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Supplementary Table S1. Patient demographics

Supplementary Figures S1-4. Figure S1. 89Zr-J591 versus bone scan comparison of lesion positivity. Figure S2. Patient with metastatic prostate cancer (PSA of 10.1) with 89Zr-J591+ lesions that were not seen on MDP. Figure S3. Patient with metastatic prostate cancer (PSA of 0.62). Figure S4. Uniform prior distribution.

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 with conventional imaging modalities (CIM) 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 and for soft-tissue lesions (n = 90), it was 4.8 (P < 0.00003). 89Zr-J591 detected 491 osseous sites compared with 339 by MDP and 90 soft-tissue lesions compared with 124 by computed tomography (CT). Compared with all CIMs combined, 89Zr-J591 detected an additional 99 osseous sites. Forty-six lesions (21 bone and 25 soft tissue) were biopsied in 34 patients; 18 of 19 89Zr-J591–positive osseous sites and 14 of 16 89Zr-J591–positive soft tissue sites were positive for prostate cancer. The overall accuracy of 89Zr-J591 was 95.2% (20 of 21) for osseous lesions and 60% (15 of 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 prostate-specific membrane antigen (PSMA) targeting agents for prostate cancer. Clin Cancer Res; 21(23); 5277–85. ©2015 AACR.

Supplementary Appendix

Purpose To investigate whether radiomics features extracted from magnetic resonance imaging (MRI) of patients with biopsy-proven atypical ductal hyperplasia (ADH) coupled with machine learning can differentiate high-risk lesions that will upgrade to malignancy at surgery from those that will not, and to determine if qualitatively and semi-quantitatively assessed imaging features, clinical factors, and image-guided biopsy technical factors are associated with upgrade rate. Methods This retrospective study included 127 patients with 139 breast lesions yielding ADH at biopsy who were assessed with multiparametric MRI prior to biopsy. Two radiologists assessed all lesions independently and with a third reader in consensus according to the BI-RADS lexicon. Univariate analysis and multivariate modeling were performed to identify significant radiomic features to be included in a machine learning model to discriminate between lesions that upgraded to malignancy on surgery from those that did not. Results Of 139 lesions, 28 were upgraded to malignancy at surgery, while 111 were not upgraded. Diagnostic accuracy was 53.6%, specificity 79.2%, and sensitivity 15.3% for the model developed from pre-contrast features, and 60.7%, 86%, and 22.8% for the model developed from delta radiomics datasets. No significant associations were found between any radiologist-assessed lesion parameters and upgrade status. There was a significant correlation between the number of specimens sampled during biopsy and upgrade status (p = 0.003). Conclusion Radiomics analysis coupled with machine learning did not predict upgrade status of ADH. The only significant result from this analysis is between the number of specimens sampled during biopsy procedure and upgrade status at surgery.

To assess whether no enhancement on pre-treatment MRI can rule out malignancy of additional US mass(es) initially assessed as BI-RADS 3 or 4 in women with newly diagnosed breast cancer. This retrospective study included consecutive women from 2010–2018 with newly diagnosed breast cancer; at least one additional breast mass (distinct from index cancer) assigned a BI-RADS 3 or 4 on US; and a bilateral contrast-enhanced breast MRI performed within 90 days of US. All malignant masses were pathologically proven; benign masses were pathologically proven or defined as showing at least 2 years of imaging stability. Incidence of malignant masses and NPV were calculated on a per-patient level using proportions and exact 95% CIs. In 230 patients with 309 additional masses, 140/309 (45%) masses did not enhance while 169/309 (55%) enhanced on MRI. Of the 140 masses seen in 105 women (mean age, 54 years; range 28–82) with no enhancement on MRI, all had adequate follow-up and 140/140 (100%) were benign, of which 89/140 (63.6%) were pathologically proven and 51/140 (36.4%) demonstrated at least 2 years of imaging stability. Pre-treatment MRI demonstrating no enhancement of US mass correlate(s) had an NPV of 100% (95% CI 96.7–100.0). All BI-RADS 3 and 4 US masses with a non-enhancing correlate on pre-treatment MRI were benign. The incorporation of MRI, when ordered by the referring physician, may decrease unnecessary follow-up imaging and/or biopsy if the initial US BI-RADS assessment and management recommendation were to be retrospectively updated. • Of 309 BI-RADS 3 or 4 US masses with a corresponding mass on MRI, 140/309 (45%) demonstrated no enhancement whereas 169/309 (55%) demonstrated enhancement • All masses classified as BI-RADS 3 or 4 on US without enhancement on MRI were benign • MRI can rule out malignancy in non-enhancing US masses with an NPV of 100%

Background To investigate if baseline and/or changes in contralateral background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) measured on magnetic resonance imaging (MRI) and mammographic breast density (MD) can be used as imaging biomarkers for overall and recurrence-free survival in patients with invasive lobular carcinomas (ILCs) undergoing adjuvant endocrine treatment. Methods Women who fulfilled the following inclusion criteria were included in this retrospective HIPAA-compliant IRB-approved study: unilateral ILC, pre-treatment breast MRI and/or mammography from 2000 to 2010, adjuvant endocrine treatment, follow-up MRI, and/or mammography 1–2 years after treatment onset. BPE, FGT, and mammographic MD of the contralateral breast were independently graded by four dedicated breast radiologists according to BI-RADS. Associations between the baseline levels and change in levels of BPE, FGT, and MD with overall survival and recurrence-free survival were assessed using Kaplan–Meier survival curves and Cox regression analysis. Results Two hundred ninety-eight patients (average age = 54.1 years, range = 31–79) fulfilled the inclusion criteria. The average follow-up duration was 11.8 years (range = 2–19). Baseline and change in levels of BPE, FGT, and MD were not significantly associated with recurrence-free or overall survival. Recurrence-free and overall survival were affected by histological subtype (p p p = 0.01), and adjuvant endocrine treatment duration (p Conclusions Qualitative evaluation of BPE, FGT, and mammographic MD changes cannot predict which patients are more likely to benefit from adjuvant endocrine treatment.

ObjectivesTo assess DWI for tumor visibility and breast cancer detection by the addition of different synthetic b-values.MethodsEighty-four consecutive women who underwent a breast-multiparametric-MRI (mpMRI) with enhancing lesions on DCE-MRI (BI-RADS 2–5) were included in this IRB-approved retrospective study from September 2018 to March 2019. Three readers evaluated DW acquired b-800 and synthetic b-1000, b-1200, b-1500, and b-1800 s/mm2images for lesion visibility and preferred b-value based on lesion conspicuity. Image quality (1–3 scores) and breast composition (BI-RADS) were also recorded. Diagnostic parameters for DWI were determined using a 1–5 malignancy score based on qualitative imaging parameters (acquired + preferred synthetic b-values) and ADC values. BI-RADS classification was used for DCE-MRI and quantitative ADC values + BI-RADS were used for mpMRI.ResultsSixty-four malignant (average = 23 mm) and 39 benign (average = 8 mm) lesions were found in 80 women. Although b-800 achieved the best image quality score, synthetic b-values 1200–1500 s/mm2were preferred for lesion conspicuity, especially in dense breast. b-800 and synthetic b-1000/b-1200 s/mm2values allowed the visualization of 84–90% of cancers visible with DCE-MRI performing better than b-1500/b-1800 s/mm2. DWI was more specific (86.3% vs 65.7%,p< 0.001) but less sensitive (62.8% vs 90%,p< 0.001) and accurate (71% vs 80.7%,p= 0.003) than DCE-MRI for breast cancer detection, where mpMRI was the most accurate modality accounting for less false positive cases.ConclusionThe addition of synthetic b-values enhances tumor conspicuity and could potentially improve tumor visualization particularly in dense breast. However, its supportive role for DWI breast cancer detection is still not definite.Key Points•The addition of synthetic b-values (1200–1500 s/mm2) to acquired DWI afforded a better lesion conspicuity without increasing acquisition time and was particularly useful in dense breasts.•Despite the use of synthetic b-values, DWI was less sensitive and accurate than DCE-MRI for breast cancer detection.•A multiparametric MRI modality still remains the best approach having the highest accuracy for breast cancer detection and thus reducing the number of unnecessary biopsies.

Objectives To investigate whether radiomics features extracted from MRI of BRCA-positive patients with sub-centimeter breast masses can be coupled with machine learning to differentiate benign from malignant lesions using model-free parameter maps. Methods In this retrospective study, BRCA-positive patients who had an MRI from November 2013 to February 2019 that led to a biopsy (BI-RADS 4) or imaging follow-up (BI-RADS 3) for sub-centimeter lesions were included. Two radiologists assessed all lesions independently and in consensus according to BI-RADS. Radiomics features were calculated using open-source CERR software. Univariate analysis and multivariate modeling were performed to identify significant radiomics features and clinical factors to be included in a machine learning model to differentiate malignant from benign lesions. Results Ninety-six BRCA mutation carriers (mean age at biopsy = 45.5 ± 13.5 years) were included. Consensus BI-RADS classification assessment achieved a diagnostic accuracy of 53.4%, sensitivity of 75% (30/40), specificity of 42.1% (32/76), PPV of 40.5% (30/74), and NPV of 76.2% (32/42). The machine learning model combining five parameters (age, lesion location, GLCM-based correlation from the pre-contrast phase, first-order coefficient of variation from the 1st post-contrast phase, and SZM-based gray level variance from the 1st post-contrast phase) achieved a diagnostic accuracy of 81.5%, sensitivity of 63.2% (24/38), specificity of 91.4% (64/70), PPV of 80.0% (24/30), and NPV of 82.1% (64/78). Conclusions Radiomics analysis coupled with machine learning improves the diagnostic accuracy of MRI in characterizing sub-centimeter breast masses as benign or malignant compared with qualitative morphological assessment with BI-RADS classification alone in BRCA mutation carriers. Key Points • Radiomics and machine learning can help differentiate benign from malignant breast masses even if the masses are small and morphological features are benign. • Radiomics and machine learning analysis showed improved diagnostic accuracy, specificity, PPV, and NPV compared with qualitative morphological assessment alone.

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 pre-filtering, 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 parenchymal enhancement (BPE) is assessed on breast MRI reports as mandated by the Breast Imaging Reporting and Data System (BI-RADS) but is prone to inter and intrareader variation. Semiautomated and fully automated BPE assessment tools have been developed but none has surpassed radiologist BPE designations.To develop a deep learning model for automated BPE classification and to compare its performance with current standard-of-care radiology report BPE designations.Retrospective.Consecutive high-risk patients (i.e.gt;20% lifetime risk of breast cancer) who underwent contrast-enhanced screening breast MRI from October 2013 to January 2019. The study included 5224 breast MRIs, divided into 3998 training, 444 validation, and 782 testing exams. On radiology reports, 1286 exams were categorized as high BPE (i.e., marked or moderate) and 3938 as low BPE (i.e., mild or minimal).A 1.5 T or 3 T system; one precontrast and three postcontrast phases of fat-saturated T1-weighted dynamic contrast-enhanced imaging.Breast MRIs were used to develop two deep learning models (Slab artificial intelligence (AI); maximum intensity projection [MIP] AI) for BPE categorization using radiology report BPE labels. Models were tested on a heldout test sets using radiology report BPE and three-reader averaged consensus as the reference standards.Model performance was assessed using receiver operating characteristic curve analysis. Associations between high BPE and BI-RADS assessments were evaluated using McNemar's chi-square test (α* = 0.025).The Slab AI model significantly outperformed the MIP AI model across the full test set (area under the curve of 0.84 vs. 0.79) using the radiology report reference standard. Using three-reader consensus BPE labels reference standard, our AI model significantly outperformed radiology report BPE labels. Finally, the AI model was significantly more likely than the radiologist to assign "high BPE" to suspicious breast MRIs and significantly less likely than the radiologist to assign "high BPE" to negative breast MRIs.Fully automated BPE assessments for breast MRIs could be more accurate than BPE assessments from radiology reports.4 TECHNICAL EFFICACY STAGE: 3.

Purpose: To develop a deep network architecture that would achieve fully automated radiologist-level segmentation of cancers at breast MRI. Materials and Methods: In this retrospective study, 38229 examinations (composed of 64063 individual breast scans from 14475 patients) were performed in female patients (age range, 12-94 years; mean age, 52 years +/- 10 [standard deviation]) who presented between 2002 and 2014 at a single clinical site. A total of 2555 breast cancers were selected that had been segmented on two-dimensional (2D) images by radiologists, as well as 60108 benign breasts that served as examples of noncancerous tissue; all these were used for model training. For testing, an additional 250 breast cancers were segmented independently on 2D images by four radiologists. Authors selected among several three-dimensional (3D) deep convolutional neural network architectures, input modalities, and harmonization methods. The outcome measure was the Dice score for 2D segmentation, which was compared between the network and radiologists by using the Wilcoxon signed rank test and the two one-sided test procedure. Results: The highest-performing network on the training set was a 3D U-Net with dynamic contrast-enhanced MRI as input and with intensity normalized for each examination. In the test set, the median Dice score of this network was 0.77 (interquartile range, 0.26). The performance of the network was equivalent to that of the radiologists (two one-sided test procedures with radiologist performance of 0.69-0.84 as equivalence bounds, P

Simple Summary To our knowledge, this is the first study assessing radiomics coupled with machine learning from MRI-derived features to predict PD-L1 expression status in biopsy-proven triple negative breast cancers and comparing the performance of this approach with the performance of qualitative assessment by two radiologists. This pilot study shows that radiomics analysis coupled with machine learning of DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment. This technique could also be used to monitor PD-L1 expression, as it can vary over time and between different regions of the tumor, thus avoiding repeated biopsies. Abstract The purpose of this retrospective study was to assess whether radiomics analysis coupled with machine learning (ML) based on standard-of-care dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict PD-L1 expression status in patients with triple negative breast cancer, and to compare the performance of this approach with radiologist review. Patients with biopsy-proven triple negative breast cancer who underwent pre-treatment breast MRI and whose PD-L1 status was available were included. Following 3D tumor segmentation and extraction of radiomic features, radiomic features with significant differences between PD-L1+ and PD-L1− patients were determined, and a final predictive model to predict PD-L1 status was developed using a coarse decision tree and five-fold cross-validation. Separately, all lesions were qualitatively assessed by two radiologists independently according to the BI-RADS lexicon. Of 62 women (mean age 47, range 31–81), 27 had PD-L1− tumors and 35 had PD-L1+ tumors. The final radiomics model to predict PD-L1 status utilized three MRI parameters, i.e., variance (FO), run length variance (RLM), and large zone low grey level emphasis (LZLGLE), for a sensitivity of 90.7%, specificity of 85.1%, and diagnostic accuracy of 88.2%. There were no significant associations between qualitative assessed DCE-MRI imaging features and PD-L1 status. Thus, radiomics analysis coupled with ML based on standard-of-care DCE-MRI is a promising approach to derive prognostic and predictive information and to select patients who could benefit from anti-PD-1/PD-L1 treatment.

This study aims to evaluate ultrafast DCE-MRI-derived kinetic parameters that reflect contrast agent inflow effects in differentiating between subcentimeter BI-RADS 4–5 breast carcinomas and benign lesions. We retrospectively reviewed consecutive 3-T MRI performed from February to October 2017, during which ultrafast DCE-MRI was performed as part of a hybrid clinical protocol with conventional DCE-MRI. In total, 301 female patients with 369 biopsy-proven breast lesions were included. Ultrafast DCE-MRI was acquired continuously over approximately 60 s (temporal resolution, 2.7–7.1 s/phase) starting simultaneously with the start of contrast injection. Four ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS], contrast enhancement ratio [CER], bolus arrival time [BAT], and initial area under gadolinium contrast agent concentration [IAUGC]) and one conventional DCE-MRI-derived kinetic parameter (signal enhancement ratio [SER]) were calculated for each lesion. Wilcoxon rank sum test or Fisher’s exact test was performed to compare kinetic parameters, volume, diameter, age, and BI-RADS morphological descriptors between subcentimeter carcinomas and benign lesions. Univariate/multivariate logistic regression analyses were performed to determine predictive parameters for subcentimeter carcinomas. In total, 125 lesions (26 carcinomas and 99 benign lesions) were identified as BI-RADS 4–5 subcentimeter lesions. Subcentimeter carcinomas demonstrated significantly larger MS and SER and shorter BAT than benign lesions (p = 0.0117, 0.0046, and 0.0102, respectively). MS, BAT, and age were determined as significantly predictive for subcentimeter carcinoma (p = 0.0208, 0.0023, and

To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P

Our study aimed to evaluate if breast‐conserving surgery and adjuvant treatment could affect the morphological features of axillary and intramammary lymph nodes on magnetic resonance imaging (MRI) in patients with invasive breast cancer and clinically negative axilla. In this single‐center study, we retrospectively evaluated 50 patients who had (a) breast‐conserving surgery, (b) clinically negative axilla, (c) preoperative MRI within 3 months before surgery, and (d) postoperative MRI within 12 months after surgery. Axillary and intramammary lymph nodes on postoperative MRI were identified and then compared with preoperative MRI by two breast radiologists with regards to the following: enlargement, cortical thickening, presence of fatty hilum, irregularity, heterogeneity, matting, and axillary lymph node asymmetry. Three hundred and two axillary and eight intramammary lymph nodes were evaluated. Enlargement and cortical thickening were seen in 5/50 (10%) patients in three axillary and two intramammary lymph nodes. None of the lymph nodes on postoperative MRI demonstrated occurrence of lack of fatty hilum, irregularity, heterogeneity, matting or axillary lymph node asymmetry. No evidence of recurrence was observed on 2‐year follow‐up. Lymph node enlargement and cortical thickening may be observed in a few patients in the postoperative period. Nevertheless, in patients with clinically negative axilla, these changes in morphology are often related to treatment rather than malignancy and favor short‐term follow‐up as an alternative to lymph node biopsy.

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), co-occurrence 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 subtyping. Histopathologic results were considered the gold standard. Results For lesion that were segmented on DWI and segmentation ROIs were propagated to ADC maps the following classification accuracies > 90% were obtained: luminal B vs. HER2-enriched, 94.7 % (based on COM features); luminal B vs. others, 92.3 % (COM, HIS); and HER2-enriched vs. others, 90.1 % (RLM, COM). For lesions that were segmented directly on ADC maps, better results were achieved yielding the following classification accuracies: luminal B vs. HER2-enriched, 100 % (COM, WAV); luminal A vs. luminal B, 91.5 % (COM, WAV); and luminal B vs. others, 91.1 % (WAV, ARM, COM). Conclusions Radiomic signatures from DWI with ADC mapping allows evaluation of breast cancer receptor status and molecular subtyping with high diagnostic accuracy. Better classification accuracies were obtained when breast tumor segmentations could be performed on ADC maps.

Key Points Question Is the accuracy of magnetic resonance imaging (MRI)–guided biopsy comparable with reference-standard surgical resection for diagnosing pathologic complete response after neoadjuvant chemotherapy in patients with breast cancer? Findings In this pilot nonrandomized controlled trial of 20 patients with evaluable data, the accuracy of MRI-guided biopsy for diagnosing pathologic complete response after neoadjuvant chemotherapy was 95% and the negative predictive value was 92.8%. Meaning The results from this pilot study suggest greater accuracy with this method than do the majority of published data and support the need for a larger study comparing MRI-guided biopsy with reference-standard surgical resection in diagnosing a pathologic complete response after neoadjuvant chemotherapy., This nonrandomized controlled trial uses data from a single tertiary care center in the US to investigate the accuracy of magnetic resonance imaging (MRI)–guided biopsy compared with surgical resection for assessing pathologic complete response after neoadjuvant chemotherapy in patients with breast cancer., Importance After neoadjuvant chemotherapy (NAC), pathologic complete response (pCR) is an optimal outcome and a surrogate end point for improved disease-free and overall survival. To date, surgical resection remains the only reliable method for diagnosing pCR. Objective To evaluate the accuracy of magnetic resonance imaging (MRI)–guided biopsy for diagnosing a pCR after NAC compared with reference-standard surgical resection. Design, Setting, and Participants Single-arm, phase 1, nonrandomized controlled trial in a single tertiary care cancer center from September 26, 2017, to July 29, 2019. The median follow-up was 1.26 years (interquartile range, 0.85-1.59 years). Data analysis was performed in November 2019. Eligible patients had (1) stage IA to IIIC biopsy-proven operable invasive breast cancer; (2) standard-of-care NAC; (3) MRI before and after NAC, with imaging complete response defined as no residual enhancement on post-NAC MRI; and (4) definitive surgery. Patients were excluded if they were younger than 18 years, had a medical reason precluding study participation, or had a prior history of breast cancer. Interventions Post-NAC MRI-guided biopsy without the use of intravenous contrast of the tumor bed before definitive surgery. Main Outcomes and Measures The primary end point was the negative predictive value of MRI-guided biopsy, with true-negative defined as negative results of the biopsy (ie, no residual cancer) corresponding to a surgical pCR. Accuracy, sensitivity, positive predictive value, and specificity were also calculated. Two clinical definitions of pCR were independently evaluated: definition 1 was no residual invasive cancer; definition 2, no residual invasive or in situ cancer. Results Twenty of 23 patients (87%) had evaluable data (median [interquartile range] age, 51.5 [39.0-57.5] years; 20 women [100%]; 13 White patients [65%]). Of the 20 patients, pre-NAC median tumor size on MRI was 3.0 cm (interquartile range, 2.0-5.0 cm). Nineteen of 20 patients (95%) had invasive ductal carcinoma; 15 of 20 (75%) had stage II cancer; 11 of 20 (55%) had ERBB2 (formerly HER2 or HER2/neu)–positive cancer; and 6 of 20 (30%) had triple-negative cancer. Surgical pathology demonstrated a pCR in 13 of 20 (65%) patients and no pCR in 7 of 20 patients (35%) when pCR definition 1 was used. Results of MRI-guided biopsy had a negative predictive value of 92.8% (95% CI, 66.2%-99.8%), with accuracy of 95% (95% CI, 75.1%-99.9%), sensitivity of 85.8% (95% CI, 42.0%-99.6%), positive predictive value of 100%, and specificity of 100% for pCR definition 1. Only 1 patient had a false-negative MRI-guided biopsy result (surgical pathology showed

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 &gt, 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&ndash, 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&ndash, 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.

Background Ultrafast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived kinetic parameters have demonstrated at least equivalent accuracy to standard DCE-MRI in differentiating malignant from benign breast lesions. However, it is unclear if they have any efficacy as prognostic imaging markers. The aim of this study was to investigate the relationship between ultrafast DCE-MRI-derived kinetic parameters and breast cancer characteristics. Methods Consecutive breast MRI examinations between February 2017 and January 2018 were retrospectively reviewed to determine those examinations that meet the following inclusion criteria: (1) BI-RADS 4–6 MRI performed on a 3T scanner with a 16-channel breast coil and (2) a hybrid clinical protocol with 15 phases of ultrafast DCE-MRI (temporal resolution of 2.7–4.6 s) followed by early and delayed phases of standard DCE-MRI. The study included 125 examinations with 142 biopsy-proven breast cancer lesions. Ultrafast DCE-MRI-derived kinetic parameters (maximum slope [MS] and bolus arrival time [BAT]) were calculated for the entire volume of each lesion. Comparisons of these parameters between different cancer characteristics were made using generalized estimating equations, accounting for the presence of multiple lesions per patient. All comparisons were exploratory and adjustment for multiple comparisons was not performed; P values Results Significantly larger MS and shorter BAT were observed for invasive carcinoma than ductal carcinoma in situ (DCIS) (P P = 0.008, respectively). Significantly shorter BAT was observed for invasive carcinomas with more aggressive characteristics than those with less aggressive characteristics: grade 3 vs. grades 1–2 (P = 0.025), invasive ductal carcinoma vs. invasive lobular carcinoma (P = 0.002), and triple negative or HER2 type vs. luminal type (P Conclusions Ultrafast DCE-MRI-derived parameters showed a strong relationship with some breast cancer characteristics, especially histopathology and molecular subtype.

The rationale was to assess whether there are differences in multiparametric 18F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board-approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined 18F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer 18F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from 18F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in 18F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric 18F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

Purpose To determine if real-time breast ultrasound (RTUR) after second opinion reinterpretation of submitted static ultrasound images at a comprehensive cancer center impacts clinical management, specifically by detecting additional cancer and preventing unnecessary biopsy. Materials and methods In this IRB-approved and HIPAA-compliant retrospective study, 209 patients were included who had breast ultrasound studies from outside facilities submitted for second opinion review between January 2013 and May 2014, and who subsequently underwent RTUR at our institution within three months of the outside study. Findings on submitted exams were compared with those on RTUR and disagreements between them were annotated to indicate the presence or absence of suspicious lesions and recommendation for biopsy. Changes in management were defined as any additional biopsies performed or biopsies averted after RTUR and reported as frequencies and percentages using 95% confidence intervals. Results Following RTUR, 49 additional biopsies were performed in 43/209 patients (20.6%; 95% CI 15.1–26.1%). Additional cancer was found in 12/49 (24.5%) biopsies in 11/209 patients (5.3%; 95% CI 2.2–8.2%). Forty biopsies in 31/209 (14.8%; CI 10.0–19.7%) patients originally recommended were canceled after RTUR. Overall, a change in management after RTUR was observed in 68/209 patients (32.5%; 95% CI 26.1–38.9%), including patients with either additional biopsies performed or biopsies averted. Conclusion RTUR was found to be an important tool in the management of patients at our comprehensive cancer center. Although additional false-positive lesions may be detected on RTUR, a great number of patients will benefit from RTUR in finding additional cancers or avoiding unnecessary biopsies.

Objective. To investigate if histogram analysis and visually assessed heterogeneity of diffusion-weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping can predict molecular subtypes of invasive breast cancers. Materials and Methods. In this retrospective study, 91 patients with invasive breast carcinoma who underwent preoperative magnetic resonance imaging (MRI) with DWI at our institution were included. Two radiologists delineated a 2-D region of interest (ROI) on ADC maps in consensus. Tumors were also independently classified into low and high heterogeneity based on visual assessment of DWI. First-order statistics extracted through histogram analysis within the ROI of the ADC maps (mean, 10th percentile, 50th percentile, 90th percentile, standard deviation, kurtosis, and skewness) and visually assessed heterogeneity were evaluated for associations with tumor receptor status (ER, PR, and HER2 status) as well as molecular subtype. Results. HER2-positive lesions demonstrated significantly higher mean (p=0.034), Perc50 (p=0.046), and Perc90 (p=0.040), with AUCs of 0.605, 0.592, and 0.652, respectively, than HER2-negative lesions. No significant differences were found in the histogram values for ER and PR statuses. Neither quantitative histogram analysis based on ADC maps nor qualitative visual heterogeneity assessment of DWI images was able to significantly differentiate between molecular subtypes, i.e., luminal A versus all other subtypes (luminal B, HER2-enriched, and triple negative) combined, luminal A and B combined versus HER2-enriched and triple negative combined, and triple negative versus all other types combined. Conclusion. Histogram analysis and visual heterogeneity assessment cannot be used to differentiate molecular subtypes of invasive breast 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.

Background Available 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. Methods In 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. Results There 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. Conclusions Diagnostic 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.

The rationale was to assess whether there are differences in multiparametric (18)F-FDG PET/MRI biomarkers of contralateral healthy breast tissue in patients with benign and malignant breast tumors. Methods: In this institutional review board–approved prospective single-institution study, 141 women with imaging abnormalities on mammography or sonography (BI-RADS 4/5) underwent combined (18)F-FDG PET/MRI of the breast at 3T with dynamic contrast-enhanced MRI, diffusion-weighted imaging, and the radiotracer (18)F-FDG. In all patients, the following imaging biomarkers were recorded for the contralateral (tumor-free) breast: breast parenchymal uptake (BPU) (from (18)F-FDG PET), mean apparent diffusion coefficient (from diffusion-weighted imaging), background parenchymal enhancement (BPE), and amount of fibroglandular tissue (FGT) (from MRI). Appropriate statistical tests were used to assess differences in (18)F-FDG PET/MRI biomarkers between patients with benign and malignant lesions. Results: There were 100 malignant and 41 benign lesions. BPE was minimal in 61 patients, mild in 56, moderate in 19, and marked in 5. BPE differed significantly (P < 0.001) between patients with benign and malignant lesions, with patients with cancer demonstrating decreased BPE in the contralateral tumor-free breast. FGT approached but did not reach significance (P = 0.055). BPU was 1.5 for patients with minimal BPE, 1.9 for mild BPE, 2.2 for moderate BPE, and 1.9 for marked BPE. BPU differed significantly between patients with benign lesions (mean, 1.9) and patients with malignant lesions (mean, 1.8) (P < 0.001). Mean apparent diffusion coefficient did not differ between groups (P = 0.19). Conclusion: Differences in multiparametric (18)F-FDG PET/MRI biomarkers, obtained from contralateral tumor-free breast tissue, exist between patients with benign and patients with malignant breast tumors. Contralateral BPE, BPU, and FGT are decreased in breast cancer patients and may potentially serve as imaging biomarkers for the presence of malignancy.

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 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

Purpose To develop a multiparametric [18F]FDG positron emission tomography/magnetic resonance imaging (PET/MRI) model for breast cancer diagnosis incorporating imaging biomarkers of breast tumors and contralateral healthy breast tissue. Methods In this prospective study and retrospective data analysis, 141 patients (mean 57 years) with an imaging abnormality detected on mammography and/or ultrasound (BI-RADS 4/5) underwent combined multiparametric [18F]FDG PET/MRI with PET/computed tomography and multiparametric MRI of the breast at 3 T. Images were evaluated and the following were recorded: for the tumor, BI-RADS descriptors on dynamic contrast-enhanced (DCE)-MRI, mean apparent diffusion co-efficient (ADCmean) on diffusion-weighted imaging (DWI), and maximum standard uptake value (SUVmax) on [18F]FDG-PET; and for the contralateral healthy breast, background parenchymal enhancement (BPE) and amount of fibroglandular tissue (FGT) on DCE-MRI, ADCmean on DWI, and SUVmax. Histopathology served as standard of reference. Uni-, bi-, and multivariate logistic regression analyses were performed to assess the relationships between malignancy and imaging features. Predictive discrimination of benign and malignant breast lesions was examined using area under the receiver operating characteristic curve (AUC). Results There were 100 malignant and 41 benign lesions (size: median 1.9, range 0.5–10 cm). The multivariate regression model incorporating significant univariate predictors identified tumor enhancement kinetics (P = 0.0003), tumor ADCmean (P

Anti-prostate-specific membrane antigen (PSMA) imaging with minibodies has the advantage of faster clearance due to smaller size. We conducted a Phase I dose escalation study with 89Zr-desferrioxamine-IAB2M (89Zr-IAB2M), an anti-PSMA minibody, in patients with metastatic prostate cancer (mPC). Methods: Patients received 185 MBq (5 mCi) of 89Zr-IAB2M and Df-IAB2M at total mass doses of 10 mg (n = 6), 20 mg (n = 6), and 50 mg (n = 6). Whole-body (WB) 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. WB clearance was monoexponential with median biological half (T1/2b) of 215 h, while serum clearance showed biexponential kinetics with a median T1/2b of 3.7 h (12.3 %/L) and 33.8 h (17.9 %/L). The RAD 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 89Zr-IAB2M, most visualized by 48 h imaging. Conclusion:89Zr-IAB2M is safe and demonstrates favorable biodistribution and kinetics for targeting mPC. Imaging with 10 mg minibody mass provides optimal biodistribution, and imaging at 48 h post-injection provides good lesion visualization. Assessment of lesion targeting is being studied in detail in an expansion cohort.

e20028 Background: Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL), a subtype of ALCL, arises as a seroma in the space between the implant and the capsule, or as an adjacent mass. BIA-ALCL appears to be related to textured-surface implants, after 7-10 years of exposure. We conducted two large cohort studies. The 1st, a retrospective series (Sutton, 2019) assessed the incidental findings of masses or seromas in 1070 women with breast implants undergoing MR for FDA recommended screening for silent ruptures of silicone implants. Incidental finding of seromas or breast masses on MRI were found in 18/1070 (1.7%) women, of which 1/15 had BIA-ALCL, and was symptomatic at the time of MRI. The 2nd, a prospective study (Cordeiro, 2020) defined the incidence of BIA-ALCL (1/355) in a cohort of 3546 women with textured implants followed long term. Within this cohort, there were 28 clinically relevant delayed seromas (0.79%), 8 of which were BIA-ALCL (28.5%). We hypothesize that combining these databases will inform whether asymptomatic women with textured implants may benefit from MR to r/o BIA-ALCL. Methods: The two IRB approved databases were merged. Patients with incidental findings of seroma on MRI were identified. A majority of the MRIs in this merged cohort were performed to follow the FDA recommended screening for silent ruptures. We identified all clinically relevant late seromas sent to hematopathology to r/o BIA-ALCL by cross checking pathology reports containing the words “lymphoma” or “anaplastic” or “ALCL”. Results: 572 women were included in both studies: followed long term and received an MRI after a median time of 7.4 years after breast reconstruction. 8 of 572 women had an incidental finding of seroma on random MRI, and 2 had capsular masses. None of these 10 asymptomatic women have developed BIA-ALCL to date (median follow-up 9 years). 11 of 572 women had a symptomatic seroma or mass, 4 of which later developed BIA-ALCL, a fifth patient was found to have BIA-ALCL on a PET/CT + lymph node, despite being asymptomatic. Median time from last MR to lymphoma was 5 years (3-8 years). Conclusions: In this merged cohort of patients with textured breast implants, incidence of BIA-ALCL in patients with symptomatic late seromas is around 30%, while seromas found incidentally on MR of asymptomatic patients were negative for BIA-ALCL.

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 with conventional imaging modalities (CIM) 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 and for soft-tissue lesions (n = 90), it was 4.8 (P < 0.00003). 89Zr-J591 detected 491 osseous sites compared with 339 by MDP and 90 soft-tissue lesions compared with 124 by computed tomography (CT). Compared with all CIMs combined, 89Zr-J591 detected an additional 99 osseous sites. Forty-six lesions (21 bone and 25 soft tissue) were biopsied in 34 patients; 18 of 19 89Zr-J591–positive osseous sites and 14 of 16 89Zr-J591–positive soft tissue sites were positive for prostate cancer. The overall accuracy of 89Zr-J591 was 95.2% (20 of 21) for osseous lesions and 60% (15 of 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 prostate-specific membrane antigen (PSMA) targeting agents for prostate cancer. Clin Cancer Res; 21(23); 5277–85. ©2015 AACR.

170 Background: PCWG2 radiographic progression criteria, which are associated with survival, rely on lesion counting. There is a need for a fully quantitative assessment of tumor burden as a component of a true response biomarker for mCRPC. Here, we used the bone scan index (BSI) to fully quantitate the increase in skeletal disease burden at each radiographic milestone that constitutes the PCWG2 criteria. Methods: mCRPC patients (pts) from clinical trials using PCWG2 criteria to determine progression (PD) were assessed. Pts were required to have baseline (BL) and at least two follow-up (FU) bone scans. The EXINI automated platform was used to generate BSI at key PCWG2 landmarks: 2+2 criteria for early PD and post flare PD criteria with the appearance of 1 and ≥2 new lesions. Median (M) and Interquartile range (IQR) of the relative change in BSI were calculated to quantify the increase in total tumor burden. Results: A total of 257 pts were assessed, of whom 169 had two or more FU bone scans at 8 or 12 week intervals. Of the total 169 pts, 86 (51%, 95%CI 43-58) met PD by PCWG criteria. 35 (41%, 95%CI 31-51) of 86 pts met the early PD criteria, the remaining 51 (59%, 95%CI 49-69) met PD at subsequent time points after flare period. The ability of the automated platform to reproduce a BSI value, retrieved from the same scan, was highly consistent (100%). The relative BSI increase during the course of meeting PD by PCWG2 is shown in the Table. Conclusions: This is the first quantitative assessment of changes in total tumor burden in pts meeting PCWG2 criteria. Relative changes in BSI can be substantial in meeting the criteria. These data build on the PCWG2 criteria by quantitating the increase both of existing lesions and the contribution of new lesions. We are now assessing the associations with survival that these incremental increases represent. [Table: see text]

287 Background: There is a pressing need for improved imaging biomarkers to identify disease distribution and response in both localized and advanced prostate cancer patients. PSMA-directed imaging is undergoing analytic and clinical validation for these contexts of use. IAB2M is an anti-PSMA recombinant minibody (Mb) derived from huJ591. We have previously reported on 28 pts imaged with IAB2M(Pandit-Taskar et al, SNM 2015). Here we report the lesion targeting and uptake (SUV) of the Mb and correlation with pathology of biopsied lesions on the full complement of the 38 pts examined in this trial. Methods: 38 pts with progressive metastatic PCa received escalating amounts of the Mb (16 pts at 10mg, 16 pts at 20mg, 6 pts at 50mg) in a phase I/IIa trial. All pts underwent standard imaging (SI) using CT, bone scintigraphy (BS), FDG PET, followed by imaging with 5 mCi of IAB2M. Whole body PET/CT scans were performed and evaluated for lesion targeting and SUVmax. Biopsy (bx) locations were selected by a consensus panel prioritized on the basis of: IAB2M & FDG positivity, IAB2M & FDG mismatch, and CT or BS positivity & any PET mismatch. Results: A total of 556 lesions (410 bone, 146 soft tissue) in 38 pts were detected by SI or IAB2M. In bone, IAB2M detected 344 lesions (83.9%), CT 209 (51%), BS 211 (51.5%), and FDG 109 (26.6%). For soft tissue, IAB2M detected 119 (81.5%), CT 83 (56.8%), and FDG 79 (54.1%). The SUV for bone lesions ranged from 2.1-60.4 for 10mg Mb, 1.7- 33 in 20mg Mb, and 2.3-17.5 in 50mg Mb. For soft tissue lesions, SUV range was 3.1-45.4, 2.1-20, and 1.9-13.8 respectively. 28 bxs (13 bone, 15 soft tissue) were obtained from 27 pts; 27 bxs were evaluable (1 was non-diagnostic). 20/27 (74.1%) bxs were pos for PCa; 20/24 (83.3%) IAB2M pos lesions were path pos and 3/3 (100%) IAB2M neg lesions were neg on path. All imaging and path correlated (true pos + true neg) in 23/27 (85.2%) bxs. Conclusions: PET imaging with IAB2M has demonstrated higher lesion detection when compared with SI. IAB2M’s high concordance with path suggests pos findings represent bx confirmed PCa. Further studies to examine biochemically recurrent prostate cancer are planned. Clinical trial information: NCT01923727.

220 Background: IAB2M is a novel anti-PSMA minibody (Mb) based on the humanized J591 antibody that targets the extracellular domain of PSMA. The Mb has been engineered to remove Fc-Rn interactions and reduce the molecular weight relative to J591. The result is faster blood pool clearance, for a more favorable imaging schedule, while retaining bivalent targeting of PSMA. We have previously reported on the pharmacokinetics, dosimetry, and lesion uptake of IAB2M(Pandit-Taskar et al, WMIS 2014). Here we report the correlation between imaging and pathology of biopsies of PSMA avid lesions. Methods: Following standard imaging (SI) of CT/MRI, bone scintigraphy (BS), and FDG PET, 5 mCi of 89Zr-Df-IAB2M was administered intravenously. Escalating minibody doses of 10, 20, and 50 mg were delivered, with cohort expansion at 10 and 20 mg. Whole body PET/CT scans were serially performed at various time points:1-2 h, 24 h, 48 h, and 72-144 h post injection. Metastases identified on PET were confirmed, where possible, with bx’s in the following preference: concordant IAB2M and FDG positivity, IAB2M, and FDG mismatch, or a mismatch between SI and any PET. Results: 24 patients (pts) with metastatic prostate cancer were scanned (11 at 10, 7 at 20, and 6 at 50 mg Mb doses). A total of 15 bxs (7 soft tissue, 8 bone) were performed on 14 pts; the histopathologic correlation is summarized in the table below. 13 lesions were identified by IAB2M, 11 by FDG, and 14 by SI. Of these, 12/13 (92.3%), 10/11 (90.9%), 12/14 (85.7%) were biopsy positive for cancer, respectively. 2 bone lesions were not identified by IAB2M, evident on other imaging modalities, and were both neg by bx. Overall bx concordance (pos/pos, neg/neg) with imaging was: IAB2M 14/15 (93%) vs. BS/CT 13/15 (86%) vs. FDG 12/15 (80%). Conclusions: An ongoing analysis of IAB2M imaging showed a high concordance with pathologic findings in patients with metastatic prostate cancer. Clinical trial information: NCT01923727. [Table: see text]

164 Background: There is no standard imaging (SI) modality that specifically and accurately images prostate cancer (PC) metastases, hampering prognostication and response assessment. J591 is a humanized antibody that targets the external domain of PSMA. We have previously reported on the feasibility, PK and biodistribution properties of 89Zr-J591 in 10 patients (pts) (Pandit-Taskar et al, Eur J Nuc Med Img 2014). We now report on the targeting/accuracy in 50 pts with mCRPC. Methods: Following standard CT/MRI, bone scan (BS), and FDG PET imaging, 5 mCi of 89Zr-J591 was administered IV. 89Zr-J591 was imaged 6-8 days after injection. Positive (pos) scan findings were confirmed, where possible, with biopsies (bxs) in the following preference: concordant 89Zr-J591 and FDG pos, 89Zr-J591 and FDG mismatch, and a mismatch between SI and any PET. Results: Imaging: A total of 703 lesions in 50 pts were identified using all imaging modalities. Bone:538 total bone lesions were detected. 491(91%) lesions were present on J591 of which 99 were only evident by J591. BS identified 339 (63%), CT 301 (56%), and FDG 207 (38%). Soft Tissue: 165 total soft tissue lesions were detected. 90 (55%) were seen on J591 of which 17 were only evident by J591. CT identified 124 (75%) and FDG 88 (53%). Pathology:46 bx’s were evaluable (21 bone, 25 soft tissue) in 34 pts. Of the unique J591 lesions biopsied (bx’d), 5/7 were pos for PC. Bone: We bx’d 19 J591 pos lesions and 2 J591 neg sites. Overall, path concordance with J591 was: 89% true pos, 100% true neg, 11% false pos, and 0% false neg. Soft tissue: We bx’d 16 J591 pos lesions and 9 J591 neg sites. Of these, we found 88% true pos, 11% true neg, 13% false pos, and 89% false neg. Conclusions: J591 PET identifies additional disease in bone not seen using other imaging modalities. These lesions are highly likely to correspond to disease by bx. However, the tracer performed less well in soft tissue, with a high false neg rate. Clinical trial information: NCT01543659.

25 Background: There is no standard imaging modality that specifically and accurately images prostate cancer metastases, hampering assessments of tumor distribution, prognostication, and response. J591 is a humanized antibody that targets the external domain of PSMA, which is copiously expressed in metastatic disease and upregulated in mCRPC. We have previously reported on the feasibility, and PK and biodistribution properties of Zr-89-J591 in 10 patients (Morris et al, GU ASCO 2013). We now report on the targeting/accuracy in 49 patients with mCRPC. Methods: Following standard CT/MRI, bone scintigraphy, and FDG PET imaging, 5 mCi of Zr-89-J591 was administered intravenously. Zr-89-J591 was imaged 6-8 days after injection. Positive scan findings were confirmed, where possible, with biopsies in the following preference: Zr-89-J591 and FDG positivity, then Zr-89-J591 and FDG mismatch, and lastly standard imaging and any PET mismatch. Results: A total of 535 unique bone lesions in 49 patients were identified using all imaging modalities. 339 (63%) of these were evident on bone scans, 302 (56%) on CT, 202 (38%) on FDG PET, and 490 (92%) on Zr-89-J591 PET. The concordance between bone scans and Zr-89-J591 was 303/490 (62%). 102 sites were seen only on Zr-89-J591 and not on any other imaging modality. 44 biopsies were performed from 33 patients; 21 of those biopsies were bone. The histopathologic correlation for bone is provided in the table below. All positive biopsies were J591 positive (16/16); sensitivity of J591 was =100%, and positive predictive value (16/18) was 89%. Conclusions: Imaging using J591 PET/CT detects bone disease at greater rates than any other imaging modality tested. Biopsy data of J591 positive lesions suggest that most J591 positive lesions represent biopsy confirmed prostate metastasis. We are now testing minibodies based on J591 that may target faster, offering a more favorable imaging schedule. Clinical trial information: NCT01543659. [Table: see text]

56 Background: There are no available imaging biomarkers that can prognosticate for clinical outcomes for patients treated with AA. FDG and FDHT PET demonstrate glucose metabolism and androgen receptor expression, respectively. This is the first study of FDG and FDHT PET as a prognostic biomarker for pts treated with AA. Methods: Pts with progressive metastatic castration resistant prostate cancer (mCRPC) starting AA underwent FDG and FDHT PET scans on an imaging clinical trial. PSA, SUVmax (hottest slice of hottest lesion), and SUVmaxavg (average SUVmax of 5 index lesions) were collected. An SUV value of 1 was imputed for calculating SUVmaxavg in the absence of 5 index lesions. Imaging characteristics were compared to PFS, defined as time to radiographic progression, biochemical relapse or death. The Cox proportional hazards model was used to determine the association between the PET measurements and PFS. The statistics used to measure the association was the concordance probability estimate (CPE). Results: 24 scans sets were obtained. Median pt characteristics were: age 70 (48-85), Gleason score 8 (range 6-10), PSA 85 (2-459). Radiographic and PSA progression occurred in 15/24 (63%), radiographic only 4/24 (17%), and biochemical only 2/24 (9%). 1 pt died on study, 1 elected to discontinue tx, and 1 remains on AA. 16/24 (67%) and 18/24 (75%) of FDG and FDHT PET scans respectively had ≥ 5 hypermetabolic lesions. Median FDG SUVmax was 8 (2-15), FDG SUVmaxavg 5 (1-11), FDHT SUVmax 11 (3-28) and FDHT SUVmaxavg 7 (1-16). The correlation between PET measurements and PFS is described (Table). Conclusions: Preliminarily, baseline FDG SUVmax and SUVmaxavg appear to be prognostic for PFS in pts treated with AA. Higher SUV values prognosticate for a shorter time to progression. Further evaluations of FDG/FDHT PET in mCRPC are underway to better define the role each modality has as a prognostic and response biomarker for AA. Clinical trial information: NCT00588185. [Table: see text]