A Novel Positive-Contrast Magnetic Resonance Imaging Line Marker for High-Dose-Rate (HDR) MRI-Assisted Radiosurgery (MARS).

Magnetic resonance imaging (MRI) can facilitate accurate organ delineation and optimal dose distributions in high-dose-rate (HDR) MRI-Assisted Radiosurgery (MARS). Its use for this purpose has been limited by the lack of positive-contrast MRI markers that can clearly delineate the lumen of the HDR applicator and precisely show the path of the HDR source on T1- and T2-weighted MRI sequences. We investigated a novel MRI positive-contrast HDR brachytherapy or interventional radiotherapy line marker, C4:S, consisting of C4 (visible on T1-weighted images) complexed with saline. Longitudinal relaxation time (T1) and transverse relaxation time (T2) for C4:S were measured on a 1.5 T MRI scanner. High-density polyethylene (HDPE) tubing filled with C4:S as an HDR brachytherapy line marker was tested for visibility on T1- and T2-weighted MRI sequences in a tissue-equivalent female ultrasound training pelvis phantom. Relaxivity measurements indicated that C4:S solution had good T1-weighted contrast (relative to oil [fat] signal intensity) and good T2-weighted contrast (relative to water signal intensity) at both room temperature (relaxivity ratio > 1; r2/r1 = 1.43) and body temperature (relaxivity ratio > 1; r2/r1 = 1.38). These measurements were verified by the positive visualization of the C4:S (C4/saline 50:50) HDPE tube HDR brachytherapy line marker on both T1- and T2-weighted MRI sequences. Orientation did not affect the relaxivity of the C4:S contrast solution. C4:S encapsulated in HDPE tubing can be visualized as a positive line marker on both T1- and T2-weighted MRI sequences. MRI-guided HDR planning may be possible with these novel line markers for HDR MARS for several types of cancer.

A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy.

BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA. METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient. RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible. CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

Diffusion Tensor Imaging for Characterizing Changes in Triple-Negative Breast Cancer During Neoadjuvant Systemic Therapy.

BACKGROUND: Assessment of treatment response in triple-negative breast cancer (TNBC) may guide individualized care for improved patient outcomes. Diffusion tensor imaging (DTI) measures tissue anisotropy and could be useful for characterizing changes in the tumors and adjacent fibroglandular tissue (FGT) of TNBC patients undergoing neoadjuvant systemic treatment (NAST). PURPOSE: To evaluate the potential of DTI parameters for prediction of treatment response in TNBC patients undergoing NAST. STUDY TYPE: Prospective. POPULATION: Eighty-six women (average age: 51 ± 11 years) with biopsy-proven clinical stage I-III TNBC who underwent NAST followed by definitive surgery. 47% of patients (40/86) had pathologic complete response (pCR). FIELD STRENGTH/SEQUENCE: 3.0 T/reduced field of view single-shot echo-planar DTI sequence. ASSESSMENT: Three MRI scans were acquired longitudinally (pre-treatment, after 2 cycles of NAST, and after 4 cycles of NAST). Eleven histogram features were extracted from DTI parameter maps of tumors, a peritumoral region (PTR), and FGT in the ipsilateral breast. DTI parameters included apparent diffusion coefficients and relative diffusion anisotropies. pCR status was determined at surgery. STATISTICAL TESTS: Longitudinal changes of DTI features were tested for discrimination of pCR using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC). A P value <0.05 was considered statistically significant. RESULTS: 47% of patients (40/86) had pCR. DTI parameters assessed after 2 and 4 cycles of NAST were significantly different between pCR and non-pCR patients when compared between tumors, PTRs, and FGTs. The median surface/average anisotropy of the PTR, measured after 2 and 4 cycles of NAST, increased in pCR patients and decreased in non-pCR patients (AUC: 0.78; 0.027 ± 0.043 vs. -0.017 ± 0.042 mm2 /s). DATA CONCLUSION: Quantitative DTI features from breast tumors and the peritumoral tissue may be useful for predicting the response to NAST in TNBC. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.

We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (DWI) of the pre-treatment (baseline) and after four cycles (C4) of doxorubicin/cyclophosphamide treatment were used as inputs to the model for prediction of pathologic complete response (pCR). Based on the standard pCR definition that includes disease status in either breast or axilla, the model achieved areas under the receiver operating characteristic curves (AUCs) of 0.96 ± 0.05, 0.78 ± 0.09, 0.88 ± 0.02, and 0.76 ± 0.03, for the training, validation, testing, and prospective testing groups, respectively. For the pCR status of breast only, the retrained model achieved prediction AUCs of 0.97 ± 0.04, 0.82 ± 0.10, 0.86 ± 0.03, and 0.83 ± 0.02, for the training, validation, testing, and prospective testing groups, respectively. Thus, the developed deep learning model is highly promising for predicting the treatment response to NAST of TNBC.Clinical Relevance- Deep learning based on serial and multiparametric MRIs can potentially distinguish TNBC patients with pCR from non-pCR at the early stage of neoadjuvant systemic therapy, potentially enabling more personalized treatment of TNBC patients.

Longitudinal dynamic contrast-enhanced MRI radiomic models for early prediction of response to neoadjuvant systemic therapy in triple-negative breast cancer.

Early prediction of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) patients could help oncologists select individualized treatment and avoid toxic effects associated with ineffective therapy in patients unlikely to achieve pathologic complete response (pCR). The objective of this study is to evaluate the performance of radiomic features of the peritumoral and tumoral regions from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquired at different time points of NAST for early treatment response prediction in TNBC. This study included 163 Stage I-III patients with TNBC undergoing NAST as part of a prospective clinical trial (NCT02276443). Peritumoral and tumoral regions of interest were segmented on DCE images at baseline (BL) and after two (C2) and four (C4) cycles of NAST. Ten first-order (FO) radiomic features and 300 gray-level-co-occurrence matrix (GLCM) features were calculated. Area under the receiver operating characteristic curve (AUC) and Wilcoxon rank sum test were used to determine the most predictive features. Multivariate logistic regression models were used for performance assessment. Pearson correlation was used to assess intrareader and interreader variability. Seventy-eight patients (48%) had pCR (52 training, 26 testing), and 85 (52%) had non-pCR (57 training, 28 testing). Forty-six radiomic features had AUC at least 0.70, and 13 multivariate models had AUC at least 0.75 for training and testing sets. The Pearson correlation showed significant correlation between readers. In conclusion, Radiomic features from DCE-MRI are useful for differentiating pCR and non-pCR. Similarly, predictive radiomic models based on these features can improve early noninvasive treatment response prediction in TNBC patients undergoing NAST.

Dietary protein intake and changes in muscle mass measurements in community-dwelling middle-aged and older adults: A prospective cohort study.

BACKGROUND & AIMS: Increasing dietary protein intake can be an efficient strategy to prevent sarcopenia. Nevertheless, due to the discrepancy in the population and their dietary pattern, evidence suggested the effects of dietary protein amount or source on sarcopenia prevention varies. This prospective cohort study investigated the correlation between dietary protein intakes or sources and changes in muscle mass measurements. Additionally, the study explored the link between dietary protein and the prevalence of sarcopenia. METHODS: Participants aged 40 to 75 were from Guangzhou Nutrition and Health Study (GNHS) 2011-2013 and returned in 2014-2017. Validated 79-item food frequency questionnaires were applied to calculate the amount of total, animal, and plant protein intakes and animal-to-plant protein ratio (APR). The body composition was examined by dual-energy x-ray absorptiometry (DXA) to calculate the appendicular lean mass (ALM) and its index (ASMI). Sarcopenia was diagnosed based on the 2019 Asia Working Group of Sarcopenia's criteria. ANCOVA was utilized to compare the differences of Δ ALM and Δ ASMI across the quartiles of the dietary protein, and linear regression was employed to examine dose-response associations. Multilinear mixed-effect models were employed to evaluate whether protein intake relates to annual changes in ALM and ASMI. Multivariable logistic regressions were performed to analyze the associations between dietary protein and sarcopenia. RESULTS: In total, 2709 participants during the 3.2-year follow-up period were considered eligible for analysis. Higher dietary protein intakes (total, animal, plant) in both sexes could preserve more ALM and ASMI in a dose-response manner (all P-trend < 0.05). The annual estimated preservations of ASMI were greater in the highest dietary protein intakes (total, animal, plant) quartile than the lowest (0.05-0.13 kg/m2/y, all P < 0.05). In women, the risk of sarcopenia was reduced by 35%-50 % in the highest protein intake (total, animal, plant) quartile than the lowest. The APR did not display any significant associations. CONCLUSIONS: Higher dietary protein intake, regardless of animal or plant sources, is associated with less muscle loss and a lower prevalence of sarcopenia in middle-aged and older Chinese, particularly women. GOV IDENTIFIER: NCT03179657.

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging.

OBJECTIVES: Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. METHODS: Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. RESULTS: Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images ( P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images ( P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. CONCLUSION: The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer.

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

Genome-wide characterization of the VQ genes in Triticeae and their functionalization driven by polyploidization and gene duplication events in wheat.

Valine-glutamine motif-containing (VQ) proteins are transcriptional cofactors widely involved in plant growth, development, and response to various stresses. Although the VQ family has been genome-wide identified in some species, but the knowledge regarding duplication-driven functionalization of VQ genes among evolutionarily related species is still lacking. Here, 952 VQ genes have been identified from 16 species, emphasizing seven Triticeae species including the bread wheat. Comprehensive phylogenetic and syntenic analyses allow us to establish the orthologous relationship of VQ genes from rice (Oryza sativa) to bread wheat (Triticum aestivum). The evolutionary analysis revealed that whole-genome duplication (WGD) drives the expansion of OsVQs, while TaVQs expansion is associated with a recent burst of gene duplication (RBGD). We also analyzed the motif composition and molecular properties of TaVQ proteins, enriched biological functions, and expression patterns of TaVQs. We demonstrate that WGD-derived TaVQs have become divergent in both protein motif composition and expression pattern, while RBGD-derived TaVQs tend to adopt specific expression patterns, suggesting their functionalization in certain biological processes or in response to specific stresses. Furthermore, some RBGD-derived TaVQs are found to be associated with salt tolerance. Several of the identified salt-related TaVQ proteins were located in the cytoplasm and nucleus and their salt-responsive expression patterns were validated by qPCR analysis. Yeast-based functional experiments confirmed that TaVQ27 may be a new regulator to salt response and regulation. Overall, this study lays the foundation for further functional validation of VQ family members within the Triticeae species.

Associations of serum betaine with blood pressure and hypertension incidence in middle-aged and older adults: a prospective cohort study.

The impact of betaine on the development of hypertension remains unclear, and prospective data are sparse. We aimed to investigate the association of serum betaine with repeated measurements of blood pressure (BP) and hypertension incidence. This study was based on the Guangzhou Nutrition and Health Study (GNHS), a community-based prospective cohort study in China. Baseline serum betaine was measured by high-performance liquid chromatography-tandem mass spectrometry. BP and hypertension status were assessed at the baseline and 3-year intervals. Linear mixed-effects models (LMEMs) were used to analyze the longitudinal association of serum betaine with BP (n = 1996). Cox proportional hazard models were used to evaluate the association of baseline serum betaine with hypertension incidence (n = 1339). LMEMs showed that compared with the lowest quartile group, the higher quartile groups had lower systolic blood pressure (SBP), diastolic blood pressure (DBP) and pulse pressure (all P-trend < 0.05). Each standard deviation (16.3 µmol L-1) increase in serum betaine was associated with -0.92 (-1.52, -0.32) mmHg of SBP, -0.49 (-0.84, -0.13) mmHg of DBP and -0.43 (-0.81, -0.05) mmHg of pulse pressure. During a median follow-up of 9.2 years, 371 incident cases of hypertension were identified. Serum betaine was associated with lower risk of hypertension only when comparing the third quartile level with the lowest quartile (HR, 0.74; 95% CI, 0.56-0.99). A nonlinear association between serum betaine and the risk of hypertension was found (P-nonlinear = 0.040). A higher serum betaine level was associated with lower risk of hypertension below 54.5 µmol L-1. Our findings suggested that higher serum betaine was associated with favorable blood pressure in middle-aged and older Chinese adults. Higher concentrations of serum betaine were related to lower hypertension risk in people with relatively low serum betaine concentrations.

The optimized rectal cancer MRI protocol: choosing the right sequences, sequence parameters, and preparatory strategies.

Pelvic MRI plays a critical role in rectal cancer staging and treatment response assessment. Despite a consensus regarding the essential protocol components of a rectal cancer MRI, substantial differences in image quality persist across institutions and vendor software/hardware platforms. In this review, we present image optimization strategies for rectal cancer MRI examinations, including but not limited to preparation strategies, high-resolution T2-weighted imaging, and diffusion-weighted imaging. Our specific recommendations are supported by case studies from multiple institutions. Finally, we describe an ongoing initiative by the Society of Abdominal Radiology's Disease-Focused Panel (DFP) on Rectal and Anal Cancer to create standardized rectal cancer MRI protocols across scanner platforms.

Assessment of Response to Neoadjuvant Systemic Treatment in Triple-Negative Breast Cancer Using Functional Tumor Volumes from Longitudinal Dynamic Contrast-Enhanced MRI.

Early assessment of neoadjuvant systemic therapy (NAST) response for triple-negative breast cancer (TNBC) is critical for patient care in order to avoid the unnecessary toxicity of an ineffective treatment. We assessed functional tumor volumes (FTVs) from dynamic contrast-enhanced (DCE) MRI after 2 cycles (C2) and 4 cycles (C4) of NAST as predictors of response in TNBC. A group of 100 patients with stage I-III TNBC who underwent DCE MRI at baseline, C2, and C4 were included in this study. Tumors were segmented on DCE images of 1 min and 2.5 min post-injection. FTVs were measured using the optimized percentage enhancement (PE) and signal enhancement ratio (SER) thresholds. The Mann-Whitney test was used to compare the performance of the FTVs at C2 and C4. Of the 100 patients, 49 (49%) had a pathologic complete response (pCR) and 51 (51%) had a non-pCR. The maximum area under the receiving operating characteristic curve (AUC) for predicting the treatment response was 0.84 (p < 0.001) for FTV at C4 followed by FTV at C2 (AUC = 0.82, p < 0.001). The FTV measured at baseline was not able to discriminate pCR from non-pCR. FTVs measured on DCE MRI at C2, as well as at C4, of NAST can potentially predict pCR and non-pCR in TNBC patients.

Prediction of pathologic complete response to neoadjuvant systemic therapy in triple negative breast cancer using deep learning on multiparametric MRI.

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer. Neoadjuvant systemic therapy (NAST) followed by surgery are currently standard of care for TNBC with 50-60% of patients achieving pathologic complete response (pCR). We investigated ability of deep learning (DL) on dynamic contrast enhanced (DCE) MRI and diffusion weighted imaging acquired early during NAST to predict TNBC patients' pCR status in the breast. During the development phase using the images of 130 TNBC patients, the DL model achieved areas under the receiver operating characteristic curves (AUCs) of 0.97 ± 0.04 and 0.82 ± 0.10 for the training and the validation, respectively. The model achieved an AUC of 0.86 ± 0.03 when evaluated in the independent testing group of 32 patients. In an additional prospective blinded testing group of 48 patients, the model achieved an AUC of 0.83 ± 0.02. These results demonstrated that DL based on multiparametric MRI can potentially differentiate TNBC patients with pCR or non-pCR in the breast early during NAST.

Serum trimethylamine-N-oxide is associated with incident type 2 diabetes in middle-aged and older adults: a prospective cohort study.

BACKGROUND: The role of trimethylamine-N-oxide (TMAO) in the development of diabetes remains controversial, and prospective data are few. We aimed to investigate the association between serum TMAO and incident type 2 diabetes in middle-aged and older adults. METHODS: This study was based on the Guangzhou Nutrition and Health Study (GNHS), a community-based prospective cohort study in China. A total of 2088 diabetes-free participants aged 40-75 years were included from 2008 to 2010. Incident type 2 diabetes was ascertained during follow-up visits. Baseline serum TMAO was measured by high-performance liquid chromatography with online electrospray ionization tandem mass spectrometry. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) for diabetes across tertiles of serum TMAO were calculated using Cox proportional hazard models. Prospective associations of serum TMAO with changes in glycemic traits (fasting glucose, HbA1c, insulin, HOMA-IR) over time were estimated using linear mixed-effects models (LMEMs). RESULTS: We ascertained 254 incident type 2 diabetes cases during a median follow-up of 8.9 years. The median (interquartile range) of serum TMAO was 1.54 (0.86-2.91) µmol/L. From the first to the third tertile of serum TMAO, the multivariable-adjusted HRs for diabetes were 1.00 (reference), 1.17 (95% CI: 0.84-1.61), and 1.42 (95% CI: 1.03-1.96) (P-trend = 0.031). LMEMs showed that the estimated yearly change in fasting glucose was 0.011 (0.001-0.022) mmol/L/y in the highest tertile of serum TMAO, compared with the lowest tertile (P-interaction = 0.044). Serum TMAO was not associated with longitudinal changes in HbA1c, insulin or HOMA-IR. CONCLUSIONS: Our findings suggested that higher serum TMAO was associated with a higher risk of type 2 diabetes and an increase in fasting glucose among middle-aged and older Chinese adults. TRIAL REGISTRATION: NCT03179657. https://clinicaltrials.gov/ct2/show/NCT03179657?term=NCT03179657&draw=2&rank=1.

MRI-Based Digital Models Forecast Patient-Specific Treatment Responses to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to improve targeting and evaluation of responses to therapy in this disease are needed. Here, we integrate quantitative MRI data with biologically based mathematical modeling to accurately predict the response of TNBC to neoadjuvant systemic therapy (NAST) on an individual basis. Specifically, 56 patients with TNBC enrolled in the ARTEMIS trial (NCT02276443) underwent standard-of-care doxorubicin/cyclophosphamide (A/C) and then paclitaxel for NAST, where dynamic contrast-enhanced MRI and diffusion-weighted MRI were acquired before treatment and after two and four cycles of A/C. A biologically based model was established to characterize tumor cell movement, proliferation, and treatment-induced cell death. Two evaluation frameworks were investigated using: (i) images acquired before and after two cycles of A/C for calibration and predicting tumor status after A/C, and (ii) images acquired before, after two cycles, and after four cycles of A/C for calibration and predicting response following NAST. For Framework 1, the concordance correlation coefficients between the predicted and measured patient-specific, post-A/C changes in tumor cellularity and volume were 0.95 and 0.94, respectively. For Framework 2, the biologically based model achieved an area under the receiver operator characteristic curve of 0.89 (sensitivity/specificity = 0.72/0.95) for differentiating pathological complete response (pCR) from non-pCR, which is statistically superior (P &lt; 0.05) to the value of 0.78 (sensitivity/specificity = 0.72/0.79) achieved by tumor volume measured after four cycles of A/C. Overall, this model successfully captured patient-specific, spatiotemporal dynamics of TNBC response to NAST, providing highly accurate predictions of NAST response. SIGNIFICANCE: Integrating MRI data with biologically based mathematical modeling successfully predicts breast cancer response to chemotherapy, suggesting digital twins could facilitate a paradigm shift from simply assessing response to predicting and optimizing therapeutic efficacy.

Quantitative Apparent Diffusion Coefficients From Peritumoral Regions as Early Predictors of Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer.

BACKGROUND: Pathologic complete response (pCR) to neoadjuvant systemic therapy (NAST) in triple-negative breast cancer (TNBC) is a strong predictor of patient survival. Edema in the peritumoral region (PTR) has been reported to be a negative prognostic factor in TNBC. PURPOSE: To determine whether quantitative apparent diffusion coefficient (ADC) features from PTRs on reduced field-of-view (rFOV) diffusion-weighted imaging (DWI) predict the response to NAST in TNBC. STUDY TYPE: Prospective. POPULATION/SUBJECTS: A total of 108 patients with biopsy-proven TNBC who underwent NAST and definitive surgery during 2015-2020. FIELD STRENGTH/SEQUENCE: A 3.0 T/rFOV single-shot diffusion-weighted echo-planar imaging sequence (DWI). ASSESSMENT: Three scans were acquired longitudinally (pretreatment, after two cycles of NAST, and after four cycles of NAST). For each scan, 11 ADC histogram features (minimum, maximum, mean, median, standard deviation, kurtosis, skewness and 10th, 25th, 75th, and 90th percentiles) were extracted from tumors and from PTRs of 5 mm, 10 mm, 15 mm, and 20 mm in thickness with inclusion and exclusion of fat-dominant pixels. STATISTICAL TESTS: ADC features were tested for prediction of pCR, both individually using Mann-Whitney U test and area under the receiver operating characteristic curve (AUC), and in combination in multivariable models with k-fold cross-validation. A P value < 0.05 was considered statistically significant. RESULTS: Fifty-one patients (47%) had pCR. Maximum ADC from PTR, measured after two and four cycles of NAST, was significantly higher in pCR patients (2.8 ± 0.69 vs 3.5 ± 0.94 mm2 /sec). The top-performing feature for prediction of pCR was the maximum ADC from the 5-mm fat-inclusive PTR after cycle 4 of NAST (AUC: 0.74; 95% confidence interval: 0.64, 0.84). Multivariable models of ADC features performed similarly for fat-inclusive and fat-exclusive PTRs, with AUCs ranging from 0.68 to 0.72 for the cycle 2 and cycle 4 scans. DATA CONCLUSION: Quantitative ADC features from PTRs may serve as early predictors of the response to NAST in TNBC. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 4.

Assessment of Early Response to Neoadjuvant Systemic Therapy in Triple-Negative Breast Cancer Using Amide Proton Transfer-weighted Chemical Exchange Saturation Transfer MRI: A Pilot Study.

Purpose To determine if amide proton transfer-weighted chemical exchange saturation transfer (APTW CEST) MRI is useful in the early assessment of treatment response in persons with triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, a total of 51 participants (mean age, 51 years [range, 26-79 years]) with TNBC were included who underwent APTW CEST MRI with 0.9- and 2.0-µT saturation power performed at baseline, after two cycles (C2), and after four cycles (C4) of neoadjuvant systemic therapy (NAST). Imaging was performed between January 31, 2019, and November 11, 2019, and was a part of a clinical trial (registry number NCT02744053). CEST MR images were analyzed using two methods-magnetic transfer ratio asymmetry (MTRasym) and Lorentzian line shape fitting. The APTW CEST signals at baseline, C2, and C4 were compared for 51 participants to evaluate the saturation power levels and analysis methods. The APTW CEST signals and their changes during NAST were then compared for the 26 participants with pathology reports for treatment response assessment. Results A significant APTW CEST signal decrease was observed during NAST when acquisition at 0.9-µT saturation power was paired with Lorentzian line shape fitting analysis and when the acquisition at 2.0 µT was paired with MTRasym analysis. Using 0.9-µT saturation power and Lorentzian line shape fitting, the APTW CEST signal at C2 was significantly different from baseline in participants with pathologic complete response (pCR) (3.19% vs 2.43%; P = .03) but not with non-pCR (2.76% vs 2.50%; P > .05). The APTW CEST signal change was not significant between pCR and non-pCR at all time points. Conclusion Quantitative APTW CEST MRI depended on optimizing acquisition saturation powers and analysis methods. APTW CEST MRI monitored treatment effects but did not differentiate participants with TNBC who had pCR from those with non-pCR. © RSNA, 2021 Clinical trial registration no. NCT02744053 Supplemental material is available for this article.Keywords Molecular Imaging-Cancer, Molecular Imaging-Clinical Translation, MR-Imaging, Breast, Technical Aspects, Tumor Response, Technology Assessment.

Computer-aided detection of mantle cell lymphoma on 18F-FDG PET/CT using a deep learning convolutional neural network.

18F-FDG PET/CT can provide quantitative characterization with prognostic value for mantle cell lymphoma (MCL). However, detection of MCL is performed manually, which is labor intensive and not a part of the routine clinical practice. This study investigates a deep learning convolutional neural network (DLCNN) for computer-aided detection of MCL on 18F-FDG PET/CT. We retrospectively analyzed 142 baseline 18F-FDG PET/CT scans of biopsy-confirmed MCL acquired between May 2007 and October 2018. Of the 142 scans, 110 were from our institution and 32 were from outside institutions. An Xception-based U-Net was constructed to classify each pixel of the PET/CT images as MCL or not. The network was first trained and tested on the within-institution scans by applying five-fold cross-validation. Sensitivity and false positives (FPs) per patient were calculated for network evaluation. The network was then tested on the outside-institution scans, which were excluded from network training. For the 110 within-institution patients (85 male; median age, 58 [range: 39-84] years), the network achieved an overall median sensitivity of 88% (interquartile range [IQR]: 25%) with 15 (IQR: 12) FPs/patient. Sensitivity was dependent on lesion size and SUVmax but not on lesion location. For the 32 outside-institution patients (24 male; median age, 59 [range: 40-67] years), the network achieved a median sensitivity of 84% (IQR: 24%) with 14 (IQR: 10) FPs/patient. No significant performance difference was found between the within and outside institution scans. Therefore, DLCNN can potentially help with MCL detection on 18F-FDG PET/CT with high sensitivity and limited FPs.

Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging.

INTRODUCTION: Magnetic resonance imaging (MRI) has played an increasingly major role in the evaluation of patients with prostate cancer, although prostate MRI presents several technical challenges. Newer techniques, such as deep learning (DL), have been applied to medical imaging, leading to improvements in image quality. Our goal is to evaluate the performance of a new deep learning-based reconstruction method, "DLR" in improving image quality and mitigating artifacts, which is now commercially available as AIRTM Recon DL (GE Healthcare, Waukesha, WI). We hypothesize that applying DLR to the T2WI images of the prostate provides improved image quality and reduced artifacts. METHODS: This study included 31 patients with a history of prostate cancer that had a multiparametric MRI of the prostate with an endorectal coil (ERC) at 1.5 T or 3.0 T. Four series of T2-weighted images were generated in total: one set with the ERC signal turned on (ERC) and another set with the ERC signal turned off (Non-ERC). Each of these sets then reconstructed using two different reconstruction methods: conventional reconstruction (Conv) and DL Recon (DLR): ERCDLR, ERCConv, Non-ERCDLR, and Non-ERCConv. Three radiologists independently reviewed and scored the four sets of images for (i) image quality, (ii) artifacts, and (iii) visualization of anatomical landmarks and tumor. RESULTS: The Non-ERCDLR scored as the best series for (i) overall image quality (p < 0.001), (ii) reduced artifacts (p < 0.001), and (iii) visualization of anatomical landmarks and tumor. CONCLUSION: Prostate imaging without the use of an endorectal coil could benefit from deep learning reconstruction as demonstrated with T2-weighted imaging MRI evaluations of the prostate.