DCE Sequences Research Articles

Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.

Uterine fibroids (UFs) can pose a serious health risk to women. UFs are benign tumors that vary in clinical presentation from asymptomatic to causing debilitating symptoms. UF management is limited by our inability to predict UF growth rate and future morbidity. We aim to develop a predictive model to identify UFs with increased growth rates and possible resultant morbidity. We retrospectively analyzed 44 expertly outlined UFs from 20 patients who underwent two multi-parametric MR imaging exams as part of a prospective study over an average of 16 months. We identified 44 initial features by extracting quantitative magnetic resonance imaging (MRI) features plus morphological and textural radiomics features from DCE, T2, and apparent diffusion coefficient sequences. Principal component analysis reduced dimensionality, with the smallest number of components explaining over 97.5% of the variance selected. Employing a leave-one-fibroid-out scheme, a linear discriminant analysis classifier utilized these components to output a growth risk score. The classifier incorporated the first three principal components and achieved an area under the receiver operating characteristic curve of 0.80 (95% confidence interval [0.69; 0.91]), effectively distinguishing UFs growing faster than the median growth rate of from slower-growing ones within the cohort. Time-to-event analysis, dividing the cohort based on the median growth risk score, yielded a hazard ratio of 0.33 [0.15; 0.76], demonstrating potential clinical utility. We developed a promising predictive model utilizing quantitative MRI features and principal component analysis to identify UFs with increased growth rates. Furthermore, the model's discrimination ability supports its potential clinical utility in developing tailored patient and fibroid-specific management once validated on a larger cohort.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study.

Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF categories such as shape based, Gray level dependence matrix (GLDM), Gray level co-occurrence matrix (GLCM), First order, Gray level run length matrix (GLRLM), Gray level size zone matrix (GLSZM), NGTDM (Neighbouring gray tone difference matrix) were extracted from the DCE-MRI sequence using a 3D slicer. The maximum relevance and minimum redundancy (mRMR) was applied using Google Colab for identifying the top fifteen relevant radiomic features. The Mann-Whitney U test was performed to identify significant RF for differentiating IDC and ILC. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Ten DCE MRI-based RFs used in our study showed a significant difference (p <0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

Differentiation of invasive ductal and lobular carcinoma of the breast using MRI radiomic features: a pilot study

Background Breast cancer (BC) is one of the main causes of cancer-related mortality among women. For clinical management to help patients survive longer and spend less time on treatment, early and precise cancer identification and differentiation of breast lesions are crucial. To investigate the accuracy of radiomic features (RF) extracted from dynamic contrast-enhanced Magnetic Resonance Imaging (DCE MRI) for differentiating invasive ductal carcinoma (IDC) from invasive lobular carcinoma (ILC). Methods This is a retrospective study. The IDC of 30 and ILC of 28 patients from Dukes breast cancer MRI data set of The Cancer Imaging Archive (TCIA), were included. The RF were extracted from the DCE-MRI sequence using a 3D slicer. The relevance of RF for differentiating IDC from ILC was evaluated using the maximum relevance minimum redundancy (mRMR) and Mann-Whitney test. Receiver Operating Characteristic (ROC) curve analysis was performed to ascertain the accuracy of RF in distinguishing between IDC and ILC. Results Ten DCE MRI-based RFs used in our study showed a significant difference (p &lt;0.001) between IDC and ILC. We noticed that DCE RF, such as Gray level run length matrix (GLRLM) gray level variance (sensitivity (SN) 97.21%, specificity (SP) 96.2%, area under curve (AUC) 0.998), Gray level co-occurrence matrix (GLCM) difference average (SN 95.72%, SP 96.34%, AUC 0.983), GLCM interquartile range (SN 95.24%, SP 97.31%, AUC 0.968), had the strongest ability to differentiate IDC and ILC. Conclusions MRI-based RF derived from DCE sequences can be used in clinical settings to differentiate malignant lesions of the breast, such as IDC and ILC, without requiring intrusive procedures.

MRI monitoring of combined therapy with transcatheter arterial delivery of NK cells and systemic administration of sorafenib for the treatment of HCC.

This preclinical study explored the synergistic potential of sorafenib and NK cell chemoimmunotherapy to combat hepatocellular carcinoma (HCC) in a rat model. We aimed to enhance NK cell cytotoxicity through IL-12/18 cytokines supplementation and elucidate the underlying molecular mechanisms driving this collaborative antitumor action. Twenty-four Sprague-Dawley rats were divided into distinct treatment groups, receiving sorafenib via gavage and NK cells via catheterization of the proper hepatic artery. Tumor growth and treatment response were monitored through weekly MRI scans, including T1w, T2w, DCE, and DWI sequences. Histological examinations assessed tumor cell viability, apoptosis fraction, and microvessel density. The combined therapy demonstrated significant inhibition of tumor growth, angiogenesis, and induction of durable antitumor immunity compared to either modality alone. DCE-MRI and DWI revealed distinct alterations in tumor microvasculature, highlighting the effectiveness of the combination. Our findings highlight the promise of sorafenib-augmented NK cell chemoimmunotherapy as a potential therapeutic strategy for HCC management. The targeted delivery of IL-12/18 cytokines supplemented NK cells effectively enhanced cytotoxicity within the tumor microenvironment, leading to improved antitumor responses. Further investigation in clinical trials is warranted to validate these findings in human patients and explore the translational potential of this approach.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

ObjectiveThis study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). MethodsThis study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018–July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. ResultsA total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555–0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. ConclusionThe combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

MA-QC: Free online software for prostate MR quality control and PI-QUAL assessment

PurposeTo validate MRI Analyzer Quality Control (MA-QC), a free and open-source online software designed to facilitate MR data acquisition quality control and PI-QUAL score calculation. Material and methodsMA-QC is a web-based software, designed for analysing DICOM data related to MR acquisition parameters. The software allows automatic extraction of 18 technical criteria, and manual input of 12 visual criteria, to calculate the PI-QUAL score. We collected 100 prostate MRI datasets from four MR device manufacturers to test data compatibility, automatic sequence recognition, and robustness of technical criteria extraction from DICOM data. The main issue was to determine the spatial resolution in the phase and frequency directions, due to variable encoding of the DICOM datasets. ResultsAcquisition data could be extracted from all sample examinations (100%), with a median analysis speed of 15.2 ± 4.4 images per second and mean processing time of 96 [11–326] seconds per examination. MA-CQ automatically detected the optimal T2-w, DWI and DCE sequences in 71 out of 100 (71%) cases, and required manual selection of at least one sequence in 29 out of 100 (29%) cases to get the best parameters. Display of technical criteria for the 3 sequences was instantaneous. PI-QUAL score could be calculated in all cases. ConclusionThis software brings substantial help in the quality assessment of prostate MRI examinations, by providing fast extraction of series data and the 18 technical parameters of PI-QUAL. PI-QUAL scoring can be performed in less than two minutes, helping to focus on the visual criteria, allowing use of this software in the clinical workflow in the aim of improving overall image quality in prostate MR imaging.

Impact of DWI and ADC values in Ovarian-Adnexal Reporting and Data System (O-RADS) MRI score

PurposeIntroduce DWI and quantitative ADC evaluation in O-RADS MRI system and observe how diagnostic performance changes. Assess its validity and reproducibility between readers with different experience in female pelvic imaging. Finally, evaluate any correlation between ADC value and histotype in malignant lesions.Materials and MethodsIn total, 173 patients with 213 indeterminate adnexal masses (AMs) on ultrasound were subjected to MRI examination, from which 140 patients with 172 AMs were included in the final analysis. Standardised MRI sequences were used, including DWI and DCE sequences. Two readers, blinded to histopathological data, retrospectively classified AMs according to the O-RADS MRI scoring system. A quantitative analysis method was applied by placing a ROI on the ADC maps obtained from single-exponential DWI sequences. AMs considered benign (O-RADS MRI score 2) were excluded from the ADC analysis.ResultsExcellent inter-reader agreement was found in the classification of lesions according to the O-RADS MRI score (K = 0.936; 95% CI). Two ROC curves were created to determine the optimal cut-off value for the ADC variable between O-RADS MRI categories 3–4 and 4–5, respectively, 1.411 × 10–3 mm2/sec and 0.849 × 10–3 mm2/sec. Based on these ADC values, 3/45 and 22/62 AMs were upgraded, respectively, to score 4 and 5, while 4/62 AMs were downgraded to score 3. ADC values correlated significantly with the ovarian carcinoma histotype (p value < 0.001).ConclusionOur study demonstrates the prognostic potential of DWI and ADC values in the O-RADS MRI classification for better radiological standardisation and characterisation of AMs.

PD22-01 THE GLIMPSE STUDY: GLOBAL VARIATION IN THE QUALITY OF MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING OF THE PROSTATE FROM THE PRIME TRIAL

You have accessJournal of UrologyCME1 Apr 2023PD22-01 THE GLIMPSE STUDY: GLOBAL VARIATION IN THE QUALITY OF MULTIPARAMETRIC MAGNETIC RESONANCE IMAGING OF THE PROSTATE FROM THE PRIME TRIAL Aqua Asif, Francesco Giganti, Vinson Wai-Shun Chan, Marimo Rossiter, Arjun Nathan, Pramit Khetrapal, Louise Dickinson, Shonit Punwani, Chris Brew-Graves, Alex Freeman, Mark Emberton, Caroline M. Moore, Clare Allen, Veeru Kasivisvanathan, and Prime Quality Improvement Group Aqua AsifAqua Asif More articles by this author , Francesco GigantiFrancesco Giganti More articles by this author , Vinson Wai-Shun ChanVinson Wai-Shun Chan More articles by this author , Marimo RossiterMarimo Rossiter More articles by this author , Arjun NathanArjun Nathan More articles by this author , Pramit KhetrapalPramit Khetrapal More articles by this author , Louise DickinsonLouise Dickinson More articles by this author , Shonit PunwaniShonit Punwani More articles by this author , Chris Brew-GravesChris Brew-Graves More articles by this author , Alex FreemanAlex Freeman More articles by this author , Mark EmbertonMark Emberton More articles by this author , Caroline M. MooreCaroline M. Moore More articles by this author , Clare AllenClare Allen More articles by this author , Veeru KasivisvanathanVeeru Kasivisvanathan More articles by this author , and Prime Quality Improvement Group More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003295.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Multiparametric MRI (mpMRI) is standard-of-care in patients with clinical suspicion of prostate cancer (PCa). However, there is still high variability in MRI quality. We aim to offer the first global 'GLIMPSE' into prostate mpMRI quality variation, analysing data from the PRIME Trial, and to determine whether basic modifications to MRI protocols could optimise diagnostic quality. METHODS: PRIME is a prospective, international, multicentre, level 1-evidence diagnostic study. It aims to recruit 500 men to evaluate whether biparametric MRI is non-inferior to mpMRI in detecting clinically significant PCa. For each scanner, centres were invited to submit 5 consecutive MRI scans of patients with suspected PCa, the detailed MRI protocol, PI-RADS v2.1 scores and pathology results. These were evaluated in consensus by 2 expert radiologists, blinded to the MRI and biopsy results. MRI quality was assessed for each scanner using the PI-QUAL scoring system — a Likert scale using PI-RADS v2.1 recommendations and each MR sequence (DWI, T2-WI and DCE). A PI-QUAL score 5/5 means the scan is of optimal diagnostic quality, using all 3 sequences; 3/5 means at least 2 sequences together are of this, whilst 1/5 means all sequences are below the minimum standard. Feedback was provided for scanners not reaching PI-QUAL 5 to improve MRI quality and centres were invited to resubmit new images using modified protocols for re-evaluation. RESULTS: 66 centres from 22 countries across 5 continents expressed an interest to take part in PRIME, 42 (64%) of which took part in the GLIMPSE study. 391 scans from 71 different scanners were evaluated, 54 (76%) were 3T systems, and 5 (7%) used an endorectal coil. On initial review, 9/71 (13%) scanners scored PI-QUAL 3, 39/71 (55%) scored PI-QUAL 4 and 23/71 (32%) scored PI-QUAL 5. All scanners were of adequate diagnostic quality for T2-WI and DWI sequences, whilst only 58/71 (82%) were for DCE sequences. Recommendations were primarily made for DCE sequences (62%, 44/71), followed by DWI (35%, 25/71) and T2-WI (25%, 18/71). After feedback and modifications, MRI quality increased on final review with 62/71 (87%) of scanners reaching PI-QUAL 5 compared to only 32% at initial review. CONCLUSIONS: The GLIMPSE Study offers the first global overview into prostate MR image quality variation. Initial MRI quality was fair, with room for improvement, particularly with DCE imaging. With basic changes in-line with PI-RADS recommendations, global MRI quality can be easily improved. Source of Funding: The PRIME trial (NCT04571840) is funded by Prostate Cancer UK, The John Black Charitable Foundation, the European Association of Urology Research Foundation, and the Dieckmann Foundation © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e664 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Aqua Asif More articles by this author Francesco Giganti More articles by this author Vinson Wai-Shun Chan More articles by this author Marimo Rossiter More articles by this author Arjun Nathan More articles by this author Pramit Khetrapal More articles by this author Louise Dickinson More articles by this author Shonit Punwani More articles by this author Chris Brew-Graves More articles by this author Alex Freeman More articles by this author Mark Emberton More articles by this author Caroline M. Moore More articles by this author Clare Allen More articles by this author Veeru Kasivisvanathan More articles by this author Prime Quality Improvement Group More articles by this author Expand All Advertisement PDF downloadLoading ...

Abstract P067: Evaluating the risk of clinically significant prostate cancer in lesions detected on multiparametric MRI of the prostate

Abstract Introduction: Multiparametric Magnetic Resonance Imaging (mpMRI) is a valuable tool in the prostate cancer diagnostic pathway. mpMRI has a negative predictive value (NPV) of approximately 90% for detecting clinically significant prostate cancer (csPCa), defined as the presence of any Gleason Grade (GG) 4 or 5. Lesions detected on mpMRI are scored using the PIRADS scoring system from 1 to 5. PIRADS-3, 4 and 5 lesions are recommended for biopsy. The use of mpMRI to guide prostate biopsy has been shown to be superior to systematic biopsy alone for detecting early csPCa as small lesions can be identified and accurately targeted. We analysed the outcome of targeted biopsy of prostate lesions detected on mpMRI, and systematic biopsy when no lesions were detected, to determine the risk of csPCa based on mpMRI findings. Methods: Men attending our prostate diagnostic pathway were offered mpMRI before biopsy. The criteria for pre-biopsy mpMRI were any man suitable for radical surgery or radiotherapy, plus one or more of PSA &amp;gt; 3, palpable lesion on digital rectal examination, or family history of PCa in a first degree relative (father or brother). mpMRI was performed with a 1.5-Tesla magnet and included T2, DWI and DCE sequences. Men with PIRADS 3, 4, and 5 lesions were offered targeted biopsy with 2-4 biopsy cores per lesion, and men with no lesion on MRI were offered systematic prostate biopsy with 8-16 biopsy cores. Results: 546 targeted lesions were biopsied in 349 men. 159 lesions were PIRADS-3, 226 were PIRADS-4, and 161 were PIRADS-5. 141 men with negative mpMRI had systematic prostate biopsy. Of the PIRADS-3 lesions, 84% were benign, 5% had GG 3, 11% had GG 4 and none had GG 5. Of the PIRADS-4 lesions, 44% were benign, 12% had GG 3, 42% had GG 4, and 2% had GG 5. Of the PIRADS-5 lesions, 23% were benign, 2% had GG 3, 62% had GG 4, and 13% had GG 5. Of the men with no lesion on mpMRI, 9% had GG 3, 4% had GG 4, and none had GG 5. Conclusion: Most men with negative mpMRI or PIRADS-3 lesions have benign tissue on biopsy or early PCa and are unlikely to have csPCa. A decision for biopsy vs PSA monitoring in these men should weigh the risks and benefits of biopsy and should be a shared decision taking into consideration individual risk factors for PCa, age and co-morbidities. Citation Format: Chidi N. Molokwu, Christos Gkikas, Nicola Lee, Joanna Lesniak-Buzon, Faisal Ali, Harry Bardgett. Evaluating the risk of clinically significant prostate cancer in lesions detected on multiparametric MRI of the prostate. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P067.

3T-MRI Artificial Intelligence in Patients with Invasive Breast Cancer to Predict Distant Metastasis Status: A Pilot Study.

The incidence of breast cancer metastasis has decreased over the years. However, 20-30% of patients with early breast cancer still die from metastases. The purpose of this study is to evaluate the performance of a Deep Learning Convolutional Neural Networks (CNN) model to predict the risk of distant metastasis using 3T-MRI DCE sequences (Dynamic Contrast-Enhanced). A total of 157 breast cancer patients who underwent staging 3T-MRI examinations from January 2011 to July 2022 were retrospectively examined. Patient data, tumor histological and MRI characteristics, and clinical and imaging follow-up examinations of up to 7 years were collected. Of the 157 MRI examinations, 39/157 patients (40 lesions) had distant metastases, while 118/157 patients (120 lesions) were negative for distant metastases (control group). We analyzed the role of the Deep Learning technique using a single variable size bounding box (SVB) option and employed a Voxel Based (VB) NET CNN model. The CNN performance was evaluated in terms of accuracy, sensitivity, specificity, and area under the ROC curve (AUC). The VB-NET model obtained a sensitivity, specificity, accuracy, and AUC of 52.50%, 80.51%, 73.42%, and 68.56%, respectively. A significant correlation was found between the risk of distant metastasis and tumor size, and the expression of PgR and HER2. We demonstrated a currently insufficient ability of the Deep Learning approach in predicting a distant metastasis status in patients with BC using CNNs.

Multiparametric MRI Features of Breast Cancer Molecular Subtypes.

Background and Objectives: Breast cancer (BC) molecular subtypes have unique incidence, survival and response to therapy. There are five BC subtypes described by immunohistochemistry: luminal A, luminal B HER2 positive and HER2 negative, triple negative (TNBC) and HER2-enriched. Multiparametric breast MRI (magnetic resonance imaging) provides morphological and functional characteristics of breast tumours and is nowadays recommended in the preoperative setting. Aim: To evaluate the multiparametric MRI features (T2-WI, ADC values and DCE) of breast tumours along with breast density and background parenchymal enhancement (BPE) features among different BC molecular subtypes. Materials and Methods: This was a retrospective study which included 344 patients. All underwent multiparametric breast MRI (T2WI, ADC and DCE sequences) and features were extracted according to the latest BIRADS lexicon. The inter-reader agreement was assessed using the intraclass coefficient (ICC) between the ROI of ADC obtained from the two breast imagers (experienced and moderately experienced). Results: The study population was divided as follows: 89 (26%) with luminal A, 39 (11.5%) luminal B HER2 positive, 168 (48.5%) luminal B HER2 negative, 41 (12%) triple negative (TNBC) and 7 (2%) with HER2 enriched. Luminal A tumours were associated with special histology type, smallest tumour size and persistent kinetic curve (all p-values < 0.05). Luminal B HER2 negative tumours were associated with lowest ADC value (0.77 × 10−3 mm2/s2), which predicts the BC molecular subtype with an accuracy of 0.583. TNBC were associated with asymmetric and moderate/marked BPE, round/oval masses with circumscribed margins and rim enhancement (all p-values < 0.05). HER2 enriched BC were associated with the largest tumour size (mean 37.28 mm, p-value = 0.02). Conclusions: BC molecular subtypes can be associated with T2WI, ADC and DCE MRI features. ADC can help predict the luminal B HER2 negative cases.

Comparison and prediction of artefact severity due to total hip replacement in 1.5 T versus 3 T MRI of the prostate

PurposeTo evaluate image quality and diagnostic value of multiparametric prostate MRI (mpMRI) in patients with total hip replacement (THR) at 1.5 and 3 Tesla. MethodsIn this retrospective multicenter cohort study patients with uni- or bilateral THR and 1.5 T or 3 T mpMRI were included. Seventy consecutive, standard-of-care examinations per field strength were evaluated regarding their diagnostic value. The overall diagnostic value and prostate imaging quality score (PI-QUAL) were assessed. Artifact severity in the localizer and mpMRI sequences (T2w, DWI, DCE) was scored on a 3-point scale. Correlation between diagnostic value and artifacts was analysed. Moreover, a subgroup analysis focussed on image quality at different 3 T scanner generations. Results140 consecutive patients (mean age 72, median PSA value 8.3 ng/ml) were included. When comparing 1.5 T to 3 T examinations, no significant differences were observed regarding the artifact severity of DWI and the localizer and the overall diagnostic value of the images. There was a strong correlation between the diagnostic value, PI-QUAL score, and artifact severity in the localizer and DWI. T2w and DCE sequences showed overall low artifacts. Significant improvement in image quality for 3 T at the latest scanner generation was observed, especially for DWI (p < 0.03). ConclusionsMpMRI of patients with THR can be conducted at both field strengths without significant differences in artifacts. The localizer might be useful as an early forecasting feature for diagnostic value and particularly for contrast medium application decision. Patients with THR could benefit from technically advanced scanner generation and rs/ptx-EPI DWI sequences.

Can quantitative analysis of multi-parametric MRI independently predict failure of focal salvage HIFU therapy in men with radio-recurrent prostate cancer?

ObjectivesFocal salvage HIFU is a feasible therapeutic option in some men who have recurrence after primary radiotherapy for prostate cancer. We aimed to determine if multi-parametric quantitative parameters, in addition to clinical factors, might have a role in independently predicting focal salvage HIFU outcomes. MethodsA retrospective registry analysis included 150 consecutive men who underwent focal salvage HIFU (Sonablate500) (2006-2015); 89 had mpMRI available. Metastatic disease was excluded by nodal assessment on pelvic MRI, a radioisotope bone-scan and/or choline or FDG PET/CT scan. All men had mpMRI and either transperineal template prostate mapping biopsy or targeted and systematic TRUS-biopsy. mpMRI included T2‐weighted, diffusion‐weighted and dynamic contrast‐enhancement. Pre-HIFU quantitative mpMRI data was obtained using Horos DICOM Viewer v3.3.5 for general MRI parameters and IB DCE v2.0 plug-in. Progression-free survival (PFS) was defined by biochemical failure and/or positive localized or distant imaging results and/or positive biopsy and/or systemic therapy and/or metastases/prostate cancer‐specific death. Potential predictors of PFS were analyzed by univariable and multivariable Cox-regression. ResultsMedian age at focal salvage HIFU was 71 years (interquartile range [IQR] 65–74.5) and median PSA pre-focal salvage treatment was 5.8ng/ml (3.8-8). Median follow-up was 35 months (23-47) and median time to failure was 15 months (7.8–24.3). D-Amico low, intermediate and high-risk disease was present in 1% (1/89), 40% (36/89) and 43% (38/89) prior to focal salvage HIFU (16% missing data). 56% (50/89) failed by the composite outcome. A total of 22 factors were evaluated on univariable and 8 factors on multivariable analysis. The following quantitative parameters were included: Ktrans, Kep, Ve, Vp, IS, rTTP and TTP. On univariable analysis, PSA, prostate volume at time of radiotherapy failure and Ve (median) value were predictors for failure. Ve represents extracellular fraction of the whole tissue volume. On multivariable analysis, only Ve (median) value remained as an independent predictor. ConclusionsOne pharmacokinetic quantitative parameter based on DCE sequences seems to independently predict failure following focal salvage HIFU for radio-recurrent prostate cancer. This likely relates to the tumor microenvironment producing heat-sinks which counter the heating effect of HIFU. Further validation in larger datasets and evaluating mechanisms to reduce heat-sinks are required.

MRI features of the normal prostatic peripheral zone: the relationship between age and signal heterogeneity on T2WI, DWI, and DCE sequences

ObjectivesTo assess the multiparametric MRI (mpMRI) appearances of normal peripheral zone (PZ) across age groups in a biopsy-naïve population, where prostate cancer (PCa) was subsequently excluded, and propose a scoring system for background PZ changes.MethodsThis retrospective study included 175 consecutive biopsy-naïve patients (40–74 years) referred with a suspicion of PCa, but with subsequent negative investigations. Patients were grouped by age into categories ≤ 54, 55–59, 60–64, and ≥ 65 years. MpMRI sequences (T2-weighted imaging [T2WI], diffusion-weighted imaging [DWI]/apparent diffusion coefficient [ADC], and dynamic contrast-enhanced imaging [DCE]) were independently evaluated by two uro-radiologists on a proposed 4-point grading scale for background change on each sequence, wherein score 1 mirrored PIRADS-1 change and score 4 represented diffuse background change. Peripheral zone T2WI signal intensity and ADC values were also analyzed for trends relating to age.ResultsThere was a negative correlation between age and assigned background PZ scores for each mpMRI sequence: T2WI: r = − 0.52, DWI: r = − 0.49, DCE: r = − 0.45, p < 0.001. Patients aged ≤ 54 years had mean scores of 3.0 (T2WI), 2.7 (DWI), and 3.1 (DCE), whilst patients ≥ 65 years had significantly lower mean scores of 1.7, 1.4, and 1.9, respectively. There was moderate inter-reader agreement for all scores (range κ = 0.43–0.58). Statistically significant positive correlations were found for age versus normalized T2WI signal intensity (r = 0.2, p = 0.009) and age versus ADC values (r = 0.33, p = 0.001).ConclusionThe normal PZ in younger patients (≤ 54 years) demonstrates significantly lower T2WI signal intensity, lower ADC values, and diffuse enhancement on DCE, which may hinder diagnostic interpretation in these patients. The proposed standardized PZ background scoring system may help convey the potential for diagnostic uncertainty to clinicians.Key Points• Significant, positive correlations were found between increasing age and higher normalized T2-weighted signal intensity and mean ADC values of the prostatic peripheral zone.• Younger men exhibit lower T2-weighted imaging signal intensity, lower ADC values, and diffuse enhancement on dynamic contrast-enhanced imaging, which may hinder MRI interpretation.• A scoring system is proposed which aims towards a standardized assessment of the normal background PZ. This may help convey the potential for diagnostic uncertainty to clinicians.

How Fast Can We Go: Abbreviated Prostate MR Protocols.

Multiparametric MRI (mpMRI), composed of T2WI, DWI, and DCE sequences, is effective in identifying prostate cancer (PCa), but length and cost preclude its application as a PCa screening tool. Here we review abbreviated MRI protocols that shorten or omit conventional mpMRI components to reduce scan time and expense without forgoing diagnostic accuracy. The DCE sequence, which plays a limited diagnostic role in PI-RADS, is eliminated in variations of the biparametric MRI (bpMRI). T2WI, the lengthiest sequence, is truncated by only acquiring the axial plane or utilizing 3D acquisition with subsequent 2D reconstruction. DW-EPISMS further accelerates DWI acquisition. The fastest protocol described to date consists of just DW-EPISMS and axial-only 2D T2WI and runs less than 5min. Abbreviated protocols can mitigate scan expense and increase scan access, allowing prostate MRI to become an efficient PCa screening tool.

Automatic lesion detection, segmentation and characterization via 3D multiscale morphological sifting in breast MRI

Previous studies on computer aided detection/diagnosis (CAD) in 4D breast magnetic resonance imaging (MRI) usually regard lesion detection, segmentation and characterization as separate tasks, and typically require users to manually select 2D MRI slices or regions of interest as the input. In this work, we present a breast MRI CAD system that can handle 4D multimodal breast MRI data, and integrate lesion detection, segmentation and characterization with no user intervention. The proposed CAD system consists of three major stages: region candidate generation, feature extraction and region candidate classification. Breast lesions are firstly extracted as region candidates using the novel 3D multiscale morphological sifting (MMS). The 3D MMS, which uses linear structuring elements to extract lesion-like patterns, can segment lesions from breast images accurately and efficiently. Analytical features are then extracted from all available 4D multimodal breast MRI sequences, including T1-, T2-weighted and DCE sequences, to represent the signal intensity, texture, morphological and enhancement kinetic characteristics of the region candidates. The region candidates are lastly classified as lesion or normal tissue by the random under-sampling boost (RUSboost), and as malignant or benign lesion by the random forest. Evaluated on a breast MRI dataset which contains a total of 117 cases with 141 biopsy-proven lesions (95 malignant and 46 benign lesions), the proposed system achieves a true positive rate (TPR) of 0.90 at 3.19 false positives per patient (FPP) for lesion detection and a TPR of 0.91 at a FPP of 2.95 for identifying malignant lesions without any user intervention. The average dice similarity index (DSI) is for lesion segmentation. Compared with previously proposed lesion detection, detection-segmentation and detection-characterization systems evaluated on the same breast MRI dataset, the proposed CAD system achieves a favourable performance in breast lesion detection and characterization.

Abstract 2301: Indications for systematic prostate biopsy in men with negative multi-parametric magnetic resonance imaging

Abstract Introduction: Multiparametric Magnetic Resonance Imaging (mpMRI) is a valuable tool in the prostate cancer diagnostic pathway, and MRI-influenced biopsy has been shown to be superior to systematic biopsy alone for detecting clinically significant prostate cancer (csPCa), defined as the presence of any Gleason ≥ 4. Negative predictive value of mpMRI for csPCa varies widely in published reports, with a median of approximately 88%. Counselling patients with negative mpMRI (PIRADS score &amp;lt;3)to decide whether or not to proceed with systematic biopsy or PSA monitoring could be challenging. Several studies have shown that a PSA density (PSAD) ≥ 0.15 is an indication for systematic biopsy. We analysed the clinical, radiological and pathological features of patients with negative mpMRI but csPca on systematic biopsy to identify other criteria that may inform the need for systematic biopsy when mpMRI is negative. Methods: For 18 months between Nov 2017 and May 2019, all men on our prostate diagnostic pathway with negative mpMRI were offered systematic biopsy. The criteria for pre-biopsy mpMRI was any man suitable for radical surgery or radiotherapy, plus one or more of PSA ≥ 3, palpable lesion on digital rectal examination, or family history of PCa in a first degree relative (father or brother). mpMRI was performed with a 1.5-Tesla magnet and included T2, DWI and DCE sequences. Systematic biopsy was TRUS-guided, with 8-16 biopsy cores. Results: Of 589 mpMRI scans in 573 men, 216 (37%) had negative mpMRI. Of these, 141 (65%) proceeded with systematic biopsy. Among the men that had biopsy, Gleason 3+3 was detected in 12 men (9%) and Gleason ≥ 4 in six (4%). Important pre-biopsy features in men with Gleason ≥ 4 were PSAD ≥ 0.15 (n=3), artefact on MRI from hip joint prosthesis (n=1), and family history (n=2). There were no unusual histological variants seen on the positive biopsy cores. Conclusion: Most men with negative mpMRI can avoid prostate biopsy and be managed with PSA monitoring. Based on our experience, we recommend systematic biopsy in men with negative mpMRI who have PSAD ≥ 1.5, family history or any imaging artifact that may alter image interpretation. Citation Format: Chidi N. Molokwu, Christos Gkikas, Harry Bardgett, Nicola Lee, Faisal Ali, Rohit Chahal, Rajindra Singh. Indications for systematic prostate biopsy in men with negative multi-parametric magnetic resonance imaging [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2301.

Comparison of double inversion recovery magnetic resonance imaging (DIR-MRI) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) in detection of prostate cancer: A pilot study

IntroductionDCE-MRI is established for detecting prostate cancer (PCa). However, it requires a gadolinium contrast agent, with potential risks for patients. The application of DIR-MRI is simple and may allow cancer detection without the use of an intravenous contrast agent by differentially nullifying signal from normal and abnormal prostate tissue, creating contrast between the cancer and background normal prostate. In this pilot study we gathered data from DIR-MRI and DCE-MRI of the prostate for an equivalence trial. We also looked at how the DIR-MRI appearance varies with the aggressiveness of PCa. MethodDIR-MRI and DCE-MRI were acquired. The images were assessed by an experienced Consultant Radiologist and a novice reporter (Radiographer). The potential PCa lesions were quantified using a lesion to normal ratio (LNR). Radiological pathological correlation was made to identify the MRI lesions that represented significant PCa. A Wilcoxon sign rank was used to compare DCE-LNR and DIR-LNR for PCa containing lesions. Pearson's correlation was used to look at the relationship between DIR-LNR and PCa grade group (aggressiveness). ResultsDCE-LNR and DIR-LNR were found to be significantly different (Z = −5.910, p < 0.001). However, a significant correlation was found between PCa grade group and DIR-LNR. ConclusionDIR and DCE sequences are not equivalent and significant cancer is more conspicuous on the DCE sequence. However, DIR-LNR does correlate with PCa aggressiveness. Implications for practiceWith the correlation of PCa grade group with DIR-LNR this may be a useful sequence in evaluation of the prostate; stratifying the risk of there being clinically significant PCa before biopsy is performed. Furthermore, given that DIR-LNR appears to predict PCa aggressiveness DIR might be used as part of a multiparametric MRI protocol designed to avoid biopsy.

Breast MRI as a radiomic biomarker of immune enrichment in HER2+ breast cancer.

e14281 Background: Trastuzumab is an important drug in the treatment (tx) of HER2+ breast cancer, but not all tumors are responsive. Immune-enriched tumors have a better response to tx, and a biomarker to identify these tumors would be valuable. Multiparametric functional MRI is a non-invasive technique that can assess the entire tumor without the sampling error of biopsy. In this pilot study, we evaluate breast MRI as a potential biomarker for tumor immune response to trastuzumab. Methods: Women with de novo cT1-2N0 HER2+ breast cancer were enrolled and received paired multiparametric breast MRI examinations (including DWI and DCE sequences) prior to and ~2 weeks after a single dose of trastuzumab, followed by surgery or biopsy (if additional neoadjuvant tx planned). Tumor peak signal enhancement ratio (SER) and mean apparent diffusion coefficient (ADC), reflecting tissue perfusion and tissue cellularity, were determined from the images. Pre- and post-tx tissue were immunologically profiled with TIL counts and an immune-focused RNA gene expression panel. SER and ADC were correlated with TIL and RNA gene expression using Spearman’s rho. Results: To date, 8 women have enrolled and 6 had pre- and post-tx tissue available. The primary endpoint was correlation between SER and ADC measurements with tumor TILs. Secondary endpoints included correlating SER with angiogenesis and inflammation markers and correlating changes in SER and ADC with apoptosis/cell death markers. About half of the patients had TIL-enriched (TIL≥ 50%) tumors at baseline, and after trastuzumab the majority of tumors remained enriched or had an increase in TILs. Results suggest that pre-tx ADC had a negative correlation with TILs by histology and gene expression, although correlations did not reach statistical significance in this small cohort. Results also suggest a strong correlation between pre-tx SER and VEGF-A (r = 0.83) with a trend towards significance (P = 0.10). A decrease in SER post-tx had a strong correlation with an increase in caspase 3 expression (r = -0.85, p = 0.05), which suggested tx effect. Conclusions: Functional MRI may provide a novel biomarker of breast tumor immune response to trastuzumab. Results from additional patients will be available at the meeting.

Correlation between apparent diffusion coefficient of magnetic resonance imaging and tumor-infiltrating lymphocytes in breast cancer.

To evaluate a possible correlation between apparent diffusion coefficient (ADC) value and tumor-infiltrating lymphocytes (TILs) level in breast cancer (BC). A second objective was to assess whether there were other histopathologic features that could affect mean ADC value. In this 4-year retrospective study were included 125 patients who underwent radical or modified mastectomy for monofocal BC. All subjects had performed preoperative MRI with the same 1.5-T machine and protocol, which consisted of STIR, DWI and DCE sequences. Based on TIL score, BCs were stratified into two groups: absent-low TIL (≤ 10%) and medium-high TIL (> 10%). The t test was used to correlate mean ADC value with TIL groups. Receiver operating characteristic curve and Youden index were used in order to identify ADC value threshold to distinguish the two TIL groups. BC patients with absent-low TIL level and medium-high TIL one were, respectively, 66 (52.8%) and 59 (47.2%). Mean ADC value was 1.05 ± 0.19*10-3mm2s-1. Absent-low TIL group showed a lower mean ADC value than medium-high TIL one (0.96 ± 0.18*10-3mm2s-1 vs 1.14 ± 0.16*10-3mm2s-1; p < 0.0001). ADC value threshold in order to distinguish the two TIL groups with maximum sensitivity (67.8%) and specificity (80.3%) was 1.03*10-3 mm2s-1. ADC value was shown to be significantly related to TILs level (p < 0.0001) and cancer histotype (p = 0.0006), with a lower mean ADC value correlated to absent-low TIL level and ductal histotype. BCs with absent-low TIL showed a statistically significant lower mean ADC value than those with medium-high TIL. ADC value threshold in order to distinguish these two groups was 1.03*10-3mm2s-1.