Performance of Diffusion-weighted Imaging-based Noncontrast MRI Protocols for Diagnosis of Breast Cancer: A Systematic Review and Meta-Analysis.

Background Diffusion-weighted imaging (DWI) is increasingly recognized as a powerful diagnostic tool and tested alternative to contrast-enhanced (CE) breast MRI. Purpose To perform a systematic review and meta-analysis that assesses the diagnostic performance of DWI-based noncontrast MRI protocols (ncDWI) for the diagnosis of breast cancer. Materials and Methods A systematic literature search in PubMed for articles published from January 1985 to September 2023 was performed. Studies were excluded if they investigated malignant lesions or selected patients and/or lesions only, used DWI as an adjunct technique to CE MRI, or were technical studies. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. Additional subgroup comparisons of ncDWI to CE MRI and standard mammography were performed. Results A total of 28 studies were included, with 4406 lesions (1676 malignant, 2730 benign) in 3787 patients. The pooled sensitivity and specificity of ncDWI were 86.5% (95% CI: 81.4, 90.4) and 83.5% (95% CI: 76.9, 88.6), and both measures presented with high between-study heterogeneity (I 2 = 81.6% and 91.6%, respectively; P < .001). CE MRI (18 studies) had higher sensitivity than ncDWI (95.1% [95% CI: 92.9, 96.7] vs 88.9% [95% CI: 82.4, 93.1], P = .004) at similar specificity (82.2% [95% CI: 75.0, 87.7] vs 82.0% [95% CI: 74.8, 87.5], P = .97). Compared with ncDWI, mammography (five studies) showed no evidence of a statistical difference for sensitivity (80.3% [95% CI: 56.3, 93.3] vs 56.7%; [95% CI: 41.9, 70.4], respectively; P = .09) or specificity (89.9% [95% CI: 85.5, 93.1] vs 90% [95% CI: 61.3, 98.1], respectively; P = .62), but ncDWI had a higher area under the summary receiver operating characteristic curve (0.93 [95% CI: 0.91, 0.95] vs 0.78 [95% CI: 0.74, 0.81], P < .001). Conclusion A direct comparison with CE MRI showed a modestly lower sensitivity at similar specificity for ncDWI, and higher diagnostic performance indexes for ncDWI than standard mammography. Heterogeneity was high, thus these results must be interpreted with caution. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kataoka and Iima in this issue.

Dual-Tracer PET-MRI-Derived Imaging Biomarkers for Prediction of Clinically Significant Prostate Cancer.

PURPOSE: To investigate if imaging biomarkers derived from 3-Tesla dual-tracer [(18)F]fluoromethylcholine (FMC) and [68Ga]Ga-PSMAHBED-CC conjugate 11 (PSMA)-positron emission tomography can adequately predict clinically significant prostate cancer (csPC). METHODS: We assessed 77 biopsy-proven PC patients who underwent 3T dual-tracer PET/mpMRI followed by radical prostatectomy (RP) between 2014 and 2017. We performed a retrospective lesion-based analysis of all cancer foci and compared it to whole-mount histopathology of the RP specimen. The primary aim was to investigate the pretherapeutic role of the imaging biomarkers FMC- and PSMA-maximum standardized uptake values (SUVmax) for the prediction of csPC and to compare it to the mpMRI-methods and PI-RADS score. RESULTS: Overall, we identified 104 cancer foci, 69 were clinically significant (66.3%) and 35 were clinically insignificant (33.7%). We found that the combined FMC+PSMA SUVmax were the only significant parameters (p < 0.001 and p = 0.049) for the prediction of csPC. ROC analysis showed an AUC for the prediction of csPC of 0.695 for PI-RADS scoring (95% CI 0.591 to 0.786), 0.792 for FMC SUVmax (95% CI 0.696 to 0.869), 0.852 for FMC+PSMA SUVmax (95% CI 0.764 to 0.917), and 0.852 for the multivariable CHAID model (95% CI 0.763 to 0.916). Comparing the AUCs, we found that FMC+PSMA SUVmax and the multivariable model were significantly more accurate for the prediction of csPC compared to PI-RADS scoring (p = 0.0123, p = 0.0253, respectively). CONCLUSIONS: Combined FMC+PSMA SUVmax seems to be a reliable parameter for the prediction of csPC and might overcome the limitations of PI-RADS scoring. Further prospective studies are necessary to confirm these promising preliminary results.

Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease.

OBJECTIVES: Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD). MATERIALS AND METHODS: A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses). RESULTS: Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available. CONCLUSION: The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice. KEY POINTS: • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083).

Contrast-enhanced Mammography versus Contrast-enhanced Breast MRI: A Systematic Review and Meta-Analysis.

Background Contrast-enhanced mammography (CEM) is a more accessible alternative to contrast-enhanced MRI (CE-MRI) in breast imaging, but a summary comparison of published studies is lacking. Purpose To directly compare the performance of CEM and CE-MRI regarding sensitivity, specificity, and negative predictive value in detecting breast cancer, involving all publicly available studies in the English language. Materials and Methods Two readers extracted characteristics of studies investigating the comparative diagnostic performance of CEM and CE-MRI in detecting breast cancer. Studies published until April 2021 were eligible. Sensitivity, specificity, negative predictive value, and positive and negative likelihood ratios were calculated using bivariate random effects models. A Fagan nomogram was used to identify the maximum pretest probability at which posttest probabilities of a negative CEM or CE-MRI examination were in line with the 2% malignancy rate benchmark for downgrading a Breast Imaging Reporting and Data System (BI-RADS) category 4 to a BI-RADS category 3 result. I 2 statistics, Deeks funnel plot asymmetry test for publication bias, and meta-regression were used. Results Seven studies investigating 1137 lesions (654 malignant, 483 benign) with an average cancer prevalence of 65.3% (range: 47.3%-82.2%) were included. No publication bias was found (P = .57). While the positive likelihood ratio was equal at a value of 3.1 for CE-MRI and 3.6 for CEM, the negative likelihood ratio of CE-MRI (0.04) was lower than that with CEM (0.12). CE-MRI had higher sensitivity for breast cancer than CEM (97% [95% CI: 86, 99] vs 91% [95% CI: 77, 97], respectively; P < .001) but lower specificity (69% [95% CI: 46, 85] vs 74% [95% CI: 52, 89]; P = .09). A Fagan nomogram demonstrated that the maximum pretest probability at which both tests could rule out breast cancer was 33% for CE-MRI and 14% for CEM. Furthermore, iodine concentration was positively associated with CEM sensitivity and negatively associated with its specificity (P = .04 and P < .001, respectively). Conclusion Contrast-enhanced MRI had superior sensitivity and negative likelihood ratios with higher pretest probabilities to rule out malignancy compared with contrast-enhanced mammography. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mann and Veldhuis in this issue.

Breast MRI: does a clinical decision algorithm outweigh reader experience?

OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (MRIb) is increasingly used for both screening and assessment purposes. The Kaiser score (KS) is a clinical decision algorithm, which formalizes and guides diagnosis in breast MRI and is expected to compensate for lesser reader experience. The aim was to evaluate the diagnostic performance of untrained residents using the KS compared to off-site radiologists experienced in breast imaging using only MR BI-RADS. METHODS: Three off-site, board-certified radiologists, experienced in breast imaging, interpreted MRIb according to the MR BI-RADS scale. The same studies were read by three residents in radiology without prior training in breast imaging using the KS. All readers were blinded to clinical information. Histology was used as the gold standard. Statistical analysis was conducted by comparing the AUC of the ROC curves. RESULTS: A total of 80 women (median age 52 years) with 93 lesions (32 benign, 61 malignant) were included. The individual within-group performance of the three expert readers (AUC 0.723-0.742) as well as the three residents was equal (AUC 0.842-0.928), p > 0.05, respectively. But, the rating of each resident using the KS significantly outperformed the experts' ratings using the MR BI-RADS scale (p ≤ 0.05). CONCLUSION: The KS helped residents to achieve better results in reaching correct diagnoses than experienced radiologists empirically assigning MR BI-RADS categories in a clinical "problem solving MRI" setting. These results support that reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience. KEY POINTS: • Reporting breast MRI benefits more from using a diagnostic algorithm rather than expert experience in a clinical "problem solving MRI" setting. • The Kaiser score, which provides a clinical decision algorithm for structured reporting, helps residents to reach an expert level in breast MRI reporting and to even outperform experienced radiologists using MR BI-RADS without further formal guidance.

Prostate-Specific Membrane Antigen (PSMA) Expression in Tumor-Associated Neovasculature Is an Independent Prognostic Marker in Patients with Ovarian Cancer.

Prostate-specific membrane antigen (PSMA) is present in the tumor-associated neovasculature of many cancer types. Current data in ovarian cancer are limited and controversial; thus, the aim of this study was to investigate PSMA expression in a larger and homogenous patient cohort. This might lead to further studies investigating the use of imaging and therapeutic modalities targeting PSMA. Eighty patients with advanced stage high-grade serous ovarian cancers were included. Using immunohistochemistry, PSMA and CD31, a marker for endothelial cells, were examined in whole tissue sections. Percentage and intensity of PSMA expression were determined in the neovasculature. Expression levels were correlated with clinicopathological parameters and survival. Low (≤10%), medium (20-80%), and high (≥90%) PSMA expression was found in 14, 46, and 20 ovarian cancer samples, respectively. PSMA expression was confined to tumor-associated neovasculature and significantly correlated with progression-free (HR 2.24, 95% CI 1.32-3.82, p = 0.003) and overall survival (HR 2.73, 95% CI 1.41-5.29, p = 0.003) in multivariate models, considering age, FIGO stage, and residual disease. This is the first study showing a clinical relevance for PSMA in patients with ovarian cancer. PSMA was detected in the vast majority of cancer samples and showed an impact on survival.

Rectal preparation significantly improves prostate imaging quality: Assessment of the PI-QUAL score with visual grading characteristics.

PURPOSE: To investigate the effects of a rectal preparation regimen, that consisted of a rectal cleansing enema and an endorectal gel filling protocol, on prostate imaging quality (PI-QUAL). METHODS: Multiparametric MRI (mpMRI) was performed in 150 consecutive patients divided into two groups of 75 patients. One group received a rectal preparation with a cleansing enema and endorectal gel filling (median age 65.3 years, median PSA level 6 ng/ml). The other patient group did not receive such a preparation (median age 64 years, median PSA level 6 ng/ml). Two uroradiologists independently rated general image quality and lesion visibility on diffusion-weighted imaging (DWI), T2-weighted (T2w), and dynamic contrast-enhanced (DCE) images using a five-point ordinal scale. In addition, two uroradiologists assigned PI-QUAL scores, using the dedicated scoring sheet. Data sets were compared using visual grading characteristics (VGC) and receiver operating characteristics (ROC)/ area under the curve (AUC) analysis. RESULTS: VGC revealed significantly better general image quality for DWI (AUC R1 0.708 (0.628-0.779 CI, p < 0.001; AUC R2 0.687 (0.606-0.760 CI, p < 0.001) and lesion visibility for both readers (AUC R1 0.729 (0.607-0.831 CI, p < 0.001); AUC R2 0.714 (0.590-0.818CI, p < 0.001) in the preparation group. For T2w imaging, rectal preparation resulted in significantly better lesion visibility for both readers (R1 0.663 (0.537-0.774 CI, p = 0.014; R2 0.663 (0.537-0.774 CI, p = 0.014)). Averaged PI-QUAL scores were significantly improved with rectal preparation (AUC R3/R4 0.667, CI 0.581-0.754, p < 0.001). CONCLUSION: Rectal preparation significantly improved prostate imaging quality (PI-QUAL) and lesion visibility. Hence, a rectal preparation regimen consisting of a rectal cleansing enema and an endorectal gel filling could be considered.

An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies.

OBJECTIVES: Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies. METHODS: This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network-derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C1, 100%, and C2, ≥ 95% sensitivity). RESULTS: Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18-85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8-89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C1) and 36.2% (C2). CONCLUSION: The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies. KEY POINTS: • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers.

Can supplementary contrast-enhanced MRI of the breast avoid needle biopsies in suspicious microcalcifications seen on mammography? A systematic review and meta-analysis.

PURPOSE: To analyze the rate of potentially avoidable needle biopsies in mammographically suspicious calcifications if supplementary Contrast-Enhanced MRI (CE-MRI) is negative. METHODS: Using predefined criteria, a systematic review was performed. Studies investigating the use of supplemental CE-MRI in the setting of mammographically suspicious calcifications undergoing stereotactic biopsy and published between 2000 and 2020 were eligible. Two reviewers extracted study characteristics and true positives (TP), false positives, true negatives and false negatives (FN). Specificity, in this setting equaling the number of avoidable biopsies and FN rates were calculated. The maximum pre-test probability at which post-test probabilities of a negative CE-MRI met with BI-RADS benchmarks was determined by a Fagan nomogram. Random-effects models, I2-statistics, Deek's funnel plot testing and meta-regression were employed. P-values <0.05 were considered significant. RESULTS: Thirteen studies investigating 1414 lesions with a cancer prevalence of 43.6% (range: 22.7-66.9%) were included. No publication bias was found (P = 0.91). CE-MRI performed better in pure microcalcification studies compared to those also including associate findings (P < 0.001). In the first group, the pooled rate of avoidable biopsies was 80.6% (95%-CI: 64.6-90.5%) while the overall and invasive cancer FN rates were 3.7% (95%-CI: 1.2-6.2%) and 1.6% (95%-CI 0-3.6%), respectively. Up to a pre-test probability of 22%, the post-test probability did not exceed 2%. CONCLUSION: A negative supplementary CE-MRI could potentially avoid 80.6% of unnecessary stereotactic biopsies in BI-RADS 4 microcalcifications at a cost of 3.7% missed breast cancers, 1.6% invasive. BI-RADS benchmarks for downgrading mammographic calcifications would be met up to a pretest probability of 22%.

Impact of frailty on the management of patients with gynecological cancer aged 80 years and older.

PURPOSE: To assess the impact of frailty on compliance of standard therapy, complication, rate and survival in patients with gynecological malignancy aged 80 years and older. METHODS: In total, 83 women with gynecological malignancy (vulva, endometrial, ovarian or cervical cancer) who underwent primary treatment between 2007 and 2017 were retrospectively analyzed. Frailty index was calculated and its association with compliance of standard treatment, peri- and postoperative mortality and morbidity, and survival was evaluated. RESULTS: Frailty was observed in 24.1% of cases. Both frail and non-frail patients were able to receive standard therapy in most cases - 75.0% and 85.7%, respectively (p = 0.27). Frail patients did not show an increased postoperative complication rate. Frail patients had shorter 3 years overall survival rates (28%) when compared to non-frail patients (55%) (p = 0.02). In multivariable analysis high frailty index (Hazard Ratio [HR] 12.15 [1.39-106.05], p = 0.02) and advanced tumor stage (HR 1.33 [1.00-1.76], p = 0.05) were associated with poor overall survival, but not age, histologic grading, performance status, and compliance of standard therapy. CONCLUSION: Majority of patients was able to receive standard therapy, as suggested by the tumor board, irrespective of age and frailty. Nonetheless, frailty is a common finding in patients with gynecological malignancy aged 80 years and older. Frail patients show shorter progression-free, and overall survival within this cohort.

Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning.

Gliomas are the most common type of tumor in the brain. Although the definite diagnosis is routinely made ex vivo by histopathologic and molecular examination, diagnostic work-up of patients with suspected glioma is mainly done using MRI. Nevertheless, l-S-methyl-11C-methionine (11C-MET) PET holds great potential in the characterization of gliomas. The aim of this study was to establish machine-learning-driven survival models for glioma built on in vivo 11C-MET PET characteristics, ex vivo characteristics, and patient characteristics. Methods: The study included 70 patients with a treatment-naïve glioma that was 11C-MET-positive and had histopathology-derived ex vivo feature extraction, such as World Health Organization 2007 tumor grade, histology, and isocitrate dehydrogenase 1 R132H mutational status. The 11C-MET-positive primary tumors were delineated semiautomatically on PET images, followed by the extraction of tumor-to-background-based general and higher-order textural features by applying 5 different binning approaches. In vivo and ex vivo features, as well as patient characteristics (age, weight, height, body mass index, Karnofsky score), were merged to characterize the tumors. Machine-learning approaches were used to identify relevant in vivo, ex vivo, and patient features and their relative weights for predicting 36-mo survival. The resulting feature weights were used to establish 3 predictive models per binning configuration: one model based on a combination of in vivo, ex vivo, and clinical patient information (M36IEP); another based on in vivo and patient information only (M36IP); and a third based on in vivo information only (M36I). In addition, a binning-independent model based on ex vivo and patient information only (M36EP) was created. The established models were validated in a Monte Carlo cross-validation scheme. Results: The most prominent machine-learning-selected and -weighted features were patient-based and ex vivo-based, followed by in vivo-based. The highest areas under the curve for our models as revealed by the Monte Carlo cross-validation were 0.9 for M36IEP, 0.87 for M36EP, 0.77 for M36IP, and 0.72 for M36IConclusion: Prediction of survival in amino acid PET-positive glioma patients was highly accurate using computer-supported predictive models based on in vivo, ex vivo, and patient features.