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.

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.

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.

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.

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.

Bone involvement by adenocarcinoma of the uterine cervix: a rare entity.

BACKGROUND: Adenocarcinoma is the second most frequent cancer of the uterine cervix after squamous carcinoma, and the most frequent histotype is the mucinous one. Endo-cervical adenocarcinoma accounts for about 10-30% of all cervical cancers and clinically the lesion can be asymptomatic or, more frequently, presenting with anomalous bleeding and/or vaginal discharge. CASE REPORT: A 41-year-old woman with a diagnosis of adenocarcinoma of the uterine cervix was subjected to chemotherapy after radical surgery. During the follow-up, the patient underwent a Positron Emission Tomography integrated with Computed Tomography and pelvic Magnetic Resonance, which showed rapid and diffuse disease progression from the site of the lesion to the pelvic bones. CONCLUSIONS: Bone involvement in patients with cervical cancer, being a rare event, is significant since it greatly reduces life expectancy. The majority of metastatic bone lesions in cervical cancer seem to be of osteolytic nature. In our patient, Positron Emission Tomography integrated with Computed Tomography and Magnetic Resonance were the imaging methods used during the follow-up and both techniques clearly showed diffuse and rapid tumour spread to the bones.

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.

MRI findings of a remote and isolated vaginal metastasis revealing an adenocarcinoma of the mid-sigmoid colon.

A remote vaginal metastasis from a colo-rectal carcinoma is extremely rare. Only few cases have been described in the literature. The radiological appearances of a vaginal metastasis from colon-rectal cancer have not been extensively investigated. We report the MRI findings with clinical and pathological correlations of a remote and isolated vaginal metastasis revealing a mid-sigmoid adenocarcinoma in a 67 years old woman.

Chest Dynamic MRI as Early Biomarker of Respiratory Impairment in Amyotrophic Lateral Sclerosis Patients: A Pilot Study.

Background: Amyotrophic lateral sclerosis (ALS) is a neuromuscular progressive disorder characterized by limb and bulbar muscle wasting and weakness. A total of 30% of patients present a bulbar onset, while 70% have a spinal outbreak. Respiratory involvement represents one of the worst prognostic factors, and its early identification is fundamental for the early starting of non-invasive ventilation and for the stratification of patients. Due to the lack of biomarkers of early respiratory impairment, we aimed to evaluate the role of chest dynamic MRI in ALS patients. Methods: We enrolled 15 ALS patients and 11 healthy controls. We assessed the revised ALS functional rating scale, spirometry, and chest dynamic MRI. Data were analyzed by using the Mann-Whitney U test and Cox regression analysis. Results: We observed a statistically significant difference in both respiratory parameters and pulmonary measurements at MRI between ALS patients and healthy controls. Moreover, we found a close relationship between pulmonary measurements at MRI and respiratory parameters, which was statistically significant after multivariate analysis. A sub-group analysis including ALS patients without respiratory symptoms and with normal spirometry values revealed the superiority of chest dynamic MRI measurements in detecting signs of early respiratory impairment. Conclusions: Our data suggest the usefulness of chest dynamic MRI, a fast and economically affordable examination, in the evaluation of early respiratory impairment in ALS patients.

Renal duplex sonographic evaluation of type 2 diabetic patients.

OBJECTIVES: The purpose of this study was to evaluate the renal volume and intrarenal hemodynamics with duplex sonography in a group of diabetic patients with normal renal function in comparison to nondiabetic controls. METHODS: The renal volume and resistive index (RI) of segmental arteries were assessed by duplex sonography in 88 diabetic patients (44 male and 44 female; median age, 58 years [range, 37-69 years]) and 73 nondiabetic control participants (48 male and 25 female; median age, 53 years [range, 27-75 years]) without renal artery stenosis. RESULTS: Both renal volume and RI values in the diabetic patients were significantly higher compared to the controls (mean volume ± SD: diabetic patients, 197.3 ± 47.6 mL; controls, 162.5 ± 35.2 mL; P < .0001; RI: diabetic patients, 0.70 ± 0.05; controls, 0.59 ± 0.06; P < .0001). Renal hypertrophy was present even in diabetic patients without proteinuria (renal volume: patients without proteinuria, 198.3 ± 45.9 mL; controls, 162.5 ± 35.2 mL; P < .005). Patients with higher RI values had significantly greater proteinuria (RI <0.75, 15.9 mg/g [range, 4.2-1718.9 mg/g]; RI >0.75, 37.9 mg/g [range, 11.34-2087.0 mg/g]; P < .02). CONCLUSIONS: Changes in renal volume and hemodynamics are detectable on sonography in diabetic patients. Those changes are also present in patients without proteinuria or signs of renal atherosclerosis and with both normal and increased glomerular filtration rates. These results indicate a potential role of duplex sonography in the early identification of morphologic and hemodynamic renal changes in type 2 diabetic patients.

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.

Segmentation methods applied to MRI-derived radiomic analysis for the prediction of placenta accreta spectrum in patients with placenta previa.

PURPOSE: To retrospectively evaluate the performance of different manual segmentation methods of placenta MR images for predicting Placenta Accreta Spectrum (PAS) disorders in patients with placenta previa (PP) using a Machine Learning (ML) Radiomics analysis. METHODS: 64 patients (n=41 with PAS and n= 23 without PAS) with PP who underwent MRI examination for suspicion of PAS were retrospectively selected. All MRI examinations were acquired on a 1.5 T using T2-weighted (T2w) sequences on axial, sagittal and coronal planes. Ten different manual segmentation methods were performed on sagittal placental T2-weighted images obtaining five sets of 2D regions of interest (ROIs) and five sets of 3D volumes of interest (VOIs) from each patient. In detail, ROIs and VOIs were positioned on the following areas: placental tissue, retroplacental myometrium, cervix, placenta with underneath myometrium, placenta with underneath myometrium and cervix. For feature stability testing, the same process was repeated on 30 randomly selected placental MRI examinations by two additional radiologists, working independently and blinded to the original segmentation. Radiomic features were extracted from all available ROIs and VOIs. 100 iterations of 5-fold cross-validation with nested feature selection, based on recursive feature elimination, were subsequently run on each ROI/VOI to identify the best-performing method to classify instances correctly. RESULTS: Among the segmentation methods, the best performance in predicting PAS was obtained by the VOIs covering the retroplacental myometrium (Mean validation score: 0.761, standard deviation: 0.116). CONCLUSION: Our preliminary results show that the VOI including the retroplacental myometrium using T2w images seems to be the best method when segmenting images for the development of ML radiomics predictive models to identify PAS in patients with PP.

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.

Pitfalls in diagnostic imaging of cystic pancreatic masses: a case of true cystic lesion mimicking a mucinous cystadenoma.

CONTEXT: Imaging characterization is a frequent topic in diagnostic evaluation of patients with pancreatic cystic lesions. CASE REPORT: We present a patient with a true pancreatic cyst with internal septation in an adult female. The presence of the internal septum should be considered in the differential diagnosis, in fact in our case CT and MR imaging findings were incorrectly suggestive of mucinous cystadenoma. CONCLUSION: True pancreatic cyst may show septate architecture and thus for imaging characterization this feature should be considered in the differential diagnosis of cystic pancreatic masses.

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study.

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = -5-8) and 6% (IQR = 0-22%), respectively. The highest and lowest scores registered were 12/36 (33%) and -5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Qualitative evaluation of MR images for assessing placenta accreta spectrum disorders in patients with placenta previa: A pilot validation study.

PURPOSE: To validate a qualitative imaging method using magnetic resonance (MR) for predicting placental accreta spectrum (PAS) in patients with placenta previa (PP). METHOD: Two MR imaging methods built in our previous experience was tested in an external comparable group of sixty-five patients with PP; these methods consisted of presence of at least one (Method 1) or two (Method 2) of the following abnormal MR imaging signs: intraplacental dark bands, focal interruption of myometrial border and abnormal placental vascularity. Three groups of radiologists with different level of expertise evaluated MR images: at least 5 years of experience in body imaging (Group 1); at least 10 (Group 2) or 20 (Group 3) years of experience in genito-urinary MR. While radiologists of Group 1 routinely evaluated MR images, those of Groups 2 and 3 used both Methods 1 and 2. RESULTS: A significant (p < 0.005) difference was found between the diagnostic accuracy values of imaging evaluation performed by Group 3 using Method 1 (63%) and Method 2 (89%); of note, the accuracy of Method 2 by Group 3 was also significantly (p < 0.005) higher compared to that of both Methods 1 (46%) and 2 (63%) by Group 2 as well as to that of the routine evaluation by Group 1 (60%). CONCLUSIONS: The qualitative identification of at least two abnormal MR signs (Method 2) represents an accurate method for predicting PAS in patients with PP particularly when this method was used by more experienced radiologists; thus, imaging expertise and methodology is required for this purpose.

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.

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.