Imaging Reporting Research Articles

A Machine Learning Approach for Breast Cancer Risk Prediction in Digital Mammography

Breast cancer is among the most prevalent cancers in the female population globally. Therefore, screening campaigns as well as approaches to identify patients at risk are particularly important for the early detection of suspect lesions. This study aims to propose a workflow for the automatic classification of patients based on one of the most relevant risk factors in breast cancer, which is represented by breast density. The proposed classification methodology takes advantage of the features automatically extracted from mammographic images, as digital mammography represents the major screening tool in women. Textural features were extracted from the breast parenchyma through a radiomics approach, and they were used to train different machine learning algorithms and neural network models to classify the breast density according to the standard Breast Imaging Reporting and Data System (BI-RADS) guidelines. Both binary and multiclass tasks have been carried out and compared in terms of performance metrics. Preliminary results show interesting classification accuracy (93.55% for the binary task and 82.14% for the multiclass task), which are promising compared to the current literature. As the proposed workflow relies on straightforward and computationally efficient algorithms, it could serve as a basis for a fast-track protocol for the screening of mammograms to reduce the radiologists’ workload.

Magnetic Resonance Elastography Combined With PI‐RADS v2.1 for the Identification of Clinically Significant Prostate Cancer

BackgroundMultiparametric MRI may cause overdiagnosis of clinically significant prostate cancer (csPCa) with the Prostate Imaging Reporting and Data System version 2.1 (PI‐RADS v2.1).ObjectivesTo investigate the diagnostic performance of stiffness as a standalone and complementary marker to PI‐RADS v2.1 for diagnosing csPCa.Study TypeProspective.SubjectsOne hundred forty‐seven participants with pathologically confirmed prostate lesions (≥1 cm), including 71 with csPCa.Field Strength/SequenceT1‐weighted fast spin‐echo, T2‐weighted fast spin‐echo, single‐shot echo‐planar diffusion‐weighted imaging, fast 3D gradient‐echo T1‐weighted dynamic contrast‐enhanced imaging, and 3D single‐shot spin‐echo based echo‐planar MR elastography at 3.0 T.AssessmentThe PI‐RADS v2.1 score was assessed by three radiologists independently. Lesion shear stiffness (SS) values at 60 Hz and 90 Hz were measured. A modified PI‐RADS integrating stiffness with PI‐RADS v2.1 was developed. Diagnostic performance for csPCa was compared between stiffness, PI‐RADS v2.1 and the modified PI‐RADS.Statistical TestSpearman's correlation, Fleiss κ and intraclass correlation, Pearson correlation, one‐way analysis of variance, area under the receiver operating characteristic curve (AUC), and the Delong test. Significance level was P < 0.05.ResultsIn the peripheral zone, csPCa (N = 35) had significantly higher SS than non‐csPCa at 60 Hz (3.22 ± 0.66 kPa vs. 2.56 ± 0.56 kPa) and at 90 Hz (5.64 ± 1.30 kPa vs. 4.48 ± 0.84 kPa). PI‐RADS v2.1 showed 100% sensitivity, 58% specificity, and 0.79 AUC for detecting csPCa. SS achieved 97% sensitivity, 52% specificity, and 0.80 AUC at 60 Hz, while SS had 63% sensitivity, 87% specificity, and 0.78 AUC at 90 Hz. The modified PI‐RADS, combing SS at 60 Hz with PI‐RADS v2.1, resulted in a significantly increased AUC (0.86) compared to that of PI‐RADS v2.1, with a sensitivity of 97% and specificity of 75%.Data ConclusionStiffness can help identifying csPCa in the peripheral zone. Combining stiffness with the PI‐RADS v2.1 improved the diagnostic accuracy and specificity for csPCa.Evidence Level1Technical EfficacyStage 2

Enhancing the diagnostic capacity of [18F]PSMA-1007 PET/MRI in primary prostate cancer staging with artificial intelligence and semi-quantitative DCE: an exploratory study

BackgroundTo investigate the ability of artificial intelligence (AI)-based and semi-quantitative dynamic contrast enhanced (DCE) multiparametric MRI (mpMRI), performed within [18F]-PSMA-1007 PET/MRI, in differentiating benign from malignant prostate tissues in patients with primary prostate cancer (PC).ResultsA total of seven patients underwent whole-body [18F]-PSMA-1007 PET/MRI examinations including a pelvic mpMRI protocol with T2w, diffusion weighted imaging (DWI) and DCE image series. Conventional analysis included visual reading of PET/MRI images and Prostate Imaging Reporting & Data System (PI-RADS) scoring of the prostate. On the prostate level, we performed manual segmentations for time-intensity curve parameter formation and semi-quantitative analysis based on DCE segmentation data of PC-suspicious lesions. Moreover, we applied a recently introduced deep learning (DL) pipeline previously trained on 1010 independent MRI examinations with systematic biopsy-enhanced histopathological targeted biopsy lesion ground truth in order to perform AI-based lesion detection, prostate segmentation and derivation of a deep learning PI-RADS score. DICE coefficients between manual and automatic DL-acquired segmentations were compared. On patient-based analysis, PET/MRI revealed PC-suspicious lesions in the prostate gland in 6/7 patients (Gleason Score-GS ≥ 7b) that were histologically confirmed. Four of these patients also showed lymph node metastases, while two of them had bone metastases. One patient with GS 6 showed no PC-suspicious lesions. Based on DCE segmentations, a distinction between PC-suspicious and normal appearing tissue was feasible with the parameters fitted maximum contrast ratio (FMCR) and wash-in-slope. DICE coefficients (manual vs. deep learning) were comparable with literature values at a mean of 0.44. Further, the DL pipeline could identify the intraprostatic PC-suspicious lesions in all six patients with clinically significant PC.ConclusionFirstly, semi-quantitative DCE analysis based on manual segmentations of time-intensity curves was able to distinguish benign from malignant tissues. Moreover, DL analysis of the MRI data could detect clinically significant PC in all cases, demonstrating the feasibility of AI-supported approaches in increasing diagnostic certainty of PSMA-radioligand PET/MRI.

Assessment of Reason for Exam Imaging Reporting and Data System (RI-RADS) in inpatient diagnostic imaging referrals

ObjectivesTo test the Reason for Exam Imaging Reporting and Data System (RI-RADS) in assessing the quality of radiology requests in an Italian cohort of inpatients; to evaluate the interobserver reliability of RI-RADS.MethodsA single-center quality care study was designed to retrospectively identify consecutive radiology request forms for computed tomography, magnetic resonance imaging, and conventional radiography examinations. One radiologist scored the requests using the RI-RADS. The association between RI-RADS and clinical request variables (urgent request, on-call requests, indication for imaging, requesting specialty, imaging modality, and body region) was evaluated. We calculated interobserver agreement between four readers in a subset of 450 requests.ResultsWe included 762 imaging requests. RI-RADS grades A (adequate request), B (barely adequate request), C (considerably limited request), D (deficient request), and X were assigned to 8 (1%), 49 (7%), 237 (31%), 404 (53%), and 64 (8%) of cases, respectively. In the multivariate analysis, the indication for imaging, body region, and requesting specialty significantly influenced the RI-RADS. Indications for imaging with a high risk of poor RI-RADS grade were routine preoperative imaging and device check requests. The upper extremity was the body region with the highest risk of poor RI-RADS grade. Requesting specialties with a high risk of poor RI-RADS grade were cardiovascular surgery, intensive care medicine, and orthopedics. The analysis of the interobserver agreement revealed substantial agreement for the RI-RADS grade.ConclusionThe majority of radiology exam requests were inadequate according to RI-RADS, especially those for routine imaging. RI-RADS demonstrated substantial reliability, suggesting that it can be satisfactorily employed in clinical settings.Critical relevant statementThe implementation of RI-RADS can provide a framework for standardizing radiology requests, thereby enabling quality assurance and promoting a culture of quality improvement.Key PointsRI-RADS aims to grade the completeness of radiology requests.Over half of the imaging requests were RI-RADS D grade; RI-RADS demonstrated substantial reliability.Most radiology requests were inadequate and RI-RADS could classify them in clinical practice.Graphical

Image Features and Diagnostic Value of Contrast-Enhanced Ultrasound for Ductal Carcinoma In Situ of the Breast: Preliminary Findings.

To explore the image features and the diagnostic value of contrast-enhanced ultrasound (CEUS) for ductal carcinoma in situ (DCIS) of the breast. A total of 96 female patients with a solitary and histologically proven DCIS were analyzed retrospectively, and 100 female cases of invasive ductal carcinoma (IDC) lesions were used as the control group. The Breast Imaging Reporting and Data System (BI-RADS) category of breast lesions was assessed according to conventional ultrasound features. The DCIS lesions were classified into mass type and non-mass type. The CEUS characteristics of these breast lesions were retrospectively analyzed qualitatively and quantitatively. The final gold standard was biopsy or surgery with histo-pathological examination. Comparing the ultrasound images of DCIS with that of IDC, there were significant differences in echo pattern, calcification morphology, and calcification distribution (p < .05 for all). There was a significant difference between DCIS and IDC in enhancement intensity, perfusion defects, peripheral high enhancement, intratumoral vessels, and arrival time (AT) (p < .05 for all). In the logistic multivariate regression analysis, two indicators linked with DCIS were recognized: perfusion defects (p = .002) and peripheral high enhancement (p < .001). In forecasting DCIS, the logistic regression equation resulted in an AUC of 0.689, a specificity of 0.720, and a sensitivity of 0.563. CEUS showed differences in enhancement characteristics between DCIS and IDC, with perfusion defects and peripheral high enhancement being associated with DCIS.

Retrospective analysis in in situ ductal carcinoma; 11 years of experience

Objective: To describe our experience in the diagnosis, treatment and follow up of patients with ductal carcinoma in situ (DCIS). Materials and methods: 953 breast carcinomas treated in the Mastology service of the Department of Gynecology and Obstetrics of the Hospital Aleman of Buenos Aires, between January 2004 and December 2014, are retrospectively analyzed. 206 biopsies (BRQ 25-mammotome 151-core biopsy 30) of ductal carcinomas in situ were identified, resulting after definitive pathological evaluation in 172 (18%) pure ductal carcinomas in situ of the total of 953 patients analyzed. Results: 206 biopsies (BRQ 25-mammotome 151-core biopsy 30) of ductal carcinomas in situ were identified, resulting after definitive pathological evaluation in 172 (18%) pure ductal carcinomas in situ of the total of 953 patients analyzed. The imaging report in the total of the 206 patients showed microcalcifications in 80.1%. The diagnosis of carcinoma in situ was made in 181 (87%) patients by preoperative microinvasive procedures and in the remaining 25 (13%) patients by radiosurgical biopsy (BRQ). There was evidence of 18.8% underdiagnosis after microinvasive procedures. In our case series, 84.3% were GH3/GH2 while 15.7% were GH 1. When comparing the size of the surgical specimens and correlating it with the 34 cases of invasive and microinvasive carcinoma, it showed that 54.8% of invasion in those tumors greater than 30 mm, 50.4% in those that exceeded 21 mm and in no case in those less than 10 mm. 20% of multicentric lesions were associated with invasive tumor. Sentinel lymph node technique was performed in 23.8% patients in the first surgery, resulting negative in all cases. When evaluating radiation and hormonal treatment, radiotherapy was performed in 131 patients (85.6%) and hormonal treatment was performed in 75% of the patients. In the follow-up until December 2014, 11 relapses (5.23%) were recorded. Conclusion: Our results are consistent with the international indexed literature in reference to diagnosis, treatment and recurrence rate pure of DCIS.

Prognostic Implications of MRI-assessed Intratumoral Fat in Hepatocellular Carcinoma: An Asian and European Cohort Study.

Background The clinicopathologic-radiologic and prognostic characteristics of intratumoral fat in hepatocellular carcinoma (HCC) are critical for personalized treatment but remain understudied. Purpose To investigate the clinicopathologic-radiologic associations and prognostic implications of MRI-assessed intratumoral fat in HCCs. Materials and Methods This retrospective cohort study included consecutive adult patients who underwent resection for solitary HCCs and preoperative contrast-enhanced MRI from two tertiary-care hospitals in East Asia (March 2011 to December 2021) and Western Europe (September 2012 to December 2019). MRI scans were independently evaluated by three radiologists at each hospital. Based on Liver Imaging Reporting and Data System (LI-RADS) version 2018, intratumoral fat was defined as "fat in mass more than adjacent liver," and the homogeneous subtype was defined as intratumoral fat "in absence of mosaic and nodule-in-nodule architecture." Recurrence-free survival (RFS) and overall survival (OS) were estimated using the Kaplan-Meier method and compared using the log-rank test. Cox regression analyses were conducted to identify factors associated with RFS and OS. Results A total of 933 patients were included in the Asian (n = 736; median age, 53 years [IQR, 45-62 years]; 626 male) and European (n = 207; median age, 64 years [IQR, 55-70 years]; 161 male) cohorts. MRI-assessed intratumoral fat was detected in 30% (215 of 726) and 31% (64 of 207) of patients in the Asian and European cohorts, respectively (P = .72). In both cohorts, the steatohepatitic subtype, nonperipheral washout, enhancing capsule, and mosaic architecture were more frequent in tumors with intratumoral fat (P value range, <.001 to .04). Intratumoral fat in general was not associated with RFS or OS in either cohort (P value range, .48-.97). However, in the Asian cohort, homogeneous intratumoral fat was associated with longer RFS (hazard ratio [HR], 0.60; P = .009) and OS (HR, 0.33; P = .008) in multivariable Cox regression analyses. Conclusion MRI-assessed intratumoral fat was more frequent in steatohepatitic HCCs and associated with nonperipheral washout, enhancing capsule, and mosaic architecture. Although intratumoral fat was generally nonprognostic, homogeneous intratumoral fat was associated with longer RFS and OS in the Asian cohort. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Harmath in this issue.

Detecting Clinically Significant Prostate Cancer in PI-RADS 3 Lesions Using T2w-Derived Radiomics Feature Maps in 3T Prostate MRI

Prostate Imaging Reporting and Data System version 2.1 (PI-RADS) category 3 lesions are a challenge in the clinical workflow. A better detection of the infrequently occurring clinically significant prostate cancer (csPCa) in PI-RADS 3 lesions is an important objective. The purpose of this study was to evaluate if feature maps calculated from T2-weighted (T2w) 3 Tesla (3T) MRI can help detect csPCa in PI-RADS category 3 lesions. In-house biparametric 3T prostate MRI examinations acquired between January 2019 and June 2023 because of elevated prostate-specific antigen (PSA) levels were retrospectively screened. Inclusion criteria were a PI-RADS 3 lesion and available results of an ultrasound-guided targeted and systematic biopsy. Exclusion criteria were a simultaneous PI-RADS category 4 or 5 lesion and hip replacement. Target lesions with the International Society of Urological Pathology (ISUP) grade group 1 were rated clinically insignificant PCa (ciPCa) and ≥2 csPCa. This resulted in 52 patients being included in the final analysis, of whom 11 (21.1%), 8 (15.4%), and 33 (63.5%) patients had csPCa, ciPCa, and no PCa, respectively, with the latter two groups being combined as non-csPCa. Eight of the csPCas were located in the peripheral zone (PZ) and three in the transition zone (TZ). In the non-csPCa group, 29 were located in the PZ and 12 in the TZ. Target lesions were marked with volumes of interest (VOIs) on axial T2w images. Axial T2w images were then converted to 93 feature maps. VOIs were copied into the maps, and feature quantity was retrieved directly. Features were tested for significant differences with the Mann–Whitney U-test. Univariate models for single feature performance and bivariate models implementing PSA density (PSAD) were calculated. Ten map-derived features differed significantly between the csPCa and non-csPCa groups (AUCs: 0.70–0.84). The diagnostic performance for TZ lesions (AUC: 0.83–1.00) was superior to PZ lesions (AUC: 0.74–0.85). In the bivariate models, performance in the PZ improved with AUCs &gt;0.90 throughout. Parametric feature maps alone and as bivariate models with PSAD can (?) noninvasively identify csPCa in PI-RADS 3 lesions and could serve as a quantitative tool reducing ambiguity in PI-RADS 3 lesions.

Optimal timing of anticoagulation after acute ischaemic stroke with atrial fibrillation (OPTIMAS): a multicentre, blinded-endpoint, phase 4, randomised controlled trial

The optimal timing of anticoagulation for patients with acute ischaemic stoke with atrial fibrillation is uncertain. We investigated the efficacy and safety of early compared with delayed initiation of direct oral anticoagulants (DOACs) in patients with acute ischaemic stroke associated with atrial fibrillation. We performed a multicentre, open-label, blinded-endpoint, parallel-group, phase 4, randomised controlled trial at 100 UK hospitals. Adults with atrial fibrillation and a clinical diagnosis of acute ischaemic stroke and whose physician was uncertain of the optimal timing for DOAC initiation were eligible for inclusion in the study. We randomly assigned participants (1:1) to early (ie, ≤4 days from stroke symptom onset) or delayed (ie, 7-14 days) anticoagulation initiation with any DOAC, using an independent online randomisation service with random permuted blocks and varying block length, stratified by stroke severity at randomisation. Participants and treating clinicians were not masked to treatment assignment, but all outcomes were adjudicated by a masked independent external adjudication committee using all available clinical records, brain imaging reports, and source images. The primary outcome was a composite of recurrent ischaemic stroke, symptomatic intracranial haemorrhage, unclassifiable stroke, or systemic embolism incidence at 90 days in a modified intention-to-treat population. We used a gatekeeper approach by sequentially testing for a non-inferiority margin of 2 percentage points, followed by testing for superiority. OPTIMAS is registered with ISRCTN (ISRCTN17896007) and ClinicalTrials.gov (NCT03759938), and the trial is ongoing. Between July 5, 2019, and Jan 31, 2024, 3648 patients were randomly assigned to early or delayed DOAC initiation. 27 participants did not fulfil the eligibility criteria or withdrew consent to include their data, leaving 3621 patients (1814 in the early group and 1807 in the delayed group; 1981 men and 1640 women) in the modified intention-to-treat analysis. The primary outcome occurred in 59 (3·3%) of 1814 participants in the early DOAC initiation group compared with 59 (3·3%) of 1807 participants in the delayed DOAC initiation group (adjusted risk difference [RD] 0·000, 95% CI -0·011 to 0·012). The upper limit of the 95% CI for the adjusted RD was less than the non-inferiority margin of 2 percentage points (pnon-inferiority=0·0003). Superiority was not identified (psuperiority=0·96). Symptomatic intracranial haemorrhage occurred in 11 (0·6%) participants allocated to the early DOAC initiation group compared with 12 (0·7%) participants allocated to the delayed DOAC initiation group (adjusted RD 0·001, -0·004 to 0·006; p=0·78). Early DOAC initiation within 4 days after ischaemic stroke associated with atrial fibrillation was non-inferior to delayed initiation for the composite outcome of ischaemic stroke, intracranial haemorrhage, unclassifiable stroke, or systemic embolism at 90 days. Our findings do not support the current common and guideline-supported practice of delaying DOAC initiation after ischaemic stroke with atrial fibrillation. British Heart Foundation.

Comparison of British Thyroid Association and TIRADS classifications and their impact on the radiological and surgical management of indeterminate thyroid nodules

BackgroundThe British Thyroid Association (BTA) Guidelines for the Management of Thyroid Cancer advocate for fine-needle aspiration cytology for all thyroid nodules graded indeterminate (U3) at ultrasound assessment. This approach raises concerns regarding potential over-diagnosis of low-risk lesions. Conversely, equivalent Thyroid Imaging Reporting and Data Systems (TIRADS) guidelines permit surveillance or discharge of indeterminate thyroid nodules of certain sizes. This service analysis analyses how guideline choice impacts the fine-needle aspiration cytology rate and subsequent surgical management of indeterminate thyroid nodules. MethodsAll patients with an indeterminate (U3) thyroid nodule identified on ultrasound over a 12-month period were included. Indeterminate thyroid nodules were retrospectively rescored using three equivalent TIRADS classifications by three independent reviewers, blinded to the histopathology. Hypothetical differences in fine-needle aspiration cytology rates and surgical activity were then compared. ResultsNinety-six nodules were identified. Retrospective application of TIRADS guidelines resulted in a hypothetical 44.8-55.2% reduction in fine-needle aspiration cytology performed for indeterminate thyroid nodules compared to BTA. A statistically significant increase in rates of surgical activity for indeterminate thyroid nodules was observed between BTA guidance and all retrospectively applied TIRADS guidelines (p<0.001). Of four confirmed thyroid cancers, three would have been unanimously removed. ConclusionUnder BTA guidance, increased fine-needle aspiration cytology rates for indeterminate thyroid nodules resulted in significantly increased surgical activity in our cohort compared to retrospectively applied TIRADS guidelines.

Evaluation and Use of Natural Language Processing (NLP) Reasoning and Classification Models to Support Clinical Trial Patient Identification and Enrollment in the Community Oncology Setting

Clinical trial research in oncology relies heavily on clinical documentation within the electronic medical record (EMR) to ascertain patient eligibility in clinical trials based on inclusion and exclusion criteria. The structured data elements within the EMR serve as the primary information source for defining patient cohorts, with clinical cancer stage and performance status being two pivotal criteria determining trial eligibility. The challenge arises from the inconsistent availability of clinical stage and performance status data within the structured fields of the EMR despite their consistent presence in clinical notes. Additionally, there is a deficiency of standardization of this data that exists in the unstructured field. Hence, due to lack of structured data and standardization of said data, there are limitations in developing artificial intelligence (AI) models. To increase the comprehensiveness of clinical records, a clinical research team at a community oncology practice was consulted to identify requirements and extract essential clinical features from de-identified data. The methods outlined in this paper focused on eliminating false positives to allow future development of Large Language Models (LLM) using the outputted structured fields which resulted in an increase in patient record completeness with high accuracy. The accuracy ranged from 97.5-97.75% for the models that were developed. Out of the 60,000+ patients, the numerical staging, TNM (tumor, node, metastasis) staging, and Karnofsky performance score models added a structured field for 29.62%, 21.01%, and 40.64% patients respectively. Additionally, a semi-supervised NLP algorithm was applied on the performance status algorithm which achieved a mean absolute error (MAE) of 1.57. This work demonstrates the use case of natural language processing (NLP) in optimizing the clinical research enrollment process by providing an efficient and accurate method to detect key clinical values in unstructured patient data. Similar methodology with more advanced algorithms such as LLM can be employed to detect additional patient elements such as molecular biomarkers, imaging reports, postoperative surgical outcomes (i.e., clear margins etc.) and patient treatment outcomes using the extracted structured fields.

A New Perspective in Diagnosis of Neuroendocrine Tumors and Pseudo Pheochromocytomas, Understanding the Biology Behind a Mislabeled Medical Terminology

Recently the incidence of undiagnosed hypertension that does not fit into either category of essential hypertension or secondary hypertension is becoming largely increased. Most of these cases are attributed to anxiety or forms of panic attacks associated with high blood pressure. Some in fact are lethal yet clinicians have been challenged to know the real cause of the disease. The term pseudopheochromocytoma widely used for patients who have typical presentation of pheochromocytoma but yet no concrete evidence of disease manifested in their imaging or lab reports to suggest increased production of adrenaline or neurepinephrine, as seen in typical pheochromocytomas. These patients suffer from life threatening attacks of hypertension, yet the only medical revenue offered are anxiolytics, or tricyclic antidepressants. Here we review a case of suspected pheochromocytoma and we present a novel theory about the real player behind all cases of pseudopheochromocytomas. Since in contrast to all other tumors, performing a tissue biopsy is detrimental in the case of pheochromocytomas, and therefore no one logically considers a tissue biopsy for adrenal gland, liquid biopsy( NETest by WREN Laboratory) is a great substitute with high accuracy to replace the tissue biopsy. We conclude that performing liquid biopsy on all cases of pseudopheochromocytoma is essentially life saving and can lead the clinician to appropriate course of action, when the other labs and functional imaging are inconclusive or negative. This is the very perfect example of why the traditional thinking of required presence of metanephrines in urine or blood, and nuclear studies(Dotetate scan/ Octrascan/ MiBG scan, etc…) for diagnosis of pheochromocytoma and surgical interventions depending on such tests is outdated.

The Horizon of Thyroid Imaging Reporting and Data System in the Diagnostic Performance of Thyroid Nodules: Clinical Application and Future Perspectives

The Horizon of Thyroid Imaging Reporting and Data System in the Diagnostic Performance of Thyroid Nodules: Clinical Application and Future Perspectives

Rare giant ovarian thecoma presented as atypical/incomplete Meigs' syndrome: A case image report

Rare giant ovarian thecoma presented as atypical/incomplete Meigs' syndrome: A case image report

Performance of breast MRI for high-risk screening during lactation.

To assess the diagnostic performance of breast MRI during lactation in the setting of high-risk breast cancer screening. Screening breast MRIs performed between April 2008 and March 2024 were retrospectively reviewed. Background parenchymal enhancement (BPE) grade was compared between lactating patients and patients who recently stopped lactating using the Mann-Whitney test. Breast Imaging Reporting and Data System (BI-RADS) scores prevalence rates were compared between lactating patients and controls encompassing young non-lactating patients using the Chi-square test. Diagnostic performance was calculated for patients with a biopsy reference or a 1-year radiologic follow-up. One-hundred forty-two screening breast MRIs were performed in lactating patients (n = 104, median age, 36.0 ± 6.0 years). Marked BPE appeared in 82% of cases (116/142), with a higher BPE grade in exams performed during lactation as compared with those performed in patients who had recently ceased lactating (p < 0.001). Screening MRIs performed during lactation had a higher rate of BI-RADS 3 scores (40/142, 28% vs. 683/8922, 7%, p < 0.001) and a lower rate of BI-RADS 1/2 scores (88/142, 62% vs. 7549/8922, 84.6%, p = 0.002) compared with those performed in controls (n = 8922). One pregnancy-associated breast cancer was detected, and one interval-cancer occurred. All MRI-guided biopsies were negative (n = 13). Screening breast MRI during lactation had 50% sensitivity (1/2), 60% specificity (72/120), 2.0% positive predictive value (1/49), and 98.6% negative predictive value (71/82). The efficacy of breast MRI for high-risk screening during lactation is limited by prominent BPE, leading to an increased rate of BI-RADS 3 categorization and diminished overall specificity. Question Studies on breast MRI during lactation were solely focused on studies conducted in patients with known cancer but not in the screening setting. Findings Screening breast MRI during lactation usually results in marked background parenchymal enhancement, negatively impacting its diagnostic performance. Clinical relevance Despite the lower performance, and amidst the significant risk of pregnancy-associated breast cancer, this screening approach remains relevant for lactating patients with high-risk profiles, such as BReast Cancer (BRCA) carriers. Radiologists should be familiar with the normal appearance of breast MRI duringlactation.

Diagnostic significance of peritumoral enhancement in distinguishing between muscle-invasive and non-muscle-invasive bladder cancer.

The aim of this study was to assess the prevalence of peritumoral enhancement (PTE) in patients with muscle-invasive bladder cancer (MIBC) and non-MIBC (NMIBC) and to propose a modified diagnostic criterion for Vesical Imaging Reporting and Data System (VI-RADS) that incorporates PTE. This retrospective study included 95 patients with bladder cancer (age, 72 ± 11; 77 men; 36 MIBCs and 59 Non-MIBCs) who underwent multiparametric MRI in our referral center between 2011 and 2023. The images were interpreted by four radiologists. The readers classified the possibility of muscle layer invasion into categories 1-5, based on the VI-RADS categorical diagnostic criterion. PTE was defined as a linear contrast enhancement observed at the edge of tumor invasion which is convex outward from the normal bladder wall and contrasts more than the normal muscle layer and tumor. A modified VI-RADS that upgrades the final VI-RADS category to 4 if PTE is present when the original VI-RADS category is 3 or less was proposed. The frequency of PTE in the MIBC and NMIBC groups was compared using the Fisher's exact test. Sensitivity and specificity for the diagnosis of MIBC were compared with the original VI-RADS using McNemar test. Pathologic diagnosis was used as the reference standard. PTE was present in 70-81% (25/36-29/36) of MIBC and absent in 92-98% (54/59-58/59) of non-MIBC. For all readers, the PTE was significantly more frequent (p < 0.001) in the MIBC group than the NMIBC group. The sensitivities of modified VI-RADS (75.0-86.1%) were significantly higher than those of original VI-RADS (41.7-55.6%) (p = 0.002-0.008). The specificity of modified VI-RADS (91.5-98.3%) were not statistically different from original VI-RADS (98.3-100%). In conclusion, PTE is a highly specific finding for MIBC. modified VI-RADS incorporating PTE increases sensitivity for MIBC.

Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o

BackgroundTo assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.MethodsA radiologist with 20 years’ experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with “Explain this medical report to a patient using simple language”. Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach’s α, and the Kruskal–Wallis test were used.ResultsMammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging.ConclusionThe model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care.Relevance statementSimplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice.Key PointsAI simplifies complex breast imaging reports, enhancing patient understanding.Simplified reports from AI maintain accuracy, improving patient comprehension significantly.Implementing AI reports enhances patient engagement and communication in breast imaging.Graphical

Inter-Reader Agreement for Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System Major Features and Final Categorization: A Subanalysis From a Prospective Multicenter Study.

Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) is used to definitively diagnose hepatocellular carcinoma (HCC) in patients at risk. However, the user variability associated with CEUS LI-RADS has not been validated in North American and European patients. This study aims to evaluate the inter-reader agreements of CEUS LI-RADS features for, and final categorization of, HCC in patients at risk. This retrospective multicenter clinical study used the database of a previous prospective multinational study, evaluating the accuracy of CEUS LI-RADS for HCC diagnosis in patients at risk. All cases were first evaluated by a site physician performing/supervising the CEUS examination. Randomly selected cases were re-evaluated by a blinded central reader. Final diagnosis was confirmed with the reference standard, which was a composite of imaging tests and histology. Cohen's kappa test was used to evaluate inter-reader agreement. This study included 150 liver nodules and 58.0% (87/150) were confirmed as HCC, 4.7% (7/150) non-HCC malignancies, 22.7% (34/150) had no confirmed final diagnosis, and 14.7% (22/150) were nonmalignant. Inter-reader agreements were substantial for CEUS LI-RADS categorization (κ = 0.61; 95% confidence interval [CI]: 0.51-0.71) and major features assessment (ranged κ = 0.64-0.78), LR-5 (κ = 0.65; 95% CI: 0.52-0.77), and LR-M (κ = 0.67; 95% CI: 0.44-0.90), while for LR-1 and LR-2 categorization was almost perfect (κ = 0.85; 95% CI: 0.65-1.00). Our study reported a substantial inter-reader agreement for overall CEUS LI-RADS categorization, especially for LR-5 and LR-M, and major imaging features of HCC, further confirming CEUS LI-RADS as a valuable and reliable tool for diagnosis of HCC.

Role of Image-Guided Biopsy in Nonpalpable Breast Lesions: A Study in the Sub-Himalayan Region of North India

Background Nonpalpable breast lesions pose a challenge in their early diagnosis. Image-guided biopsy is preferred in these cases so that a pathological diagnosis of breast carcinoma is reached timely for a better prognosis as the disease has an increased chance of successful outcome with early identification and treatment. Objective The study aims at evaluating the role of stereotactic core needle biopsy (CNB) and percutaneous ultrasound-guided core needle biopsy (US-CNB) in diagnosing suspicious nonpalpable breast lesions. Methods Our study included 35 patients with nonpalpable breast lesions and having a Breast Imaging Reporting and Data System (BI-RADS) risk assessment category IV or V on mammography or sonography. These 35 lesions were subjected to percutaneous image-guided (stereotactic or US) biopsy for histopathological analysis. Results Out of a total of 35 cases, 17 were pathologically malignant (48.6%), with the most common subtype being invasive ductal carcinoma (82.3%). Twenty-nine cases underwent US-CNB, 16 (55.1%) of which were malignant and 13 (44.8%) were benign on histopathological evaluation (HPE). The remaining six cases, which on mammography showed no mass but suspicious malignant calcification only, were subjected to stereotactic CNB, out of which one (16.6%) was malignant and five (83.3%) were benign on HPE. Hence, the lesions visible on sonography were more likely to be malignant. Conclusion Sonography and mammography play a complimentary role in detecting breast carcinoma. Percutaneous biopsy under image guidance can be used as an accurate diagnostic alternative to open surgical excisional biopsy to avoid diagnostic delay.

Automated breast density assessment for full-field digital mammography and digital breast tomosynthesis.

Mammographic density is a strong risk factor for breast cancer (BC) and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for both full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) as both types of exams are acquired in standard clinical practice. We trained a deep learning model to automate the estimation of BI-RADS density from a prospective Washington University (WashU) clinic-based cohort of 9,714 women, entering into the cohort in 2013 with follow-up through, October 31, 2020. The cohort included 27% non-Hispanic Black women. The trained algorithm was assessed in an external validation cohort that included 18,360 women screened at Emory from January 1, 2013 and followed through December 31, 2020 that included 42% non-Hispanic Black women. Our model-estimated BI-RADS density demonstrated substantial agreement with the density as assessed by radiologist. In the external validation, the agreement with radiologists for category B 81% and C 77% for FFDM and B 83% and C 74% for DBT show important distinction for separation of women with dense breast. We obtained a Cohen's κ of 0.72 (95% CI, 0.71, 0.73) in FFDM and 0.71 (95% CI 0.69, 0.73) in DBT. We provided a consistent and fully automated BI-RADS estimation for both FFDM and DBT using a deep learning model. The software can be easily implemented anywhere for clinical use and risk prediction.