18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy.

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

Molecular Imaging for Estrogen Receptor-Positive Breast Cancer: Clinical Applications of Whole Body and Dedicated Breast Positron Emission Tomography.

18F-fluoroestradiol (18F-FES) is a Food and Drug Administration-approved radiopharmaceutical used for molecular imaging of the estrogen receptor (ER). When combined with PET, 18F-FES may improve the diagnosis of ER-positive breast cancer in the metastatic setting and provide insights into tumor heterogeneity. In this article, we review data on the use of 18F-FES imaging for treatment selection, staging, imaging lobular breast cancer, and the novel breast specific imaging tool, dedicated breast PET.

Breast MRI during Neoadjuvant Chemotherapy: Lack of Background Parenchymal Enhancement Suppression and Inferior Treatment Response.

Background Suppression of background parenchymal enhancement (BPE) is commonly observed after neoadjuvant chemotherapy (NAC) at contrast-enhanced breast MRI. It was hypothesized that nonsuppressed BPE may be associated with inferior response to NAC. Purpose To investigate the relationship between lack of BPE suppression and pathologic response. Materials and Methods A retrospective review was performed for women with menopausal status data who were treated for breast cancer by one of 10 drug arms (standard NAC with or without experimental agents) between May 2010 and November 2016 in the Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis 2, or I-SPY 2 TRIAL (NCT01042379). Patients underwent MRI at four points: before treatment (T0), early treatment (T1), interregimen (T2), and before surgery (T3). BPE was quantitatively measured by using automated fibroglandular tissue segmentation. To test the hypothesis effectively, a subset of examinations with BPE with high-quality segmentation was selected. BPE change from T0 was defined as suppressed or nonsuppressed for each point. The Fisher exact test and the Z tests of proportions with Yates continuity correction were used to examine the relationship between BPE suppression and pathologic complete response (pCR) in hormone receptor (HR)-positive and HR-negative cohorts. Results A total of 3528 MRI scans from 882 patients (mean age, 48 years ± 10 [standard deviation]) were reviewed and the subset of patients with high-quality BPE segmentation was determined (T1, 433 patients; T2, 396 patients; T3, 380 patients). In the HR-positive cohort, an association between lack of BPE suppression and lower pCR rate was detected at T2 (nonsuppressed vs suppressed, 11.8% [six of 51] vs 28.9% [50 of 173]; difference, 17.1% [95% CI: 4.7, 29.5]; P = .02) and T3 (nonsuppressed vs suppressed, 5.3% [two of 38] vs 27.4% [48 of 175]; difference, 22.2% [95% CI: 10.9, 33.5]; P = .003). In the HR-negative cohort, patients with nonsuppressed BPE had lower estimated pCR rate at all points, but the P values for the association were all greater than .05. Conclusions In hormone receptor-positive breast cancer, lack of background parenchymal enhancement suppression may indicate inferior treatment response. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Philpotts in this issue.

Mask-Guided Convolutional Neural Network for Breast Tumor Prognostic Outcome Prediction on 3D DCE-MR Images.

In this proof-of-concept work, we have developed a 3D-CNN architecture that is guided by the tumor mask for classifying several patient-outcomes in breast cancer from the respective 3D dynamic contrast-enhanced MRI (DCE-MRI) images. The tumor masks on DCE-MRI images were generated using pre- and post-contrast images and validated by experienced radiologists. We show that our proposed mask-guided classification has a higher accuracy than that from either the full image without tumor masks (including background) or the masked voxels only. We have used two patient outcomes for this study: (1) recurrence of cancer after 5 years of imaging and (2) HER2 status, for comparing accuracies of different models. By looking at the activation maps, we conclude that an image-based prediction model using 3D-CNN could be improved by even a conservatively generated mask, rather than overly trusting an unguided, blind 3D-CNN. A blind CNN may classify accurately enough, while its attention may really be focused on a remote region within 3D images. On the other hand, only using a conservatively segmented region may not be as good for classification as using full images but forcing the model's attention toward the known regions of interest.

Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL.

Dynamic contrast-enhanced (DCE) MRI provides both morphological and functional information regarding breast tumor response to neoadjuvant chemotherapy (NAC). The purpose of this retrospective study is to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The full cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). A total of 384 patients (median age: 49 y/o) were included. Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone. AUCs estimated for the combined versus highest AUCs among single features were 0.81 (95% confidence interval [CI]: 0.76, 0.86) versus 0.79 (95% CI: 0.73, 0.85) in the full cohort, 0.83 (95% CI: 0.77, 0.92) versus 0.73 (95% CI: 0.61, 0.84) in HR-positive/HER2-negative, 0.88 (95% CI: 0.79, 0.97) versus 0.78 (95% CI: 0.63, 0.89) in HR-positive/HER2-positive, 0.83 (95% CI not available) versus 0.75 (95% CI: 0.46, 0.81) in HR-negative/HER2-positive, and 0.82 (95% CI: 0.74, 0.91) versus 0.75 (95% CI: 0.64, 0.83) in triple negatives. Multi-feature MRI analysis improved pCR prediction over analysis of any individual feature that we examined. Additionally, the improvements in prediction were more notable when analysis was conducted according to cancer subtype.

Evaluation of primary breast cancers using dedicated breast PET and whole-body PET.

Metabolic imaging of the primary breast tumor with 18F-fluorodeoxyglucose ([18F]FDG) PET may assist in predicting treatment response in the neoadjuvant chemotherapy (NAC) setting. Dedicated breast PET (dbPET) is a high-resolution imaging modality with demonstrated ability in highlighting intratumoral heterogeneity and identifying small lesions in the breast volume. In this study, we characterized similarities and differences in the uptake of [18F]FDG in dbPET compared to whole-body PET (wbPET) in a cohort of ten patients with biopsy-confirmed, locally advanced breast cancer at the pre-treatment timepoint. Patients received bilateral dbPET and wbPET following administration of 186 MBq and 307 MBq [18F]FDG on separate days, respectively. [18F]FDG uptake measurements and 20 radiomic features based on morphology, tumor intensity, and texture were calculated and compared. There was a fivefold increase in SULpeak for dbPET (median difference (95% CI): 4.0 mL-1 (1.8-6.4 mL-1), p = 0.006). Additionally, spatial heterogeneity features showed statistically significant differences between dbPET and wbPET. The higher [18F]FDG uptake in dbPET highlighted the dynamic range of this breast-specific imaging modality. Combining with the higher spatial resolution, dbPET may be able to detect treatment response in the primary tumor during NAC, and future studies with larger cohorts are warranted.

Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2- Patients.

OBJECTIVE: Women with advanced HER2- breast cancer have limited treatment options. Breast MRI functional tumor volume (FTV) is used to predict pathologic complete response (pCR) to improve treatment efficacy. In addition to FTV, background parenchymal enhancement (BPE) may predict response and was explored for HER2- patients in the I-SPY-2 TRIAL. METHODS: Women with HER2- stage II or III breast cancer underwent prospective serial breast MRIs during four neoadjuvant chemotherapy timepoints. BPE was quantitatively calculated using whole-breast manual segmentation. Logistic regression models were systematically explored using pre-specified and optimized predictor selection based on BPE or combined with FTV. RESULTS: A total of 352 MRI examinations in 88 patients (29 with pCR, 59 non-pCR) were evaluated. Women with hormone receptor (HR)+HER2- cancers who achieved pCR demonstrated a significantly greater decrease in BPE from baseline to pre-surgery compared to non-pCR patients (odds ratio 0.64, 95% confidence interval (CI): 0.39-0.92, P = 0.04). The associated BPE area under the curve (AUC) was 0.77 (95% CI: 0.56-0.98), comparable to the range of FTV AUC estimates. Among multi-predictor models, the highest cross-validated AUC of 0.81 (95% CI: 0.73-0.90) was achieved with combined FTV+HR predictors, while adding BPE to FTV+HR models had an estimated AUC of 0.82 (95% CI: 0.74-0.92). CONCLUSION: Among women with HER2- cancer, BPE alone demonstrated association with pCR in women with HR+HER2- breast cancer, with similar diagnostic performance to FTV. BPE predictors remained significant in multivariate FTV models, but without added discrimination for pCR prediction. This may be due to small sample size limiting ability to create subtype-specific multivariate models.

Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network.

The Clinical Trial Design and Development Working Group within the Quantitative Imaging Network focuses on providing support for the development, validation, and harmonization of quantitative imaging (QI) methods and tools for use in cancer clinical trials. In the past 10 years, the Group has been working in several areas to identify challenges and opportunities in clinical trials involving QI and radiation oncology. The Group has been working with Quantitative Imaging Network members and the Quantitative Imaging Biomarkers Alliance leadership to develop guidelines for standardizing the reporting of quantitative imaging. As a validation platform, the Group led a multireader study to test a semi-automated positron emission tomography quantification software. Clinical translation of QI tools cannot be possible without a continuing dialogue with clinical users. This article also highlights the outreach activities extended to cooperative groups and other organizations that promote the use of QI tools to support clinical decisions.

Impact of MRI Protocol Adherence on Prediction of Pathological Complete Response in the I-SPY 2 Neoadjuvant Breast Cancer Trial.

We investigated the impact of magnetic resonance imaging (MRI) protocol adherence on the ability of functional tumor volume (FTV), a quantitative measure of tumor burden measured from dynamic contrast-enhanced MRI, to predict response to neoadjuvant chemotherapy. We retrospectively reviewed dynamic contrast-enhanced breast MRIs for 990 patients enrolled in the multicenter I-SPY 2 TRIAL. During neoadjuvant chemotherapy, each patient had 4 MRI visits (pretreatment [T0], early-treatment [T1], inter-regimen [T2], and presurgery [T3]). Protocol adherence was rated for 7 image quality factors at T0-T2. Image quality factors confirmed by DICOM header (acquisition duration, early phase timing, field of view, and spatial resolution) were adherent if the scan parameters followed the standardized imaging protocol, and changes from T0 for a single patient's visits were limited to defined ranges. Other image quality factors (contralateral image quality, patient motion, and contrast administration error) were considered adherent if imaging issues were absent or minimal. The area under the receiver operating characteristic curve (AUC) was used to measure the performance of FTV change (percent change of FTV from T0 to T1 and T2) in predicting pathological complete response. FTV changes with adherent image quality in all factors had higher estimated AUC than those with non-adherent image quality, although the differences did not reach statistical significance (T1, 0.71 vs. 0.66; T2, 0.72 vs. 0.68). These data highlight the importance of MRI protocol adherence to predefined scan parameters and the impact of data quality on the predictive performance of FTV in the breast cancer neoadjuvant setting.

Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy.

Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor-negative and human epidermal growth factor receptor 2-positive subtype.

Tumor Sphericity Predicts Response in Neoadjuvant Chemotherapy for Invasive Breast Cancer.

This retrospective study examined magnetic resonance imaging (MRI)-derived tumor sphericity (SPH) as a quantitative measure of breast tumor morphology, and investigated the association between SPH and reader-assessed morphological pattern (MP). In addition, association of SPH with pathologic complete response was evaluated in patients enrolled in an adaptively randomized clinical trial designed to rapidly identify new agents for breast cancer. All patients underwent MRI examinations at multiple time points during the treatment. SPH values from pretreatment (T0) and early-treatment (T1) were investigated in this study. MP on T0 dynamic contrast-enhanced MRI was ranked from 1 to 5 in 220 patients. Mean SPH values decreased with the increased order of MP. SPH was higher in patients with pathologic complete response than in patients without (difference at T0: 0.04, 95% confidence interval [CI]: 0.02-0.05, P < .001; difference at T1: 0.03, 95% CI: 0.02-0.04, P < .001). The area under the receiver operating characteristic curve was estimated as 0.61 (95% CI, 0.57-0.65) at T0 and 0.58 (95% CI, 0.55-0.62) at T1. When the analysis was performed by cancer subtype defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status, highest area under the receiver operating characteristic curve were observed in HR-/HER2+: 0.67 (95% CI, 0.54-0.80) at T0, and 0.63 (95% CI, 0.51-0.76) at T1. Tumor SPH showed promise to quantify MRI MPs and as a biomarker for predicting treatment outcome at pre- or early-treatment time points.

Current Landscape of Breast Cancer Imaging and Potential Quantitative Imaging Markers of Response in ER-Positive Breast Cancers Treated with Neoadjuvant Therapy.

In recent years, neoadjuvant treatment trials have shown that breast cancer subtypes identified on the basis of genomic and/or molecular signatures exhibit different response rates and recurrence outcomes, with the implication that subtype-specific treatment approaches are needed. Estrogen receptor-positive (ER+) breast cancers present a unique set of challenges for determining optimal neoadjuvant treatment approaches. There is increased recognition that not all ER+ breast cancers benefit from chemotherapy, and that there may be a subset of ER+ breast cancers that can be treated effectively using endocrine therapies alone. With this uncertainty, there is a need to improve the assessment and to optimize the treatment of ER+ breast cancers. While pathology-based markers offer a snapshot of tumor response to neoadjuvant therapy, non-invasive imaging of the ER disease in response to treatment would provide broader insights into tumor heterogeneity, ER biology, and the timing of surrogate endpoint measurements. In this review, we provide an overview of the current landscape of breast imaging in neoadjuvant studies and highlight the technological advances in each imaging modality. We then further examine some potential imaging markers for neoadjuvant treatment response in ER+ breast cancers.

Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol.

Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted.

Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL.

BACKGROUND: The change in apparent diffusion coefficient (ADC) measured from diffusion-weighted imaging (DWI) has been shown to be predictive of pathologic complete response (pCR) for patients with locally invasive breast cancer undergoing neoadjuvant chemotherapy. PURPOSE: To investigate the additive value of tumor ADC in a multicenter clinical trial setting. STUDY TYPE: Retrospective analysis of multicenter prospective data. POPULATION: In all, 415 patients who enrolled in the I-SPY 2 TRIAL from 2010 to 2014 were included. FIELD STRENGTH/SEQUENCE: 1.5T or 3T MRI system using a fat-suppressed single-shot echo planar imaging sequence with b-values of 0 and 800 s/mm2 for DWI, followed by a T1-weighted sequence for dynamic contrast-enhanced MRI (DCE-MRI) performed at pre-NAC (T0), after 3 weeks of NAC (T1), mid-NAC (T2), and post-NAC (T3). ASSESSMENT: Functional tumor volume and tumor ADC were measured at each MRI exam; pCR measured at surgery was assessed as the binary outcome. Breast cancer subtype was defined by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status. STATISTICAL TESTS: A logistic regression model was used to evaluate associations between MRI predictors with pCR. The cross-validated area under the curve (AUC) was calculated to assess the predictive performance of the model with and without ADC. RESULTS: In all, 354 patients (128 HR+/HER2-, 60 HR+/HER2+, 34 HR-/HER2+, 132 HR-/HER2-) were included in the analysis. In the full cohort, adding ADC predictors increased the AUC from 0.76 to 0.78 at mid-NAC and from 0.76 to 0.81 at post-NAC. In HR/HER2 subtypes, the AUC increased from 0.52 to 0.65 at pre-NAC for HR+/HER2-, from 0.67 to 0.73 at mid-NAC and from 0.72 to 0.76 at post-NAC for HR+/HER2+, from 0.71 to 0.81 at post-NAC for triple negatives. DATA CONCLUSION: The addition of ADC to standard functional tumor volume MRI showed improvement in the prediction of treatment response in HR+ and triple-negative breast cancer. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2019;50:1742-1753.

Tumor Drug Penetration Measurements Could Be the Neglected Piece of the Personalized Cancer Treatment Puzzle.

Precision medicine aims to use patient genomic, epigenomic, specific drug dose, and other data to define disease patterns that may potentially lead to an improved treatment outcome. Personalized dosing regimens based on tumor drug penetration can play a critical role in this approach. State-of-the-art techniques to measure tumor drug penetration focus on systemic exposure, tissue penetration, cellular or molecular engagement, and expression of pharmacological activity. Using in silico methods, this information can be integrated to bridge the gap between the therapeutic regimen and the pharmacological link with clinical outcome. These methodologies are described, and challenges ahead are discussed. Supported by many examples, this review shows how the combination of these techniques provides enhanced patient-specific information on drug accessibility at the tumor tissue level, target binding, and downstream pharmacology. Our vision of how to apply tumor drug penetration measurements offers a roadmap for the clinical implementation of precision dosing.

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival.

The Use of Quantitative Imaging in Radiation Oncology: A Quantitative Imaging Network (QIN) Perspective.

Modern radiation therapy is delivered with great precision, in part by relying on high-resolution multidimensional anatomic imaging to define targets in space and time. The development of quantitative imaging (QI) modalities capable of monitoring biologic parameters could provide deeper insight into tumor biology and facilitate more personalized clinical decision-making. The Quantitative Imaging Network (QIN) was established by the National Cancer Institute to advance and validate these QI modalities in the context of oncology clinical trials. In particular, the QIN has significant interest in the application of QI to widen the therapeutic window of radiation therapy. QI modalities have great promise in radiation oncology and will help address significant clinical needs, including finer prognostication, more specific target delineation, reduction of normal tissue toxicity, identification of radioresistant disease, and clearer interpretation of treatment response. Patient-specific QI is being incorporated into radiation treatment design in ways such as dose escalation and adaptive replanning, with the intent of improving outcomes while lessening treatment morbidities. This review discusses the current vision of the QIN, current areas of investigation, and how the QIN hopes to enhance the integration of QI into the practice of radiation oncology.

Features of MRI stromal enhancement with neoadjuvant chemotherapy: a subgroup analysis of the ACRIN 6657/I-SPY TRIAL.

Although the role of cancer-activated stroma in malignant progression has been well investigated, the influence of an activated stroma in therapy response is not well understood. Using retrospective pilot cohorts, we previously observed that MRI detected stromal contrast enhancement was associated with proximity to the tumor and was predictive for relapse-free survival in patients with breast cancer receiving neoadjuvant chemotherapy. Here, to evaluate the association of stromal contrast enhancement to therapy, we applied an advanced tissue mapping technique to evaluate stromal enhancement patterns within 71 patients enrolled in the I-SPY 1 neoadjuvant breast cancer trial. We correlated MR stromal measurements with stromal protein levels involved in tumor progression processes. We found that stromal percent enhancement values decrease with distance from the tumor edge with the estimated mean change ranging [Formula: see text] to [Formula: see text] ([Formula: see text]) for time points T2 through T4. While not statistically significant, we found a decreasing trend in global stromal signal enhancement ratio values with the use of chemotherapy. There were no statistically significant differences between MR enhancement measurements and stromal protein levels. Findings from this study indicate that stromal features characterized by MRI are impacted by chemotherapy and may have predictive value in a larger study.

Heterogeneous drug penetrance of veliparib and carboplatin measured in triple negative breast tumors.

BACKGROUND: Poly(ADP-ribose) polymerase inhibitors (PARPi), coupled to a DNA damaging agent is a promising approach to treating triple negative breast cancer (TNBC). However, not all patients respond; we hypothesize that non-response in some patients may be due to insufficient drug penetration. As a first step to testing this hypothesis, we quantified and visualized veliparib and carboplatin penetration in mouse xenograft TNBCs and patient blood samples. METHODS: MDA-MB-231, HCC70 or MDA-MB-436 human TNBC cells were implanted in 41 beige SCID mice. Low dose (20 mg/kg) or high dose (60 mg/kg) veliparib was given three times daily for three days, with carboplatin (60 mg/kg) administered twice. In addition, blood samples were analyzed from 19 patients from a phase 1 study of carboplatin + PARPi talazoparib. Veliparib and carboplatin was quantified using liquid chromatography-mass spectrometry (LC-MS). Veliparib tissue penetration was visualized using matrix-assisted laser desorption/ionization mass spectrometric imaging (MALDI-MSI) and platinum adducts (covalent nuclear DNA-binding) were quantified using inductively coupled plasma-mass spectrometry (ICP-MS). Pharmacokinetic modeling and Pearson's correlation were used to explore associations between concentrations in plasma, tumor cells and peripheral blood mononuclear cells (PBMCs). RESULTS: Veliparib penetration in xenograft tumors was highly heterogeneous between and within tumors. Only 35% (CI 95% 26-44%), 74% (40-97%) and 46% (9-37%) of veliparib observed in plasma penetrated into MDA-MB-231, HCC70 and MDA-MB-436 cell-based xenografts, respectively. Within tumors, penetration heterogeneity was larger with the 60 mg/kg compared to the 20 mg/kg dose (RSD 155% versus 255%, P = 0.001). These tumor concentrations were predicted similar to clinical dosing levels, but predicted tumor concentrations were below half maximal concentration values as threshold of response. Xenograft veliparib concentrations correlated positively with platinum adduct formation (R 2 = 0.657), but no PARPi-platinum interaction was observed in patients' PBMCs. Platinum adduct formation was significantly higher in five gBRCA carriers (ratio of platinum in DNA in PBMCs/plasma 0.64% (IQR 0.60-1.16%) compared to nine non-carriers (ratio 0.29% (IQR 0.21-0.66%, P < 0.0001). CONCLUSIONS: PARPi/platinum tumor penetration can be measured by MALDI-MSI and ICP-MS in PBMCs and fresh frozen, OCT embedded core needle biopsies. Large variability in platinum adduct formation and spatial heterogeneity in veliparib distribution may lead to insufficient drug exposure in select cell populations.