Breast PET/MRI Hybrid Imaging and Targeted Tracers.

The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (18 F-FDG, 18 F-NaF, choline, 18 F-FES, 18 F-FES, 89 Zr-trastuzumab, choline derivatives, 18 F-FLT, and 68 Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Pancreatic Neuroendocrine Tumors in Patients with Multiple Endocrine Neoplasia Type 1: Diagnostic Value of Different MRI Sequences.

BACKGROUND: MRI is a useful imaging modality to assess the presence of pancreatic neuroendocrine tumors (PNETs), allowing repeat monitoring examinations in multiple endocrine neoplasia type 1 (MEN-1) patients. OBJECTIVES: We aimed to compare the diagnostic accuracy of conventional MRI sequences to identify which sequence better depicts the presence of PNETs in MEN-1 patients. METHOD: We performed a retrospective analysis of consecutive MEN-1 patients who underwent a conventional MRI protocol to monitor previously proven PNETs. MRI sequences T1-w chemical shift (CS), T2-w HASTE, fat-suppressed (FS) T2-w HASTE, diffusion-weighted imaging (DWI), and pre- and post-contrast FS T1-w sequences were independently analyzed by 2 experienced radiologists using a 3-grade score (no lesion, uncertain lesion, and certain lesion); lesion size and signal intensity were recorded. A Friedman ANOVA and a Wilcoxon pairwise test for the post hoc analysis were used. The sensitivity of each sequence was measured, and the results were analyzed with the χ2 test. RESULTS: We included 21 patients with a total of 45 PNETs proven by histology, endoscopic ultrasonography-guided fine-needle aspiration, CT, and nuclear medicine studies. A statistically significant (p < 0.01) difference was observed in the detection performance of each MRI sequence, particularly between DWI (91%) and T2-w FS (85%) sequences in comparison to the others (T1-w CS, T2-w, and pre- and post-contrast FS T1-w, ≤56% for all); no significant (p = 0.5) difference was found between the detection performance of DWI and T2-w FS sequences. No correlation was observed between the qualitative score of each sequence and lesion tumor size. CONCLUSIONS: DWI and T2-w FS sequences proved to be the most accurate in the detection of PNETs, thus suggesting a role for an abbreviated MRI protocol without contrast medium administration for monitoring MEN-1 patients.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions.

OBJECTIVES: We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML. METHODS: Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar's test. RESULTS: After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70-90%) with an AUC of 0.82 (95% CI = 0.70-0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist's performance improved when using ML (80.2%), but not significantly (p = 0.508). CONCLUSIONS: A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. KEY POINTS: • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier's performance was comparable to that of a breast radiologist • The radiologist's accuracy improved with machine learning, but not significantly.

Clinically Significant Prostate Cancer Detection With Biparametric MRI: A Systematic Review and Meta-Analysis.

OBJECTIVE. The purpose of this study was to perform a systematic review and a meta-analysis of diagnostic accuracy studies that used biparametric MRI (bpMRI) for the detection of clinically significant prostate cancer (csPCa). MATERIALS AND METHODS. Multiple medical databases were systematically searched to identify articles using bpMRI for csPCa detection. Sensitivity, specificity, PPV, and NPV were calculated for each study after enough data were extracted to create a 2 × 2 contingency table. Risk of bias was assessed using the QUADAS-2 tool. Meta-analyses based on bivariate random-effects methods were used to calculate pooled sensitivity, specificity, and summary ROC (SROC) curves. A meta-regression analysis was performed to assess heterogeneity sources. RESULTS. A total of 17 studies (3964 patients) that adopted PI-RADS or other scoring systems were included. Sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio of bpMRI in the detection of csPCa were 0.83 (95% CI, 0.76-0.88), 0.71 (95% CI, 0.63-0.79), 2.9 (95% CI, 2.3-3.7), 0.24 (95% CI, 0.17-0.33), and 12 (95% CI, 8-19), respectively, with an area under the SROC curve of 0.84 (95% CI, 0.81-0.87). The overall quality of the included studies was heterogeneous. CONCLUSION. Our results confirm the feasibility of bpMRI for the detection of csPCa and for reducing acquisition time, patient discomfort, and costs. Nevertheless, the available studies proved to be heterogeneous, indicating a need for a more robust validation of this imaging protocol and a standardization of prostate bpMRI acquisition and reporting.

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis.

PURPOSE: To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. METHODS: Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. RESULTS: In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, -8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54-0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84-0.93) with a standard error of 0.02. CONCLUSIONS: Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice.

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.

Comparison of simultaneous 18F-2-[18F] FDG PET/MR and PET/CT in the follow-up of patients with differentiated thyroid cancer.

AIMS: 18F-FDG PET/CT is the most accurate imaging modality in differentiated thyroid cancer (DTC) patients with either an aggressive histology, an absence of radioiodine uptake in neoplastic foci, or in the absence of imaging abnormalities in patients with an elevated serum thyroglobulin (Tg) level that progresses with time. We evaluated the diagnostic performance of FDG PET/MR in comparison with that of PET/CT. METHODS AND RESULTS: Following the injection of a single 18F-FDG activity, PET/MR and PET/CT were sequentially performed in 40 consecutive patients with DTC previously treated with total thyroidectomy and radioiodine ablation. All patients were then followed up for at least 6 months. PET/MR was positive in 11 patients and PET/CT in 10. PET/MR detected 33 tumor foci and PET/CT 30. During the follow-up of the 12 patients with negative initial PET studies and with a detectable serum Tg, only one patient had a neck recurrence and the administration of an empiric high activity of 131I in the other 11 patients did not reveal any tumor focus. In the 17 patients with an initial serum Tg level < 2 ng/mL, no recurrence occurred. CONCLUSION: This study confirms the high diagnostic accuracy of FDG PET studies in DTC patients with elevated serum Tg levels and shows that PET/MR brings similar information as compared to PET/CT imaging.

Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.

OBJECTIVES: The aim of this study was to systematically review the literature and perform a meta-analysis of machine learning (ML) diagnostic accuracy studies focused on clinically significant prostate cancer (csPCa) identification on MRI. METHODS: Multiple medical databases were systematically searched for studies on ML applications in csPCa identification up to July 31, 2019. Two reviewers screened all papers independently for eligibility. The area under the receiver operating characteristic curves (AUC) was pooled to quantify predictive accuracy. A random-effects model estimated overall effect size while statistical heterogeneity was assessed with the I2 value. A funnel plot was used to investigate publication bias. Subgroup analyses were performed based on reference standard (biopsy or radical prostatectomy) and ML type (deep and non-deep). RESULTS: After the final revision, 12 studies were included in the analysis. Statistical heterogeneity was high both in overall and in subgroup analyses. The overall pooled AUC for ML in csPCa identification was 0.86, with 0.81-0.91 95% confidence intervals (95%CI). The biopsy subgroup (n = 9) had a pooled AUC of 0.85 (95%CI = 0.79-0.91) while the radical prostatectomy one (n = 3) of 0.88 (95%CI = 0.76-0.99). Deep learning ML (n = 4) had a 0.78 AUC (95%CI = 0.69-0.86) while the remaining 8 had AUC = 0.90 (95%CI = 0.85-0.94). CONCLUSIONS: ML pipelines using prostate MRI to identify csPCa showed good accuracy and should be further investigated, possibly with better standardisation in design and reporting of results. KEY POINTS: • Overall pooled AUC was 0.86 with 0.81-0.91 95% confidence intervals. • In the reference standard subgroup analysis, algorithm accuracy was similar with pooled AUCs of 0.85 (0.79-0.91 95% confidence intervals) and 0.88 (0.76-0.99 95% confidence intervals) for studies employing biopsies and radical prostatectomy, respectively. • Deep learning pipelines performed worse (AUC = 0.78, 0.69-0.86 95% confidence intervals) than other approaches (AUC = 0.90, 0.85-0.94 95% confidence intervals).

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.

Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status.

BACKGROUND: Combining MRI techniques with machine learning methodology is rapidly gaining attention as a promising method for staging of brain gliomas. This study assesses the diagnostic value of such a framework applied to dynamic susceptibility contrast (DSC)-MRI in classifying treatment-naïve gliomas from a multi-center patients into WHO grades II-IV and across their isocitrate dehydrogenase (IDH) mutation status. METHODS: Three hundred thirty-three patients from 6 tertiary centres, diagnosed histologically and molecularly with primary gliomas (IDH-mutant = 151 or IDH-wildtype = 182) were retrospectively identified. Raw DSC-MRI data was post-processed for normalised leakage-corrected relative cerebral blood volume (rCBV) maps. Shape, intensity distribution (histogram) and rotational invariant Haralick texture features over the tumour mask were extracted. Differences in extracted features across glioma grades and mutation status were tested using the Wilcoxon two-sample test. A random-forest algorithm was employed (2-fold cross-validation, 250 repeats) to predict grades or mutation status using the extracted features. RESULTS: Shape, distribution and texture features showed significant differences across mutation status. WHO grade II-III differentiation was mostly driven by shape features while texture and intensity feature were more relevant for the III-IV separation. Increased number of features became significant when differentiating grades further apart from one another. Gliomas were correctly stratified by mutation status in 71% and by grade in 53% of the cases (87% of the gliomas grades predicted with distance less than 1). CONCLUSIONS: Despite large heterogeneity in the multi-center dataset, machine learning assisted DSC-MRI radiomics hold potential to address the inherent variability and presents a promising approach for non-invasive glioma molecular subtyping and grading.

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.

Clinical value of high-resolution (5-17 MHz) echo-color Doppler (ECD) for identifying filling materials and assessment of damage or complications in aesthetic medicine/surgery.

PURPOSE: The purpose of this retrospective study is to evaluate the role of echo-color-Doppler (ECD) imaging in identifying a series of characteristics pursuant to aesthetic filling material such as their degree of absorbability and their potential complications which include their propensity to stimulate the formation of encapsulated foreign-body granulomas. In the latter case, ECD can be of aid by giving indication for surgical therapy. MATERIALS AND METHODS: Over a 4-year period, we studied 180 patients (60 ♂) who underwent an aesthetic medical/surgical treatment. We used ECD to evaluate the implant material, its thickness, the injection site, the integrity of dermal layers and the presence of any associated complications. RESULTS: In 97% (174/180) of our patients, we were able to identify the type of material used; furthermore, 57% of patients had a hyaluronic acid implant, 14% a lipofilling and 29% a non-absorbable filler (with 10% of silicone). In 6/180 (3%), we could not recognize the material used; 89% (161/180) of our patients presented post-injection complications; moreover, 67% showed peri-implant dermal-hypodermal thickening areas with adjacent lymphostasis, 6% displayed an abnormal implant site, and 17% showed inflammation with encapsulated foreign-body granulomas that required subsequent surgical excision. Biopsy samples were obtained from 37/180 patients (21%); among these, 31 patients had an ECD evidence of granuloma and on 6 patients we were not able to define the injected material. Histopathological examination identified 29 granulomas, 5 sterile abscesses and 3 chronic inflammations in the absence of granuloma. ECD showed an overall 78% diagnostic accuracy, with 90% sensitivity and 37% specificity in detecting filler granulomas. CONCLUSION: ECD is a low-cost technique that allows to identify filling materials and to assess the complications of an esthetic medical/surgical treatment.

Could Blockchain Technology Empower Patients, Improve Education, and Boost Research in Radiology Departments? An Open Question for Future Applications.

Blockchain can be considered as a digital database of cryptographically validated transactions stored as blocks of data. Copies of the database are distributed on a peer-to-peer network adhering to a consensus protocol for authentication of new blocks into the chain. While confined to financial applications in the past, this technology is quickly becoming a hot topic in healthcare and scientific research. Potential applications in radiology range from upgraded monitoring of training milestones achievement for residents to improved control of clinical imaging data and easier creation of secure shared databases.

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.

Successful management of a third-trimester pregnancy complicated by pheochromocytoma: case report.

Pheochromocytoma (PH) is a tumor that arises from chromaffin cells of the adrenal medulla. Though being this benign neoplasm very rare in pregnancies, lack of treatment nevertheless causes high mortality rates for both the mother and the fetus. Classic symptoms related to PH are hypertension, abdominal pain, diaphoresis, and headache; but it can be easily misdiagnosed as gestational hypertension or preeclampsia. Its appearance is sporadic, but there are some genetic disorders that favor its onset (e.g. MEN 2A and 2B). Individual management is needed, because no single protocol is suitable in such a complex and rare condition. In this paper we describe our experience in the clinical and surgical management of a young pregnant patient affected by PH, and in particular the specific and unique pharmacological treatment with doxazosin, the use of corticosteroids and a close monitoring of fetal well-being, which proved being an effective approach.

Integrated Imaging Characterization of Adrenal Adenoma: False-Positive Metaiodobenzylguanidine (MIBG) Findings of Adrenal Scintigraphy.

BACKGROUND: Evaluation of a patient with melanoma in whom an adrenal mass was detected on CT and MR during follow-up and further characterized with PET-CT and MIBG scintigraphy. CASE REPORT: In this case report, we describe a patient with melanoma in whom an adrenal mass was detected on CT and MRI during post-surgical follow-up and was further characterized with radionuclide studies consisting of PET-CT and MIBG scintigraphy. Although the results of imaging studies suggested that the mass was a pheochromocytoma, a cortical adrenal adenoma was histologically proven. CONCLUSIONS: Integrated structural and functional imaging is recommended to characterize adrenal tumors; however, mistakes may occur and therefore careful imaging evaluation is required.

Correlative Imaging in a Patient with Cystic Thymoma: CT, MR and PET/CT Comparison.

BACKGROUND: Cystic thymoma is a rare variant of thymic neoplasm characterized by almost complete cystic degeneration with mixed internal structure. We describe a case of a 60 year-old woman with a cystic thymoma studied with advanced tomographic imaging stydies. CT, MRI and PET/CT with (18)F-FDG were performed; volumetric CT and MRI images provided better anatomic evaluation for pre-operative assessment, while PET/CT was helpful for lesion characterization based on (18)F-FDG uptake. Although imaging studies are mandatory for pre-operative evaluation of cystic thymoma, final diagnosis still remains surgical. CASE REPORT: A 60-year-old woman with recent chest pain and no history of previous disease was admitted to our departement to investigate the result of a previous chest X-ray that showed bilateral mediastinal enlargement; for this purpose, enhanced chest CT scan was performed using a 64-rows scanner (Toshiba, Aquilion 64, Japan) before and after intravenous bolus administration of iodinated non ionic contrast agent; CT images demonstrated the presence of a large mediastinal mass (11×8 cm) located in the anterior mediastinum who extended from the anonymous vein to the cardio-phrenic space, compressing the left atrium and causing medium lobe atelectasis; bilateral pleural effusion was also present. CONCLUSIONS: In conclusion, correlative imaging plays a foundamental role for the diagnostic evaluation of patient with cystic thymoma. In particular, volumetric CT and MRI studies can provide better anatomic informations regarding internal structure and local tumor spread for pre-operative assessment. Conversely, metabolic imaging using (18)F-FDG PET/CT is helpful for lesion characterization differentiating benign from malignant lesion on the basis of intense tracer uptake. The role of PET/MRI is still under investigation. However, final diagnosis still remains surgical even though imaging studies are mandatory for pre-operative patient management.

AI-Enhanced PET and MR Imaging for Patients with Breast Cancer.

New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.