Purpose Standard imaging for assessing osseous metastases in advanced prostate cancer remains focused on altered bone metabolism and is inadequate for diagnostic, prognostic, or predictive purposes. We performed a first-in-human phase I/II study of 89Zr-DFO-huJ591 (89Zr-J591) PET/CT immunoscintigraphy to assess performance characteristics for detecting metastases compared to conventional imaging modalities (CIMs) and pathology. Experimental Design Fifty patients with progressive metastatic castration-resistant prostate cancers were injected with 5 mCi of 89Zr-J591. Whole body PET/CT scans were obtained, and images were analyzed for tumor visualization. Comparison was made to contemporaneously obtained bone scintigraphy and cross-sectional imaging on a lesion-by-lesion basis, and with biopsies of metastatic sites. Results Median standardized uptake value for 89Zr-J591-positive bone lesions (n = 491) was 8.9; soft tissue lesions (n = 90): 4.8 (p < .00003). 89Zr-J591 detected 491 osseous sites compared to 339 by MDP, and 90 soft tissue lesions compared to 124 by CT. Compared to all CIMs combined, 89Zr-J591 detected an additional 99 osseous sites. Forty-six lesions (21 bone, 25 soft tissue) were biopsied in 34 patients; 18/19 89Zr-J591-positive osseous sites and 14/16 89Zr-J591-positive soft tissue sites were positive for prostate cancer. The overall accuracy of 89Zr-J591 was 95.2% (20/21) for osseous lesions and 60% (15/25) for soft tissue lesions. Conclusions 89Zr-J591 imaging demonstrated superior targeting of bone lesions relative to CIMs. Targeting soft tissue lesions was less optimal, although 89Zr-J591 had similar accuracy as individual CIMs. This study will provide benchmark data for comparing performance of proposed PSMA targeting agents for prostate cancer.

Background To evaluate the diagnostic performance of radiomic signatures extracted from contrast-enhanced magnetic resonance imaging (CE-MRI) for the assessment of breast cancer receptor status and molecular subtypes. Methods One hundred and forty-three patients with biopsy-proven breast cancer who underwent CE-MRI at 3 T were included in this IRB-approved HIPAA-compliant retrospective study. The training dataset comprised 91 patients (luminal A, n = 49; luminal B, n = 8; HER2-enriched, n = 11; triple negative, n = 23), while the validation dataset comprised 52 patients from a second institution (luminal A, n = 17; luminal B, n = 17; triple negative, n = 18). Radiomic analysis of manually segmented tumors included calculation of features derived from the first-order histogram (HIS), co-occurrence matrix (COM), run-length matrix (RLM), absolute gradient (GRA), autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry (GEO). Fisher, probability of error and average correlation (POE + ACC), and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification (with leave-one-out cross-validation) was used for pairwise radiomic-based separation of receptor status and molecular subtypes. Histopathology served as the standard of reference. Results In the training dataset, radiomic signatures yielded the following accuracies > 80%: luminal B vs. luminal A, 84.2% (mainly based on COM features); luminal B vs. triple negative, 83.9% (mainly based on GEO features); luminal B vs. all others, 89% (mainly based on COM features); and HER2-enriched vs. all others, 81.3% (mainly based on COM features). Radiomic signatures were successfully validated in the separate validation dataset for luminal A vs. luminal B (79.4%) and luminal B vs. triple negative (77.1%). Conclusions In this preliminary study, radiomic signatures with CE-MRI enable the assessment of breast cancer receptor status and molecular subtypes with high diagnostic accuracy. These results need to be confirmed in future larger studies.

We conducted a phase I dose-escalation study with 89 Zr-desferrioxamine-IAB2M ( 89 Zr-IAB2M), an anti-prostate-specific membrane antigen minibody, in patients with metastatic prostate cancer. Methods: Patients received 185 MBq (5 mCi) of 89 Zr-IAB2M and Df-IAB2M at total mass doses of 10 (n 5 6), 20 (n 5 6), and 50 mg (n 5 6). Wholebody and serum clearance, normal-organ and lesion uptake, and radiation absorbed dose were estimated, and the effect of mass escalation was analyzed. Results: Eighteen patients were injected and scanned without side effects. Whole-body clearance was monoexponential, with a median biologic half-life of 215 h, whereas serum clearance showed biexponential kinetics, with a median biologic half-life of 3.7 (12.3%/L) and 33.8 h (17.9%/L). The radiation absorbed dose estimates were 1.67, 1.36, and 0.32 mGy/MBq to liver, kidney, and marrow, respectively, with an effective dose of 0.41 mSv/MBq (1.5 rem/mCi). Both skeletal and nodal lesions were detected with 89 Zr-IAB2M, most visualized by 48-h imaging. Conclusion: 89 Zr-IAB2M is safe and demonstrates favorable biodistribution and kinetics for targeting metastatic prostate cancer. Imaging with 10 mg of minibody mass provides optimal biodistribution, and imaging at 48 h after injection provides good lesion visualization. Assessment of lesion targeting is being studied in detail in an expansion cohort.

We evaluated the performance of radiomics and artificial intelligence (AI) from multiparametric magnetic resonance imaging (MRI) for the assessment of breast cancer molecular subtypes. Ninety-one breast cancer patients who underwent 3T dynamic contrast-enhanced (DCE) MRI and diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping were included retrospectively. Radiomic features were extracted from manually drawn regions of interest (n = 704 features per lesion) on initial DCE-MRI and ADC maps. The ten best features for subtype separation were selected using probability of error and average correlation coefficients. For pairwise comparisons with >20 patients in each group, a multi-layer perceptron feed-forward artificial neural network (MLP-ANN) was used (70% of cases for training, 30%, for validation, five times each). For all other separations, linear discriminant analysis (LDA) and leave-one-out cross-validation were applied. Histopathology served as the reference standard. MLP-ANN yielded an overall median area under the receiver-operating-characteristic curve (AUC) of 0.86 (0.77–0.92) for the separation of triple negative (TN) from other cancers. The separation of luminal A and TN cancers yielded an overall median AUC of 0.8 (0.75–0.83). Radiomics and AI from multiparametric MRI may aid in the non-invasive differentiation of TN and luminal A breast cancers from other subtypes.

Purpose: To compare annotation segmentation approaches and to assess the value of radiomics analysis applied to diffusion-weighted imaging (DWI) for evaluation of breast cancer receptor status and molecular subtyping. Procedures: In this IRB-approved HIPAA-compliant retrospective study, 91 patients with treatment-naïve breast malignancies proven by image-guided breast biopsy, (luminal A, n = 49; luminal B, n = 8; human epidermal growth factor receptor 2 [HER2]-enriched, n = 11; triple negative [TN], n = 23) underwent multiparametric magnetic resonance imaging (MRI) of the breast at 3 T with dynamic contrast-enhanced MRI, T2-weighted and DW imaging. Lesions were manually segmented on high b-value DW images and segmentation ROIS were propagated to apparent diffusion coefficient (ADC) maps. In addition in a subgroup (n = 79) where lesions were discernable on ADC maps alone, these were also directly segmented there. To derive radiomics signatures, the following features were extracted and analyzed: first-order histogram (HIS), cooccurrence matrix (COM), run-length matrix (RLM), absolute gradient, autoregressive model (ARM), discrete Haar wavelet transform (WAV), and lesion geometry. Fisher, probability of error and average correlation, and mutual information coefficients were used for feature selection. Linear discriminant analysis followed by k-nearest neighbor classification with leave-one-out cross-validation was applied for pairwise differentiation of receptor status and molecular Sunitha B. Thakur and Katja Pinker contributed equally to this work.

Background: For breast cancer patients undergoing neoadjuvant chemotherapy (NAC), pathologic complete response (pCR; no invasive or in situ) cannot be assessed non-invasively so all patients undergo surgery. The aim of our study was to develop and validate a radiomics classifier that classifies breast cancer pCR post-NAC on MRI prior to surgery. Methods: This retrospective study included women treated with NAC for breast cancer from 2014 to 2016 with (1) pre-and post-NAC breast MRI and (2) post-NAC surgical pathology report assessing response. Automated radiomics analysis of pre-and post-NAC breast MRI involved image segmentation, radiomics feature extraction, feature prefiltering, and classifier building through recursive feature elimination random forest (RFE-RF) machine learning. The RFE-RF classifier was trained with nested five-fold cross-validation using (a) radiomics only (model 1) and (b) radiomics and molecular subtype (model 2). Class imbalance was addressed using the synthetic minority oversampling technique. Results: Two hundred seventy-three women with 278 invasive breast cancers were included; the training set consisted of 222 cancers (61 pCR, 161 no-pCR; mean age 51.8 years, SD 11.8), and the independent test set consisted of 56 cancers (13 pCR, 43 no-pCR; mean age 51.3 years, SD 11.8). There was no significant difference in pCR or molecular subtype between the training and test sets. Model 1 achieved a cross-validation AUROC of 0.72 (95% CI 0.64, 0.79) and a similarly accurate (P = 0.1) AUROC of 0.83 (95% CI 0.71, 0.94) in both the training and test sets. Model 2 achieved a cross-validation AUROC of 0.80 (95% CI 0.72, 0.87) and a similar (P = 0.9) AUROC of 0.78 (95% CI 0.62, 0.94) in both the training and test sets. Conclusions: This study validated a radiomics classifier combining radiomics with molecular subtypes that accurately classifies pCR on MRI post-NAC.

Background Small breast lesions are difficult to visually categorize due to the inherent lack of morphological and kinetic detail. Purpose To assess the efficacy of radiomics analysis in discriminating small benign and malignant lesions utilizing model free parameter maps. Study Type Retrospective, single center. Population In all, 149 patients, with a total of 165 lesions scored as BI‐RADS 4 or 5 on MRI, with an enhancing volume of <0.52 cm3. Field Strength/Sequence Higher spatial resolution T1‐weighted dynamic contrast‐enhanced imaging with a temporal resolution of ~90 seconds performed at 3.0T. Assessment Parameter maps reflecting initial enhancement, overall enhancement, area under the enhancement curve, and washout were generated. Heterogeneity measures based on first‐order statistics, gray level co‐occurrence matrices, run length matrices, size zone matrices, and neighborhood gray tone difference matrices were calculated. Data were split into a training dataset (~75% of cases) and a test dataset (~25% of cases). Statistical Tests Comparison of medians was assessed using the nonparametric Mann–Whitney U‐test. The Spearman rank correlation coefficient was utilized to determine significant correlations between individual features. Finally, a support vector machine was employed to build multiparametric predictive models. Results Univariate analysis revealed significant differences between benign and malignant lesions for 58/133 calculated features (P < 0.05). Support vector machine analysis resulted in areas under the curve (AUCs) ranging from 0.75–0.81. High negative (>89%) and positive predictive values (>83%) were found for all models. Data Conclusion Radiomics analysis of small contrast‐enhancing breast lesions is of value. Texture features calculated from later timepoints on the enhancement curve appear to offer limited additional value when compared with features determined from initial enhancement for this patient cohort. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1468–1477.

BackgroundAvailable data proving the value of DWI for breast cancer diagnosis is mainly for enhancing masses; DWI may be less sensitive and specific in non-mass enhancement (NME) lesions. The objective of this study was to assess the diagnostic accuracy of DWI using different ROI measurement approaches and ADC metrics in breast lesions presenting as NME lesions on dynamic contrast-enhanced (DCE) MRI.MethodsIn this retrospective study, 95 patients who underwent multiparametric MRI with DCE and DWI from September 2007 to July 2013 and who were diagnosed with a suspicious NME (BI-RADS 4/5) were included. Twenty-nine patients were excluded for lesion non-visibility on DWI (n = 24: 12 benign and 12 malignant) and poor DWI quality (n = 5: 1 benign and 4 malignant). Two readers independently assessed DWI and DCE-MRI findings in two separate randomized readings using different ADC metrics and ROI approaches. NME lesions were classified as either benign (> 1.3 × 10−3 mm2/s) or malignant (≤ 1.3 × 10−3 mm2/s). Histopathology was the standard of reference. ROC curves were plotted, and AUCs were determined. Concordance correlation coefficient (CCC) was measured.ResultsThere were 39 malignant (59%) and 27 benign (41%) lesions in 66 (65 women, 1 man) patients (mean age, 51.8 years). The mean ADC value of the darkest part of the tumor (Dptu) achieved the highest diagnostic accuracy, with AUCs of up to 0.71. Inter-reader agreement was highest with Dptu ADC max (CCC 0.42) and lowest with the point tumor (Ptu) ADC min (CCC = − 0.01). Intra-reader agreement was highest with Wtu ADC mean (CCC = 0.44 for reader 1, 0.41 for reader 2), but this was not associated with the highest diagnostic accuracy.ConclusionsDiagnostic accuracy of DWI with ADC mapping is limited in NME lesions. Thirty-one percent of lesions presenting as NME on DCE-MRI could not be evaluated with DWI, and therefore, DCE-MRI remains indispensable. Best results were achieved using Dptu 2D ROI measurement and ADC mean.