The current study aimed to evaluate the efficiency of dynamic contrast-enhanced (DCE) MRI visual features in classifying benign liver nodules and hepatocellular carcinoma (HCC) using a machine learning model. 115 LI-RADS3, 137 LI-RADS4, and 140 LI-RADS5 nodules were included (392 nodules from 245 patients), which were evaluated by follow-up imaging for LR-3 and pathology results for LR-4 and LR-5 nodules. Data was collected retrospectively from 3 T and 1.5 T MRI scanners. All the lesions were categorized into 124 benign and 268 HCC lesions. Visual features included tumor size, arterial-phase hyper-enhancement (APHE), washout, lesion segment, mass/mass-like, and capsule presence. Gini-importance method extracted the most important features to prevent over-fitting. Final dataset was split into training(70%), validation(10%), and test dataset(20%). The SVM model was used to train the classifying algorithm. For model validation, 5-fold cross-validation was utilized, and the test data set was used to assess the final accuracy. The area under the curve and receiver operating characteristic curves were used to assess the performance of the classifier model. For test dataset, the accuracy, sensitivity, and specificity values for classifying benign and HCC lesions were 82%,84%, and 81%, respectively. APHE, washout, tumor size, and mass/mass-like features significantly differentiated benign and HCC lesions with p-value < .001. The developed classification model employing DCE-MRI features showed significant performance of visual features in classifying benign and HCC lesions. Our study also highlighted the significance of mass and mass-like features in addition to LI-RADS categorization. For future work, this study suggests developing a deep-learning algorithm for automatic lesion segmentation and feature assessment to reduce lesion categorization errors.

Background: Abbreviated magnetic resonance imaging (MRI) includes fewer sequences than standard MRI, which could be utilized for breast cancer detection. Purpose: To evaluate the diagnostic accuracy of abbreviated MRI protocol in screening and diagnostic settings. Material and Methods: All women with screening and diagnostic (problem-solving and preoperative staging) MRI examination were recruited from 2017 to 2020. Two expert radiologists assessed designed abbreviated protocol (fat-saturated T1-weighted [T1W] pre-contrast and two first fat-saturated T1W post-contrast series with reconstruction of their subtraction) including maximum intensity projection (MIP) and then evaluated standard protocol of breast MRI. Associated findings, including axillary lymphadenopathy and invasion to nipple, skin, or pectoralis muscle were also evaluated. The concordance rate of abbreviated with standard protocol in screening and diagnostic settings were also compared, based on BI-RADS classification. Diagnostic accuracy, sensitivity, specificity, and positive and negative predictive value were calculated. Results: A total of 108 (26.5%) of 408 patients (mean age = 43 ± 9 years) were classified as BI-RADS 4–5 and considered positive findings based on suspicious enhancement (mass or non-mass enhancement). Compared to standard protocol, abbreviated protocol revealed >98% accuracy in the diagnostic setting as well as 100% accuracy in the screening setting. Concordance rates in screening and diagnostic settings were 99.6% and 98.1%, respectively. There was no discordance between abbreviated and standard protocol in the evaluation of associated findings. Conclusion: Abbreviated MRI protocol possesses substantial diagnostic accuracy in both screening and diagnostic settings. Additional information provided by standard protocol might not require for cancer detection.

Neoadjuvant chemotherapy is a standard treatment approach for locally advanced breast cancer. Conventional imaging modalities, such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound, have been used for axillary lymph node evaluation which is crucial for treatment planning and prognostication. This systematic review aims to comprehensively examine the current research on applying machine learning algorithms for predicting positive axillary lymph nodes following neoadjuvant chemotherapy utilizing imaging modalities, including MRI, CT, and ultrasound. A systematic search was conducted across databases, including PubMed, Scopus, and Web of Science, to identify relevant studies published up to December 2023. Articles employing machine learning algorithms to predict positive axillary lymph nodes using MRI, CT, or ultrasound data after neoadjuvant chemotherapy were included. The review follows the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines, encompassing data extraction and quality assessment. Seven studies were included, comprising 1502 patients. Four studies used MRI, two used CT, and one applied ultrasound. Two studies developed deep-learning models, while five used classic machine-learning models mainly based on multiple regression. Across the studies, the models showed high predictive accuracy, with the best-performing models combining radiomics and clinical data. This systematic review demonstrated the potential of utilizing advanced data analysis techniques, such as deep learning radiomics, in improving the prediction of positive axillary lymph nodes in breast cancer patients following neoadjuvant chemotherapy.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a highly sensitive breast imaging modality in detecting breast carcinoma. Nonmass enhancement (NME) is uniquely seen on MRI of the breast. The correlation between NME features and pathologic results has not been extensively explored. Our goal was to evaluate the characteristics of probably benign and suspicious NME lesions in MRI and determine which features are more associated with malignancy. We performed a retrospective research after approval by the hospital ethics committee on women who underwent breast MRI from March 2017 to March 2020 and identified 63 lesions of all 400 NME that were categorized as probably benign or suspicious according to the BI-RADS classification (version 2013). MRI features of NME findings including the location, size, distribution and enhancement pattern, kinetic curve, diffusion restriction, and also pathology result or 6-12-month follow-up MRI were evaluated and analyzed in each group (probably benign or suspicious NME). Vacuum-guided biopsies (VAB) were performed under mammographic or sonographic guidance and confirmed with MRI by visualization of the inserted clips. Segmental distribution and clustered ring internal enhancement were significantly associated with malignancy ($p$ value<0.05), while linear distribution or homogeneous enhancement patterns were associated with benignity ($p$ value <0.05). Additionally, the plateau and washout types in the dynamic curve were only seen in malignant lesions ($p$ value <0.05). The presence of DWI restriction in NME lesions was also found to be a statistically important factor. Understanding the imaging findings of malignant NME is helpful to determine when biopsy is indicated. The correlation between NME features and pathologic results is critical in making appropriate management.

Crohn's disease (CD) is a chronic disorder that often starts at a young age and involves periods of remission and relapse. Prompt diagnosis of relapses through screening is crucial due to the potential morbid complications of untreated active inflammation. Magnetic resonance enterography (MRE) is a noninvasive technique to screen for active inflammation. The standard protocol involves intravenous injection of contrast agents with potential side effects. Some abbreviated non-contrast-enhanced MRE protocols are proposed as alternatives for conventional MRE to identify active inflammation. Currently, there is controversy regarding the applicability and accuracy of these protocols. This study aims to describe and compare these protocols and evaluate their accuracy in detecting active inflammation and CD complications. Results from a systematic search of three databases in August 2022 were queried and screened by abstract and full text. Eligible studies were qualitatively and quantitatively analyzed by diagnostic test accuracy meta-analysis. 59 studies entered the systematic review, and 37 were eligible for meta-analysis. Diffusion-weighted imaging (DWI) and fast T2-weighted (T2w) sequences were most frequently used in abbreviated protocols and showed non-inferior accuracy compared to the full protocol in detecting active inflammation. ADC and qualitative DWI had pooled sensitivity of 90% (CI: 82–95%) and 89% (CI:82–93%) and pooled specificity of 94% (CI: 88–97%) and 89% (CI: 79–94%), respectively for detecting active inflammation. Moreover, T2w and combined T2w+DWI sequences had pooled sensitivity of 80% (CI: 64–90%) and 76% (CI: 61–86%) and pooled specificity of 90% (CI: 80–95%) and 87% (CI: 74 – 94%), respectively. Unenhanced protocols show relatively poor diagnostic accuracy in detecting penetrating complications of CD. Magnetization transfer imaging (MTI) has demonstrated excellent accuracy in detecting fibrosis. High heterogeneity was observed in all subgroups, and accuracy was reported to be highly operator dependent in most studies. An abbreviated protocol consisting of DWI and fast T2w imaging can potentially replace the full protocol MRE. Full protocol MRE will still have its role in identifying penetrating complications. MTI should be indicated in case of suspected fibrostenotic disease.