutions, 133 patients (Institution1 = 104 and Institution2 = 29) with EC and pre-operative MRI were retrospectively enrolled and divided in two a low-risk and a high-risk group according to EC stage and grade. T2-weighted (T2w) images were three-dimensionally annotated to obtain volumes of interest of the entire tumor. A PyRadiomics based and previously validated pipeline was used to extract radiomics features and perform feature selection. In particular, feature stability, variance and pairwise correlation were analyzed. Then, the least absolute shrinkage and selection operator technique and recursive feature elimination were used to obtain the final feature set. The performance of a Support Vector Machine (SVM) algorithm was assessed on the dataset from Institution 1 via 2-fold cross-validation. Then, the model was trained on the entire Institution 1 dataset and tested on the external test set from Institution 2. RESULTS: In total, 1197 radiomics features were extracted. After the exclusion of unstable, low variance and intercorrelated features least absolute shrinkage and selection operator and recursive feature elimination identified 4 features that were used to build the predictive ML model. It obtained an accuracy of 0.71 and 0.72 in the train and test sets respectively. CONCLUSIONS: Whole-lesion T2w-derived radiomics showed encouraging results and good generalizability for the identification of low-risk EC patients.

Qualitative Heterogeneous Signal Drop on Chemical Shift (CS) MR Imaging: Correlative Quantitative Analysis between CS Signal Intensity Index and Contrast Washout Parameters Using T1-Weighted Sequences.

The aim of this study was to calculate MRI quantitative parameters extracted from chemical-shift (CS) and dynamic contrast-enhanced (DCE) T1-weighted (T1-WS) images of adrenal lesions (AL) with qualitative heterogeneous signal drop on CS T1-WS and compare them to those of AL with homogeneous or no signal drop on CS T1-WS. On 3 T MRI, 65 patients with a total of 72 AL were studied. CS images were qualitatively assessed for grouping AL as showing homogeneous (Group 1, n = 19), heterogeneous (Group 2, n = 23), and no (Group 3, n = 30) signal drop. Histopathology or follow-up data served as reference standard to classify AL. ROIs were drawn both on CS and DCE images to obtain adrenal CS signal intensity index (ASII), absolute (AWO), and relative washout (RWO) values. Quantitative parameters (QP) were compared with ANOVA analysis and post hoc Dunn's test. The performance of QP to classify AL was assessed with receiver operating characteristic analysis. CS ASII values were significantly different among the three groups (p < 0.001) with median values of 71%, 53%, and 3%, respectively. AWO/RWO values were similar in Groups 1 (adenomas) and 2 (benign AL) but significantly (p < 0.001) lower in Group 3 (20 benign AL and 10 malignant AL). With cut-offs, respectively, of 60% (Group 1 vs. 2), 20% (Group 2 vs. 3), and 37% (Group 1 vs. 3), CS ASII showed areas under the curve of 0.85, 0.96, and 0.93 for the classification of AL, overall higher than AWO/RWO. In conclusion, AL with qualitative heterogeneous signal drop at CS represent benign AL with QP by DCE sequence similar to those of AL with homogeneous signal drop at CS, but different to those of AL with no signal drop at CS; ASII seems to be the only quantitative parameter able to differentiate AL among the three different groups.

Radiomics and machine learning applications in rectal cancer: Current update and future perspectives.

The high incidence of rectal cancer in both sexes makes it one of the most common tumors, with significant morbidity and mortality rates. To define the best treatment option and optimize patient outcome, several rectal cancer biological variables must be evaluated. Currently, medical imaging plays a crucial role in the characterization of this disease, and it often requires a multimodal approach. Magnetic resonance imaging is the first-choice imaging modality for local staging and restaging and can be used to detect high-risk prognostic factors. Computed tomography is widely adopted for the detection of distant metastases. However, conventional imaging has recognized limitations, and many rectal cancer characteristics remain assessable only after surgery and histopathology evaluation. There is a growing interest in artificial intelligence applications in medicine, and imaging is by no means an exception. The introduction of radiomics, which allows the extraction of quantitative features that reflect tumor heterogeneity, allows the mining of data in medical images and paved the way for the identification of potential new imaging biomarkers. To manage such a huge amount of data, the use of machine learning algorithms has been proposed. Indeed, without prior explicit programming, they can be employed to build prediction models to support clinical decision making. In this review, current applications and future perspectives of artificial intelligence in medical imaging of rectal cancer are presented, with an imaging modality-based approach and a keen eye on unsolved issues. The results are promising, but the road ahead for translation in clinical practice is rather long.

Prediction of placenta accreta spectrum in patients with placenta previa using clinical risk factors, ultrasound and magnetic resonance imaging findings.

OBJECTIVES: To predict placental accreta spectrum (PAS) in patients with placenta previa (PP) evaluating clinical risk factors (CRF), ultrasound (US) and magnetic resonance imaging (MRI) findings. METHODS: Seventy patients with PP were retrospectively selected. CRF were retrieved from medical records. US and MRI images were evaluated to detect imaging signs suggestive of PAS. Univariable analysis was performed to identify CRF, US and MRI signs associated with PAS considering histology as standard of reference. Receiver operating characteristic curve (ROC) analysis was performed, and the area under the curve (AUC) was calculated. Multivariable analysis was also performed. RESULTS: At univariable analysis, the number of previous cesarean section, smoking, loss of the retroplacental clear space, myometrial thinning < 1 mm, placental lacunae, intraplacental dark bands (IDB), focal interruption of myometrial border (FIMB) and abnormal vascularity were statistically significant. The AUC in predicting PAS progressively increased using CRF, US and MRI signs (0.69, 0.79 and 0.94, respectively; p < 0.05); the accuracy of MRI alone was similar to that obtained combining CRF, US and MRI variables (AUC = 0.97) and was significantly higher (p < 0.05) than that combining CRF and US (AUC = 0.83). Multivariable analysis showed that only IDB (p = 0.012) and FIMB (p = 0.029) were independently associated with PAS. CONCLUSIONS: MRI is the best modality to predict PAS in patients with PP independently from CRF and/or US finding. It is reasonable to propose the combined assessment of CRF and US as the first diagnostic level to predict PAS, sparing MRI for selected cases in which US findings are uncertain for PAS.

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions.

PURPOSE: To assess a radiomic machine learning (ML) model in classifying solid adrenal lesions (ALs) without fat signal drop on chemical shift (CS) as benign or malignant. METHOD: 55 indeterminate ALs (21 lipid poor adenomas, 15 benign pheocromocytomas, 1 oncocytoma, 12 metastases, 6 primary tumors) showing no fat signal drop on CS were retrospectively included. Manual 3D segmentation on T2-weighted and CS images was performed for subsequent radiomic feature extraction. After feature stability testing and an 80-20% train-test split, the train set was balanced via oversampling. Following a multi-step feature selection, an Extra Trees model was tuned with 5-fold stratified cross-validation in the train set and then tested on the hold-out test set. RESULTS: A total of 3396 features were extracted from each AL, of which 133 resulted unstable while none had low variance (< 0.01). Highly correlated (r > 0.8) features were also excluded, leaving 440 parameters. Among these, Support Vector Machine 5-fold stratified cross-validated recursive feature elimination selected a subset of 6 features. ML obtained a cross-validation accuracy of 0.94 on the train and 0.91 on the test sets. Precision, recall and F1 score were respectively 0.92, 0.91 and 0.91. CONCLUSIONS: Our MRI handcrafted radiomics and ML pipeline proved useful to characterize benign and malignant solid indeterminate adrenal lesions.

MRI to assess deep myometrial invasion in patients with endometrial cancer:A multi-reader study to evaluate the diagnostic role of different sequences.

OBJECTIVE: The identification of deep myometrial invasion (DMI) represents a fundamental aspect in patients with endometrial cancer (EC) for accurate disease staging. It can be detected on MRI using T2-weighted (T2-w), diffusion weighted (DWI) and dynamic contrast enhanced sequences (DCE). Aim of the study was to perform a multi-reader evaluation of such sequences to identify the most accurate and its reliability for the best protocol. METHODS: In this multicenter retrospective study, MRI were independently evaluated by 4 radiologists (2 senior and 2 novice) with a sequence-based approach to identify DMI. The performance of the entire protocol was also evaluated. A comparison between the different sequences assessed by the same reader was performed using receiver operating curve and post-hoc analysis. Intraclass Correlation Coefficient (ICC) was used to assess inter- and intra-observer variability. RESULTS: A total of 92 patients were included. The performance of the readers did not show significant differences among DWI, DCE and the entire protocol. For only one senior radiologist, who reached the highest diagnostic accuracy with the entire protocol (82,6 %), both DWI (p = 0,0197) and entire protocol (p = 0,0039) were found significantly superior to T2-w. The highest inter-observer agreement was obtained with the entire protocol by expert readers (ICC = 0,77). CONCLUSIONS: For the detection of DMI, the performances of DWI and DCE alone and that of a complete protocol do not significantly differ, even though the latter ensures the highest reliability particularly for expert readers. In cases in which T2-w and DWI are consistent, an unenhanced protocol could be proposed.

Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study.

RATIONALE AND OBJECTIVES: To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability. MATERIALS AND METHODS: Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings. RESULTS: Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48). CONCLUSION: We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.

MR-enterography in Crohn's disease: what MRE mural parameters are associated to one-year therapeutic management outcome?

OBJECTIVE: To investigate the association of mural parameters of MR-enterography (MRE) with one-year therapeutic management of Crohn's disease (CD) patients. METHODS: CD patients, undergone MRE with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps between January 2017 and June 2018, were retrospectively enrolled. Extramural complications represented an exclusion criterion because of their potential influence on the intrinsic characteristic of the bowel wall. Two groups of patients were defined on the base of the therapeutic management adopted at 1-year follow-up: Medical-group and surgical-group. The following MRE parameters were evaluated: wall-thickening, longitudinal-extension, T2-fat-suppression-mural-signal, ulcers, mural-oedema, wall-enhancement-rate/pattern, DWI-scores, ADC-values, strictures. RESULTS: 70 CD patients were enrolled. 57/70 (81.4%) were included in Medical-group and 13/70 (18.6%) in Surgical-group. ADCmean and strictures resulted to be significantly (p < 0.01) different between the two groups. The ADCmean showed to be significantly associated to conservative management [p < 0.01; OR: 0.0003; 95% CI (0.00-0.13)], while the strictures to surgical management [p < 0.01; OR: 29.7; 95% CI (4.9-179.7)]. ROC curves for ADCmean showed that AUC was 0.717 [95% CI (0.607-0.810), p < 0.01] with an optimal cut-off value of 1.081 × 10-3 mm2 s-1. A negative predictive value of 90.2% was observed associating ADCmean values > 1.081 × 10-3 mm2 s-1 to conservative therapy. 13/17 (76%) strictures with an ADCmean > 1.081 × 10-3 mm2 s-1 benefited of conservative therapy. CONCLUSION: ADCmean values calculated on DWI-MRE may be associated to 1-year conservative medical therapy in patients with CD without extramural complications. ADVANCES IN KNOWLEDGE: ADC maps may be proposed to select CD patients with a lower burden of mural active inflammatory cells and/or fibrosis benefiting of 1-year conservative treatment.

MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study.

The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.

Ultrasound, shear-wave elastography, and magnetic resonance imaging in native liver survivor patients with biliary atresia after Kasai portoenterostomy: correlation with medical outcome after treatment.

BACKGROUND: Biliary atresia (BA) is a rare obliterative cholangiopathy and Kasai portoenterostomy (KP) represents its first-line treatment; clinical and laboratory parameters together with abdominal ultrasound (US) are usually performed during the follow-up. Shear-wave elastography (SWE) is able to evaluate liver parenchyma stiffness; magnetic resonance imaging (MRI) has also been proposed to study these patients. PURPOSE: To correlate US, SWE, and MRI imaging findings with medical outcome in patients with BA who are native liver survivors after KP. MATERIAL AND METHODS: We retrospectively enrolled 24 patients. They were divided in two groups based on "ideal" (n = 15) or "non-ideal" (n = 9) medical outcome. US, SWE, and MRI exams were analyzed qualitatively and quantitatively for imaging signs suggestive of chronic liver disease (CLD). RESULTS: Significant differences were found in terms of liver surface (P = 0.007) and morphology (P = 0.013), portal vein diameter (P = 0.012) and spleen size (P = 0.002) by US, liver signal intensity (P = 0.013), portal vein diameter (P = 0.010), presence of portosystemic collaterals (P = 0.042), and spleen size (P = 0.001) by MRI. The evaluation of portal vein diameter (moderate, κ = 0.44), of portosystemic collaterals (good, κ = 0.78), and spleen size (very good, κ = 0.92) showed the best agreement between US and MRI. A significant (P = 0.01) difference in liver parenchyma stiffness by SWE was also found between the two groups (cut-off = 9.6 kPa, sensitivity = 55.6%, specificity = 100%, area under the ROC curve = 0.82). CONCLUSION: US, SWE, and MRI findings correlate with the medical outcome in native liver survivor patients with BA treated with KP.

Comparison Between Multidetector CT and High-Field 3T MR Imaging in Diagnostic and Tumour Extension Evaluation of Patients with Cholangiocarcinoma.

PURPOSE: To compare multidetector computed tomography (MDCT) and magnetic resonance imaging (MRI) with cholangiopancreatography (MRCP) findings in the diagnostic evaluation of patients with cholangiocarcinoma (CCA) to establish tumour resectability. METHODS: Thirty patients (22 M, 8 F) with pathologically proven CCA by post-surgical specimens (n = 20), core biopsy (n = 6) or cytology (n = 4) underwent both MDCT and MRI with MRCP. CCA lesions were classified on the basis of anatomical locations in intra-hepatic (iCCA), peri-hilar (pCCA) and distal (dCCA) tumours. Morphological tumour pattern, lesion size, biliary dilatation, tumour contrast enhancement type, lymph node involvement and vascular infiltration were directly compared between MDCT and MRI with MRCP. As a rule, a tumour resectability judgement for each patient was formulated by both imaging techniques comparing imaging results with direct surgical assessment (n = 20) or interventional procedures (n = 10). RESULTS: In terms of anatomical location, 14 iCCA, 8 pCCA and 8 dCCA were observed; both imaging techniques were concordant about the identification and morphological characterization of tumour lesions and in the evaluation of tumour features (lesion size, contrast enhancement pattern, capsular retraction, biliary dilatation, lymph node involvement and vascular infiltration) as well as in assessing lesion resectability; an excellent agreement (k = 1) for the assessment of all the parameters included in imaging analysis was observed. CONCLUSIONS: The comparative concordant results of our study suggest that MRI with MRCP represents a valid alternative to MDCT for the diagnostic evaluation of patients with CCA to establish tumour resectability providing multiplanar scanning of high-contrast imaging quality; MDCT should be preferred in uncooperative patients, in the presence of biliary stents or when MRI is absolutely contraindicated for incompatible medical devices.

Colorectal cancer: Parametric evaluation of morphological, functional and molecular tomographic imaging.

Colorectal cancer (CRC) represents one of the leading causes of tumor-related deaths worldwide. Among the various tools at physicians' disposal for the diagnostic management of the disease, tomographic imaging (e.g., CT, MRI, and hybrid PET imaging) is considered essential. The qualitative and subjective evaluation of tomographic images is the main approach used to obtain valuable clinical information, although this strategy suffers from both intrinsic and operator-dependent limitations. More recently, advanced imaging techniques have been developed with the aim of overcoming these issues. Such techniques, such as diffusion-weighted MRI and perfusion imaging, were designed for the "in vivo" evaluation of specific biological tissue features in order to describe them in terms of quantitative parameters, which could answer questions difficult to address with conventional imaging alone (e.g., questions related to tissue characterization and prognosis). Furthermore, it has been observed that a large amount of numerical and statistical information is buried inside tomographic images, resulting in their invisibility during conventional assessment. This information can be extracted and represented in terms of quantitative parameters through different processes (e.g., texture analysis). Numerous researchers have focused their work on the significance of these quantitative imaging parameters for the management of CRC patients. In this review, we aimed to focus on evidence reported in the academic literature regarding the application of parametric imaging to the diagnosis, staging and prognosis of CRC while discussing future perspectives and present limitations. While the transition from purely anatomical to quantitative tomographic imaging appears achievable for CRC diagnostics, some essential milestones, such as scanning and analysis standardization and the definition of robust cut-off values, must be achieved before quantitative tomographic imaging can be incorporated into daily clinical practice.

Radium-223 for the treatment of bone metastases in castration-resistant prostate cancer: when and why.

Radium-223 dichloride (223Ra) is the first, recently approved, α-particle-emitting radiopharmaceutical for the treatment of patients with bone metastases in castration-resistant prostate cancer (CRPC) and no evidence of visceral metastases. We explored MEDLINE, relevant congresses, and websites for data on 223Ra and prostate cancer therapies, focusing on therapeutic strategies and timing, bone metastases, and diagnostic assessment. 223Ra represents the only bone-targeting agent that has significantly extended patients' overall survival while reducing pain and symptomatic skeletal events. Unlike other radiopharmaceuticals, such as strontium-89 and samarium-153 EDTMP, 223Ra (11.4-days half-life) has shown a high biological efficiency mainly due to its short penetration range. These features potentially allow reduced bone marrow toxicity and limit undue exposure. 223Ra has been validated under the product name Xofigo® by the US Food and Drug Administration and the European Medicines Agency. Patient selection, management, and treatment sequencing is recommended to be discussed in the context of a multidisciplinary environment, including oncology, urology, nuclear medicine, and radiation therapy physicians. No consensus has been achieved regarding the optimal timing and its administration as single agent or in combination with zoledronic acid or chemotherapy, so far. This review aims to provide a rationale for the use of 223Ra in treating metastases from CRPC, highlighting the crucial role of a multidisciplinary approach, the disputed inclusion and exclusion criteria on the basis of agencies regulations, and the value of diagnostics for therapy assessment.

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-rich (LRA) or lipid-poor (LPA). The differentiation of AA, particularly LPA, from nonadenoma adrenal lesions (NAL) may be challenging. Texture analysis (TA) can extract quantitative parameters from MR images. Machine learning is a technique for recognizing patterns that can be applied to medical images by identifying the best combination of TA features to create a predictive model for the diagnosis of interest. PURPOSE/HYPOTHESIS: To assess the diagnostic efficacy of TA-derived parameters extracted from MR images in characterizing LRA, LPA, and NAL using a machine-learning approach. STUDY TYPE: Retrospective, observational study. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: Sixty MR examinations, including 20 LRA, 20 LPA, and 20 NAL. FIELD STRENGTH/SEQUENCE: Unenhanced T1 -weighted in-phase (IP) and out-of-phase (OP) as well as T2 -weighted (T2 -w) MR images acquired at 3T. ASSESSMENT: Adrenal lesions were manually segmented, placing a spherical volume of interest on IP, OP, and T2 -w images. Different selection methods were trained and tested using the J48 machine-learning classifiers. STATISTICAL TESTS: The feature selection method that obtained the highest diagnostic performance using the J48 classifier was identified; the diagnostic performance was also compared with that of a senior radiologist by means of McNemar's test. RESULTS: A total of 138 TA-derived features were extracted; among these, four features were selected, extracted from the IP (Short_Run_High_Gray_Level_Emphasis), OP (Mean_Intensity and Maximum_3D_Diameter), and T2 -w (Standard_Deviation) images; the J48 classifier obtained a diagnostic accuracy of 80%. The expert radiologist obtained a diagnostic accuracy of 73%. McNemar's test did not show significant differences in terms of diagnostic performance between the J48 classifier and the expert radiologist. DATA CONCLUSION: Machine learning conducted on MR TA-derived features is a potential tool to characterize adrenal lesions. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

The role of dynamic post-contrast T1-w MRI sequence to characterize lipid-rich and lipid-poor adrenal adenomas in comparison to non-adenoma lesions: preliminary results.

PURPOSE: The purpose of the article is to compare the features of wash-out (WO) parameters between lipid-rich and lipid-poor adrenal adenomas as well as with a group of non-adenoma adrenal lesions. METHODS: 46 patients (36 F and 10 M, median age 58 years) with unilateral adrenal lesions (35 adenomas, 7 pheochromocytomas, 1 carcinoma, and 3 metastases) were prospectively evaluated; adrenal lesions were divided into adenomas (Group 1) and non-adenomas (Group 2). MR imaging was performed with a 3-Tesla scanner using pre- and post-contrast dedicated sequences. On the basis of the evaluation of qualitative chemical-shift (CS) signal intensity (SI) loss, adrenal adenomas were, respectively, divided in Group 1A (n = 25) as lipid-rich and Group 1B (n = 10) as lipid-poor; non-adenoma adrenal lesions were grouped in Group 2 (n = 11). The following parameters were evaluated: size (mm), CS SI index (%), early (5 min), and delayed (10 min) Relative (R) and Absolute (A) WO values (%). RESULTS: The comparison of AWO and RWO showed significant (p ≤ 0.05) differences between Group 1A and Groups 1B and 2, both using 5- and 10-min images for calculation; conversely, no differences in these dynamic parameters were found between Group 1B and 2; AWO and RWO values were significantly lower in adrenal lesions of Groups 1B and 2 compared to Group 1A, both using 5- and 10-min images for calculation. CONCLUSIONS: The quantitative evaluation of WO parameters could not be used to characterize lipid-poor adrenal adenomas for which alternative imaging modalities are required.