Nanoparticles use magnetoelectricity to target and eradicate cancer cells.

This study presents the first in vivo and in vitro evidence of an externally controlled, predictive, MRI-based nanotheranostic agent capable of cancer cell specific targeting and killing via irreversible electroporation (IRE) in solid tumors. The rectangular-prism-shaped magnetoelectric nanoparticle is a smart nanoparticle that produces a local electric field in response to an externally applied magnetic field. When externally activated, MENPs are preferentially attracted to the highly conductive cancer cell membranes, which occurs in cancer cells because of dysregulated ion flux across their membranes. In a pancreatic adenocarcinoma murine model, MENPs activated by external magnetic fields during magnetic resonance imaging (MRI) resulted in a mean three-fold tumor volume reduction (62.3% vs 188.7%; P < .001) from a single treatment. In a longitudinal confirmatory study, 35% of mice treated with activated MENPs achieved a durable complete response for 14 weeks after one treatment. The degree of tumor volume reduction correlated with a decrease in MRI T 2 * relaxation time ( r = .351; P = .039) which suggests that MENPs have a potential to serve as a predictive nanotheranostic agent at time of treatment. There were no discernable toxicities associated with MENPs at any timepoint or on histopathological analysis of major organs. MENPs are a noninvasive alternative modality for the treatment of cancer. Summary: We investigated the theranostic capabilities of magnetoelectric nanoparticles (MENPs) combined with MRI via a murine model of pancreatic adenocarcinoma. MENPs leverage the magnetoelectric effect to convert an applied magnetic field into local electric fields, which can induce irreversible electroporation of tumor cell membranes when activated by MRI. Additionally, MENPs modulate MRI relaxivity, which can be used to predict the degree of tumor ablation. Through a pilot study (n=21) and a confirmatory study (n=27), we demonstrated that, ≥300 µg of MRI-activated MENPs significantly reduced tumor volumes, averaging a three-fold decrease as compared to controls. Furthermore, there was a direct correlation between the reduction in tumor T 2 relaxation times and tumor volume reduction, highlighting the predictive prognostic value of MENPs. Six of 17 mice in the confirmatory study's experimental arms achieved a durable complete response, showcasing the potential for durable treatment outcomes. Importantly, the administration of MENPs was not associated with any evident toxicities. This study presents the first in vivo evidence of an externally controlled, MRI-based, theranostic agent that effectively targets and treats solid tumors via irreversible electroporation while sparing normal tissues, offering a new and promising approach to cancer therapy.

Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery.

Background and purpose: Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map ("forward models"), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images ("inverse model"), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS). Materials and methods: Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients. Results: Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model. Conclusions: A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.

Presurgical Upgrade Prediction of DCIS to Invasive Ductal Carcinoma Using Time-dependent Deep Learning Models with DCE MRI.

Purpose To determine whether time-dependent deep learning models can outperform single time point models in predicting preoperative upgrade of ductal carcinoma in situ (DCIS) to invasive malignancy at dynamic contrast-enhanced (DCE) breast MRI without a lesion segmentation prerequisite. Materials and Methods In this exploratory study, 154 cases of biopsy-proven DCIS (25 upgraded at surgery and 129 not upgraded) were selected consecutively from a retrospective cohort of preoperative DCE MRI in women with a mean age of 59 years at time of diagnosis from 2012 to 2022. Binary classification was implemented with convolutional neural network (CNN)-long short-term memory (LSTM) architectures benchmarked against traditional CNNs without manual segmentation of the lesions. Combinatorial performance analysis of ResNet50 versus VGG16-based models was performed with each contrast phase. Binary classification area under the receiver operating characteristic curve (AUC) was reported. Results VGG16-based models consistently provided better holdout test AUCs than did ResNet50 in CNN and CNN-LSTM studies (multiphase test AUC, 0.67 vs 0.59, respectively, for CNN models [P = .04] and 0.73 vs 0.62 for CNN-LSTM models [P = .008]). The time-dependent model (CNN-LSTM) provided a better multiphase test AUC over single time point (CNN) models (0.73 vs 0.67; P = .04). Conclusion Compared with single time point architectures, sequential deep learning algorithms using preoperative DCE MRI improved prediction of DCIS lesions upgraded to invasive malignancy without the need for lesion segmentation. Keywords: MRI, Dynamic Contrast-enhanced, Breast, Convolutional Neural Network (CNN) Supplemental material is available for this article. © RSNA, 2024.

Magnetic resonance imaging of tumor response to stroma-modifying pegvorhyaluronidase alpha (PEGPH20) therapy in early-phase clinical trials.

Pre-clinical and clinical studies have shown that PEGPH20 depletes intratumoral hyaluronic acid (HA), which is linked to high interstitial fluid pressures and poor distribution of chemotherapies. 29 patients with metastatic advanced solid tumors received quantitative magnetic resonance imaging (qMRI) in 3 prospective clinical trials of PEGPH20: HALO-109-101 (NCT00834704), HALO-109-102 (NCT01170897), and HALO-109-201 (NCT01453153). Apparent Diffusion Coefficient of water (ADC), T1, ktrans, vp, ve, and iAUC maps were computed from qMRI acquired at baseline and ≥ 1 time point post-PEGPH20. Tumor ADC and T1 decreased, while iAUC, ktrans, vp, and ve increased, on day 1 post-PEGPH20 relative to baseline values. This is consistent with HA depletion leading to a decrease in tumor extracellular water content and an increase in perfusion, permeability, extracellular matrix space, and vascularity. Baseline parameter values predictive of pharmacodynamic responses were: ADC > 1.46 × 10-3 mm2/s (Balanced Accuracy (BA) = 72%, p < 0.01), T1 > 0.54 s (BA = 82%, p < 0.01), iAUC < 9.2 mM-s (BA = 76%, p < 0.05), ktrans < 0.07 min-1 (BA = 72%, p = 0.2), ve < 0.17 (BA = 68%, p < 0.01), and vp < 0.02 (BA = 60%, p < 0.01). A low ve at baseline was moderately predictive of response in any parameter (BA = 65.6%, p < 0.01 averaged across patients). These qMRI biomarkers are potentially useful for guiding patient pre-selection and post-treatment follow-up in future clinical studies of PEGPH20 and other tumor stroma-modifying anti-cancer therapies.

Pancreatic Cyst Size Measurement on Magnetic Resonance Imaging Compared to Pathology.

BACKGROUND: While multiple cyst features are evaluated for stratifying pancreatic intraductal papillary mucinous neoplasms (IPMN), cyst size is an important factor that can influence treatment strategies. When magnetic resonance imaging (MRI) is used to evaluate IPMNs, no universally accepted sequence provides optimal size measurements. T2-weighted coronal/axial have been suggested as primary measurement sequences; however, it remains unknown how well these and maximum all-sequence diameter measurements correlate with pathology size. This study aims to compare agreement and bias between IPMN long-axis measurements on seven commonly obtained MRI sequences with pathologic size measurements. METHODS: This retrospective cohort included surgically resected IPMN cases with preoperative MRI exams. Long-axis diameter tumor measurements and the presence of worrisome features and/orhigh-risk stigmata were noted on all seven MRI sequences. MRI size and pathology agreement and MRI inter-observer agreement involved concordance correlation coefficient (CCC) and intraclass correlation coefficient (ICC), respectively. The presence of worrisome features and high-risk stigmata were compared to the tumor grade using kappa analysis. The Bland-Altman analysis assessed the systematic bias between MRI-size and pathology. RESULTS: In 52 patients (age 68 ± 13 years, 22 males), MRI sequences produced mean long-axis tumor measurements from 2.45-2.65 cm. The maximum MRI lesion size had a strong agreement with pathology (CCC = 0.82 (95% CI: 0.71-0.89)). The maximum IPMN size was typically observed on the axial T1 arterial post-contrast and MRCP coronal series and overestimated size versus pathology with bias +0.34 cm. The radiologist interobserver agreement reached ICCs 0.74 to 0.91 on the MRI sequences. CONCLUSION: The maximum MRI IPMN size strongly correlated with but tended to overestimate the length compared to the pathology, potentially related to formalin tissue shrinkage during tissue processing.

Rules-based Volumetric Segmentation of Multiparametric MRI for Response Assessment in Recurrent High-Grade Glioma.

We report domain knowledge-based rules for assigning voxels in brain multiparametric MRI (mpMRI) to distinct tissuetypes based on their appearance on Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced and contrast-enhanced, T2-weighted, and Fluid-Attenuated Inversion Recovery images. The development dataset comprised mpMRI of 18 participants with preoperative high-grade glioma (HGG), recurrent HGG (rHGG), and brain metastases. External validation was performed on mpMRI of 235 HGG participants in the BraTS 2020 training dataset. The treatment dataset comprised serial mpMRI of 32 participants (total 231 scan dates) in a clinical trial of immunoradiotherapy in rHGG (NCT02313272). Pixel intensity-based rules for segmenting contrast-enhancing tumor (CE), hemorrhage, Fluid, non-enhancing tumor (Edema1), and leukoaraiosis (Edema2) were identified on calibrated, co-registered mpMRI images in the development dataset. On validation, rule-based CE and High FLAIR (Edema1 + Edema2) volumes were significantly correlated with ground truth volumes of enhancing tumor (R = 0.85;p < 0.001) and peritumoral edema (R = 0.87;p < 0.001), respectively. In the treatment dataset, a model combining time-on-treatment and rule-based volumes of CE and intratumoral Fluid was 82.5% accurate for predicting progression within 30 days of the scan date. An explainable decision tree applied to brain mpMRI yields validated, consistent, intratumoral tissuetype volumes suitable for quantitative response assessment in clinical trials of rHGG.

Tumor Response to Stroma-Modifying Therapy: Magnetic Resonance Imaging Findings in Early-Phase Clinical Trials of Pegvorhyaluronidase alpha (PEGPH20).

Pre-clinical and clinical studies have shown that PEGPH20 depletes intratumoral hyaluronic acid (HA), which is linked to high interstitial fluid pressures and poor distribution of chemotherapies. 29 patients with metastatic advanced solid tumors received quantitative magnetic resonance imaging (qMRI) in 3 prospective clinical trials of PEGPH20, HALO-109-101 (NCT00834704), HALO-109-102 (NCT01170897), and HALO-109-201 (NCT01453153). Apparent Diffusion Coefficient of water (ADC), T1, ktrans, vp, ve, and iAUC maps were computed from qMRI acquired at baseline and ≥ 1 time point post-PEGPH20. Tumor ADC and T1 decreased, while iAUC, ktrans, vp, and ve increased, on day 1 post-PEGPH20 relative to baseline values. This is consistent with HA depletion leading to a decrease in tumor water content and an increase in perfusion, permeability, extracellular matrix space, and vascularity. Baseline parameter values that were predictive of pharmacodynamic responses were: ADC > 1.46×10-3 mm2/s (Balanced Accuracy (BA) = 72%, p < 0.01), T1 > 0.54s (BA = 82%, p < 0.01), iAUC < 9.2 mM-s (BA = 76%, p < 0.05), ktrans<0.07min-1 (BA = 72%, p = 0.2), ve<0.17 (BA = 68%, p < 0.01), and vp<0.02 (BA = 60%, p < 0.01). Further, ve<0.39 at baseline was moderately predictive of response in any parameter (BA = 65.6%, p < 0.01 averaged across patients). These qMRI biomarkers are potentially useful for guiding patient pre-selection and post-treatment follow-up in future clinical studies of PEGPH20 and other tumor stroma-modifying anti-cancer therapies.

Pretherapy Ferumoxytol-enhanced MRI to Predict Response to Liposomal Irinotecan in Metastatic Breast Cancer.

Purpose To investigate ferumoxytol (FMX)-enhanced MRI as a pretreatment predictor of response to liposomal irinotecan (nal-IRI) for thoracoabdominal and brain metastases in women with metastatic breast cancer (mBC). Materials and Methods In this phase 1 expansion trial (ClinicalTrials.gov identifier, NCT01770353; 27 participants), 49 thoracoabdominal (19 participants; mean age, 48 years ± 11 [SD]) and 19 brain (seven participants; mean age, 54 years ± 8) metastases were analyzed on MR images acquired before, 1-4 hours after, and 16-24 hours after FMX administration. In thoracoabdominal metastases, tumor transverse relaxation rate (R*2) was normalized to the mean R*2 in the spleen (rR*2), and the tumor histogram metric rR*2,N, representing the average of rR*2 in voxels above the nth percentile, was computed. In brain metastases, a novel compartmentation index was derived by applying the MRI signal equation to phantom-calibrated coregistered FMX-enhanced MRI brain scans acquired before, 1-4 hours after, and 16-24 hours after FMX administration. The fraction of voxels with an FMX compartmentation index greater than 1 was computed over the whole tumor (FCIGT1) and from voxels above the 90th percentile R*2 (FCIGT1 R*2,90). Results rR*2,90 computed from pretherapy MRI performed 16-24 hours after FMX administration, without reference to calibration phantoms, predicted response to nal-IRI in thoracoabdominal metastases (accuracy, 74%). rR*2,90 performance was robust to the inclusion of some peritumoral tissue within the tumor region of interest. FCIGT1 R*2,90 provided 79% accuracy on cross-validation in prediction of response in brain metastases. Conclusion This first in-human study focused on mBC suggests that FMX-enhanced MRI biologic markers can be useful for pretherapy prediction of response to nal-IRI in patients with mBC. Keywords: MRI Contrast Agent, MRI, Breast, Head/Neck, Tumor Response, Experimental Investigations, Brain/Brain Stem Clinical trial registration no. NCT01770353 Supplemental material is available for this article. © RSNA, 2023 See also commentary by Daldrup-Link in this issue.

Analysis of MRI radiomic pelvimetry and correlation with margin status after robotic prostatectomy.

INTRODUCTION: To evaluate the use of preoperative magnetic resonance imaging (MRI) as a predictor of positive margins after radical prostatectomy (RP). This is important as such patients may benefit from postoperative radiotherapy. With the advent of preoperative MRI, we posited that pelvimetry could predict positive margins after RP in patients with less-than ideal pelvic dimensions undergoing robotic-assisted laparoscopic surgery. MATERIALS AND METHODS: After IRB approval, data from patients undergoing RP at our center between 1/1/2018 and 12/31/2019 (n = 314) who had undergone prior prostate MRI imaging (n = 102) were analyzed. All RPs were performed using robotic-assisted laparoscopic technique. Data from the cancer center data warehouse were retrieved, to include postoperative T-stage, gland size, responsible surgeon, PSA, patient body mass index, and surgical margin status. These data were analyzed with corresponding pelvimetry data from 91 preoperative scans with complete data and imaging. RESULTS: On multivariable analysis, pathologic T-stage (p = 0.004), anteroposterior pelvic outlet (p = 0.015) and pelvic depth (length of the pubic symphysis; p = 0.019) were all statistically correlated with positive surgical margins. CONCLUSIONS: With the widespread use of MRI in the initial staging of prostate cancer, automated radiomic analysis could augment the critical data already being accumulated in terms of seminal vesical involvement, extracapsular extension, and suspicious lymph nodes as risk factors for postoperative salvage radiation. Such automated data could help screen patients preoperatively for robotic RP.

Quantitative Changes in Intratumoral Habitats on MRI Correlate With Pathologic Response in Early-stage ER/PR+ HER2- Breast Cancer Treated With Preoperative Stereotactic Ablative Body Radiotherapy.

Objective: To quantitatively evaluate intratumoral habitats on dynamic contrast-enhanced (DCE) breast MRI to predict pathologic breast cancer response to stereotactic ablative body radiotherapy (SABR). Methods: Participants underwent SABR treatment (28.5 Gy x3), baseline and post-SABR MRI, and breast-conserving surgery for ER/PR+ HER2- breast cancer. MRI analysis was performed on DCE T1-weighted images. MRI voxels were assigned eight habitats based on high (H) or low (L) maximum enhancement and the sequentially numbered dynamic sequence of maximum enhancement (H1-4, L1-4). MRI response was analyzed by percent tumor volume remaining (%VR = volume post-SABR/volume pre-SABR), and percent habitat makeup (%HM of habitat X = habitat X voxels/total voxels in the segmented volume). These were correlated with percent tumor bed cellularity (%TC) for pathologic response. Results: Sixteen patients completed the trial. The %TC ranged 20%-80%. MRI %VR demonstrated strong correlations with %TC (Pearson R = 0.7-0.89). Pre-SABR tumor %HMs differed significantly from whole breasts (P = 0.005 to <0.00001). Post-SABR %HM of tumor habitat H4 demonstrated the largest change, increasing 13% (P = 0.039). Conversely, combined %HM for H1-3 decreased 17% (P = 0.006). This change correlated with %TC (P < 0.00001) and distinguished pathologic partial responders (≤70 %TC) from nonresponders with 94% accuracy, 93% sensitivity, 100% specificity, 100% positive predictive value, and 67% negative predictive value. Conclusion: In patients undergoing preoperative SABR treatment for ER/PR+ HER2- breast cancer, quantitative MRI habitat analysis of %VR and %HM change correlates with pathologic response.

MRI Response to Pre-operative Stereotactic Ablative Body Radiotherapy (SABR) in Early Stage ER/PR+ HER2- Breast Cancer correlates with Surgical Pathology Tumor Bed Cellularity.

OBJECTIVE: This study evaluates breast MRI response of ER/PR+ HER2- breast tumors to pre-operative SABR with pathologic response correlation. METHODS: Women enrolled in a phase 2 single institution trial of SABR for ER/PR+ HER2- breast cancer were retrospectively evaluated for radiologic-pathologic correlation of tumor response. These patients underwent baseline breast MRI, SABR (28.5 Gy in 3 fractions), follow-up MRI 5 to 6 weeks post-SABR, and lumpectomy. Tumor size and BI-RADS descriptors on pre and post-SABR breast MRIs were compared to determine correlation with surgical specimen % tumor cellularity (%TC). Reported MRI tumor dimensions were used to calculate percent cubic volume remaining (%VR). Partial MRI response was defined as a BI-RADs descriptor change or %VR ≤ 70%, while partial pathologic response (pPR) was defined as %TC ≤ 70%. RESULTS: Nineteen patients completed the trial, and %TC ranged 10% to 80%. For BI-RADS descriptor analysis, 12 of 19 (63%) showed change in lesion or kinetic enhancement descriptors post-SABR. This was associated with lower %TC (29% vs. 47%, P = .042). BI-RADS descriptor change analysis also demonstrated high PPV (100%) and specificity (100%) for predicting pPR to treatment (sensitivity 71%, accuracy 74%), but low NPV (29%). MRI %VR demonstrated strong linear correlation with %TC (R = 0.70, P < .001, Pearson's Correlation) and high accuracy (89%) for predicting pPR (sensitivity 88%, specificity 100%, PPV 100%, and NPV 50%). CONCLUSION: Evaluating breast cancer response on MRI using %VR after pre-operative SABR treatment can help identify patients benefiting the most from neoadjuvant radiation treatment of their ER/PR+ HER2- tumors, a group in which pCR to neoadjuvant therapy is rare.

Joint total variation-based reconstruction of multiparametric magnetic resonance images for mapping tissue types.

Multispectral analysis of coregistered multiparametric magnetic resonance (MR) images provides a powerful method for tissue phenotyping and segmentation. Acquisition of a sufficiently varied set of multicontrast MR images and parameter maps to objectively define multiple normal and pathologic tissue types can require long scan times. Accelerated MRI on clinical scanners with multichannel receivers exploits techniques such as parallel imaging, while accelerated preclinical MRI scanning must rely on alternate approaches. In this work, tumor-bearing mice were imaged at 7 T to acquire k-space data corresponding to a series of images with varying T1-, T2- and T2*-weighting. A joint reconstruction framework is proposed to reconstruct a series of T1-weighted images and corresponding T1 maps simultaneously from undersampled Cartesian k-space data. The ambiguity introduced by undersampling was resolved by using model-based constraints and structural information from a reference fully sampled image as the joint total variation prior. This process was repeated to reconstruct T2-weighted and T2*-weighted images and corresponding maps of T2 and T2* from undersampled Cartesian k-space data. Validation of the reconstructed images and parameter maps was carried out by computing tissue-type maps, as well as maps of the proton density fat fraction (PDFF), proton density water fraction (PDwF), fat relaxation rate ( R2f*) and water relaxation rate ( R2w*) from the reconstructed data, and comparing them with ground truth (GT) equivalents. Tissue-type maps computed using 18% k-space data were visually similar to GT tissue-type maps, with dice coefficients ranging from 0.43 to 0.73 for tumor, fluid adipose and muscle tissue types. The mean T1 and T2 values within each tissue type computed using only 18% k-space data were within 8%-10% of the GT values from fully sampled data. The PDFF and PDwF maps computed using 27% k-space data were within 3%-15% of GT values and showed good agreement with the expected values for the four tissue types.

Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations.

Radiologic images provide a way to monitor tumor development and its response to therapies in a longitudinal and minimally invasive fashion. However, they operate on a macroscopic scale (average value per voxel) and are not able to capture microscopic scale (cell-level) phenomena. Nevertheless, to examine the causes of frequent fast fluctuations in tissue oxygenation, models simulating individual cells' behavior are needed. Here, we provide a link between the average data values recorded for radiologic images and the cellular and vascular architecture of the corresponding tissues. Using hybrid agent-based modeling, we generate a set of tissue morphologies capable of reproducing oxygenation levels observed in radiologic images. We then use these in silico tissues to investigate whether oxygen fluctuations can be explained by changes in vascular oxygen supply or by modulations in cellular oxygen absorption. Our studies show that intravascular changes in oxygen supply reproduce the observed fluctuations in tissue oxygenation in all considered regions of interest. However, larger-magnitude fluctuations cannot be recreated by modifications in cellular absorption of oxygen in a biologically feasible manner. Additionally, we develop a procedure to identify plausible tissue morphologies for a given temporal series of average data from radiology images. In future applications, this approach can be used to generate a set of tissues comparable with radiology images and to simulate tumor responses to various anti-cancer treatments at the tissue-scale level.

Identification of sarcomatoid differentiation in renal cell carcinoma by machine learning on multiparametric MRI.

Sarcomatoid differentiation in RCC (sRCC) is associated with a poor prognosis, necessitating more aggressive management than RCC without sarcomatoid components (nsRCC). Since suspected renal cell carcinoma (RCC) tumors are not routinely biopsied for histologic evaluation, there is a clinical need for a non-invasive method to detect sarcomatoid differentiation pre-operatively. We utilized unsupervised self-organizing map (SOM) and supervised Learning Vector Quantizer (LVQ) machine learning to classify RCC tumors on T2-weighted, non-contrast T1-weighted fat-saturated, contrast-enhanced arterial-phase T1-weighted fat-saturated, and contrast-enhanced venous-phase T1-weighted fat-saturated MRI images. The SOM was trained on 8 nsRCC and 8 sRCC tumors, and used to compute Activation Maps for each training, validation (3 nsRCC and 3 sRCC), and test (5 nsRCC and 5 sRCC) tumor. The LVQ classifier was trained and optimized on Activation Maps from the 22 training and validation cohort tumors, and tested on Activation Maps of the 10 unseen test tumors. In this preliminary study, the SOM-LVQ model achieved a hold-out testing accuracy of 70% in the task of identifying sarcomatoid differentiation in RCC on standard multiparameter MRI (mpMRI) images. We have demonstrated a combined SOM-LVQ machine learning approach that is suitable for analysis of limited mpMRI datasets for the task of differential diagnosis.

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk.

BACKGROUND. Higher categories of background parenchymal enhancement (BPE) increase breast cancer risk. However, current clinical BPE categorization is subjective. OBJECTIVE. Using a semiautomated segmentation algorithm, we calculated quantitative BPE measures and investigated the utility of individual features and feature pairs in significantly predicting subsequent breast cancer risk compared with radiologist-assigned BPE category. METHODS. In this retrospective case-control study, we identified 95 women at high risk of breast cancer but without a personal history of breast cancer who underwent breast MRI. Of these women, 19 subsequently developed breast cancer and were included as cases. Each case was age matched to four control patients (76 control patients total). Sociodemographic characteristics were compared between the cases and matched control patients using the Mann-Whitney U test. From each dynamic contrast-enhanced MRI examination, quantitative fibroglandular tissue and BPE measures were computed by averaging enhancing voxels above enhancement ratio thresholds (0-100%), totaling the enhancing volume above thresholds (BPE volume in cm3), and estimating the percentage of enhancing tissue above thresholds relative to total breast volume (BPE%) on each gadolinium-enhanced phase. For the 91 imaging features generated, we compared predictive performance using conditional logistic regression with 80:20 hold-out cross validation and ROC curve analysis. ROC AUC was the figure of merit. Sensitivity, specificity, PPV, and NPV were also computed. All feature pairs were exhaustively searched to identify those with the highest AUC and Youden index. A DeLong test was used to compare predictive performance (AUCs). RESULTS. Women subsequently diagnosed with breast cancer were more likely to have mild, moderate, or marked BPE (odds ratio, 3.0; 95% CI, 0.9-10.0; p = .07). According to ROC curve analysis, a BPE category threshold greater than minimal resulted in a maximized AUC (0.62) in distinguishing cases from control patients. Compared with BPE category, the first gadolinium-enhanced (phase 1) BPE% at the 30% and 40% enhancement ratio thresholds yielded significantly higher AUC values of 0.85 (p = .0007) and 0.84 (p = .0004), respectively. Feature combinations showed similar AUC values with improved sensitivity. CONCLUSION. Preliminary data indicate that quantitative BPE measures may outperform radiologist-assigned category in breast cancer risk prediction. CLINICAL IMPACT. Future risk prediction models that incorporate quantitative measures warrant additional investigation.

Molecular Determinants of Thyroid Nodules with Indeterminate Cytology and RAS Mutations.

Background:RAS gene family mutations are the most prevalent in thyroid nodules with indeterminate cytology and are present in a wide spectrum of histological diagnoses. We evaluated differentially expressed genes and signaling pathways across the histological/clinical spectrum of RAS-mutant nodules to determine key molecular determinants associated with a high risk of malignancy. Methods: Sixty-one thyroid nodules with RAS mutations were identified. Based on the histological diagnosis and biological behavior, the nodules were grouped into five categories indicating their degree of malignancy: non-neoplastic appearance, benign neoplasm, indeterminate malignant potential, low-risk cancer, or high-risk cancer. Gene expression profiles of these nodules were determined using the NanoString PanCancer Pathways and IO 360 Panels, and Angiopoietin-2 level was determined by immunohistochemical staining. Results: The analysis of differentially expressed genes using the five categories as supervising parameters unearthed a significant correlation between the degree of malignancy and genes involved in cell cycle and apoptosis (BAX, CCNE2, CDKN2A, CDKN2B, CHEK1, E2F1, GSK3B, NFKB1, and PRKAR2A), PI3K pathway (CCNE2, CSF3, GSKB3, NFKB1, PPP2R2C, and SGK2), and stromal factors (ANGPT2 and DLL4). The expression of Angiopoietin-2 by immunohistochemistry also showed the same trend of increasing expression from non-neoplastic appearance to high-risk cancer (p < 0.0001). Conclusions: The gene expression analysis of RAS-mutant thyroid nodules suggests increasing upregulation of key oncogenic pathways depending on their degree of malignancy and supports the concept of a stepwise progression. The utility of ANGPT2 expression as a potential diagnostic biomarker warrants further evaluation.

Hypofractionated stereotactic re-irradiation with pembrolizumab and bevacizumab in patients with recurrent high-grade gliomas: results from a phase I study.

BACKGROUND: Radiotherapy may synergize with programmed cell death 1 (PD1)/PD1 ligand (PD-L1) blockade. The purpose of this study was to determine the recommended phase II dose, safety/tolerability, and preliminary efficacy of combining pembrolizumab, an anti-PD1 monoclonal antibody, with hypofractionated stereotactic irradiation (HFSRT) and bevacizumab in patients with recurrent high-grade gliomas (HGGs). METHODS: Eligible subjects with recurrent glioblastoma or anaplastic astrocytoma were treated with pembrolizumab (100 or 200 mg based on dose level Q3W) concurrently with HFSRT (30 Gy in 5 fractions) and bevacizumab 10 mg/kg Q2W. RESULTS: Thirty-two patients were enrolled (bevacizumab-naïve, n = 24; bevacizumab-resistant, n = 8). The most common treatment-related adverse events (TRAEs) were proteinuria (40.6%), fatigue (25%), increased alanine aminotransferase (25%), and hypertension (25%). TRAEs leading to discontinuation occurred in 1 patient who experienced a grade 3 elevation of aspartate aminotransferase. In the bevacizumab-naïve cohort, 20 patients (83%) had a complete response or partial response. The median overall survival (OS) and progression-free survival (PFS) were 13.45 months (95% CI: 9.46-18.46) and 7.92 months (95% CI: 6.31-12.45), respectively. In the bevacizumab-resistant cohort, PR was achieved in 5 patients (62%). Median OS was 9.3 months (95% CI: 8.97-18.86) with a median PFS of 6.54 months (95% CI: 5.95-18.86). The majority of patients (n = 20/26; 77%) had tumor-cell/tumor-microenvironment PD-L1 expression <1%. CONCLUSIONS: The combination of HFSRT with pembrolizumab and bevacizumab in patients with recurrent HGG is generally safe and well tolerated. These findings merit further investigation of HFSRT with immunotherapy in HGGs.

Dose-response assessment by quantitative MRI in a phase 1 clinical study of the anti-cancer vascular disrupting agent crolibulin.

The vascular disrupting agent crolibulin binds to the colchicine binding site and produces anti-vascular and apoptotic effects. In a multisite phase 1 clinical study of crolibulin (NCT00423410), we measured treatment-induced changes in tumor perfusion and water diffusivity (ADC) using dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted MRI (DW-MRI), and computed correlates of crolibulin pharmacokinetics. 11 subjects with advanced solid tumors were imaged by MRI at baseline and 2-3 days post-crolibulin (13-24 mg/m2). ADC maps were computed from DW-MRI. Pre-contrast T1 maps were computed, co-registered with the DCE-MRI series, and maps of area-under-the-gadolinium-concentration-curve-at-90 s (AUC90s) and the Extended Tofts Model parameters ktrans, ve, and vp were calculated. There was a strong correlation between higher plasma drug [Formula: see text] and a linear combination of (1) reduction in tumor fraction with [Formula: see text] mM s, and, (2) increase in tumor fraction with [Formula: see text]. A higher plasma drug AUC was correlated with a linear combination of (1) increase in tumor fraction with [Formula: see text], and, (2) increase in tumor fraction with [Formula: see text]. These findings are suggestive of cell swelling and decreased tumor perfusion 2-3 days post-treatment with crolibulin. The multivariable linear regression models reported here can inform crolibulin dosing in future clinical studies of crolibulin combined with cytotoxic or immune-oncology agents.

Tumor Volume Dynamics as an Early Biomarker for Patient-Specific Evolution of Resistance and Progression in Recurrent High-Grade Glioma.

Recurrent high-grade glioma (HGG) remains incurable with inevitable evolution of resistance and high inter-patient heterogeneity in time to progression (TTP). Here, we evaluate if early tumor volume response dynamics can calibrate a mathematical model to predict patient-specific resistance to develop opportunities for treatment adaptation for patients with a high risk of progression. A total of 95 T1-weighted contrast-enhanced (T1post) MRIs from 14 patients treated in a phase I clinical trial with hypo-fractionated stereotactic radiation (HFSRT; 6 Gy × 5) plus pembrolizumab (100 or 200 mg, every 3 weeks) and bevacizumab (10 mg/kg, every 2 weeks; NCT02313272) were delineated to derive longitudinal tumor volumes. We developed, calibrated, and validated a mathematical model that simulates and forecasts tumor volume dynamics with rate of resistance evolution as the single patient-specific parameter. Model prediction performance is evaluated based on how early progression is predicted and the number of false-negative predictions. The model with one patient-specific parameter describing the rate of evolution of resistance to therapy fits untrained data ( R 2 = 0.70 ). In a leave-one-out study, for the nine patients that had T1post tumor volumes ≥1 cm3, the model was able to predict progression on average two imaging cycles early, with a median of 9.3 (range: 3-39.3) weeks early (median progression-free survival was 27.4 weeks). Our results demonstrate that early tumor volume dynamics measured on T1post MRI has the potential to predict progression following the protocol therapy in select patients with recurrent HGG. Future work will include testing on an independent patient dataset and evaluation of the developed framework on T2/FLAIR-derived data.

ENvironmental Dynamics Underlying Responsive Extreme Survivors (ENDURES) of Glioblastoma: A Multidisciplinary Team-based, Multifactorial Analytical Approach.

Although glioblastoma (GBM) is a fatal primary brain cancer with short median survival of 15 months, a small number of patients survive >5 years after diagnosis; they are known as extreme survivors (ES). Because of their rarity, very little is known about what differentiates these outliers from other patients with GBM. For the purpose of identifying unknown drivers of extreme survivorship in GBM, the ENDURES consortium (ENvironmental Dynamics Underlying Responsive Extreme Survivors of GBM) was developed. This consortium is a multicenter collaborative network of investigators focused on the integration of multiple types of clinical data and the creation of patient-specific models of tumor growth informed by radiographic and histologic parameters. Leveraging our combined resources, the goals of the ENDURES consortium are 2-fold: (1) to build a curated, searchable, multilayered repository housing clinical and outcome data on a large cohort of ES patients with GBM; and (2) to leverage the ENDURES repository for new insights into tumor behavior and novel targets for prolonging survival for all patients with GBM. In this article, the authors review the available literature and discuss what is already known about ES. The authors then describe the creation of their consortium and some preliminary results.