Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer.

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation

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

Activin type I receptor polymorphisms and body composition in older individuals with sarcopenia—Analyses from the LACE randomised controlled trial

Background: Ageing is associated with changes in body composition including an overall reduction in muscle mass and a proportionate increase in fat mass. Sarcopenia is characterised by losses in both muscle mass and strength. Body composition and muscle strength are at least in part genetically determined, consequently polymorphisms in pathways important in muscle biology (e.g., the activin/myostatin signalling pathway) are hypothesised to contribute to the development of sarcopenia.Methods: We compared regional body composition measured by DXA with genotypes for two polymorphisms (rs10783486, minor allele frequency (MAF) =0.26 and rs2854464, MAF =0.26) in the activin 1B receptor (ACVR1B) determined by PCR in a cross-sectional analysis of DNA from 110 older individuals with sarcopenia from the LACE trial.Results: Neither muscle mass nor strength showed any significant associations with either genotype in this cohort. Initial analysis of rs10783486 showed that males with the AA/AG genotype were taller than GG males (174±7cm vs 170±5cm, p=0.023) and had higher arm fat mass, (median higher by 15%, p=0.008), and leg fat mass (median higher by 14%, p=0.042). After correcting for height, arm fat mass remained significantly higher (median higher by 4% padj=0.024). No associations (adjusted or unadjusted) were seen in females.Similar analysis of the rs2854464 allele showed a similar pattern with the presence of the minor allele (GG/AG) being associated with greater height (GG/AG = 174±7 cm vs AA = 170 ±5cm, p=0.017) and greater arm fat mass (median higher by 16%, p=0.023). Again, the difference in arm fat remained after correction for height. No similar associations were seen in females analysed alone.Conclusion: These data suggest that polymorphic variation in the ACVR1B locus could be associated with body composition in older males. The activin/myostatin pathway might offer a novel potential target to prevent fat accumulation in older individuals

Effect of perindopril or leucine on physical performance in older people with sarcopenia: the LACE randomized controlled trial

Acknowledgements: AAS, TA and MDW acknowledge support from the NIHR Newcastle Biomedical Research Centre. AA acknowledges support from the Health Services Research Unit which is core funded by the Chief Scientist Office of the Scottish Government Health and Social Care Directorate. The authors acknowledge support from the NIHR Ageing Clinical Research Network and the NHS Scotland Support for Science programme, The authors would also thank the efforts of all the research nurses and other ants to the trial, all the participants, and all the staff of the Tayside Clinical Trials Unit for their support of the trial. Funding: The LACE trial (project reference 13/53/03) is funded by the Efficacy and Mechanism Evaluation (EME) Programme, an MRC and NIHR partnership. The views expressed in this publication are those of the authors and not necessarily those of the MRC, NIHR or the Department of Health and Social Care.Peer reviewedPublisher PD

Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer.

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = -0.38, P = .03 and ρ = -0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation

Diffusion Tensor Imaging for Assessment of Response to Neoadjuvant Chemotherapy in Patients with Breast Cancer

In this study, the prognostic significance of tumor metrics derived from diffusion tensor imaging (DTI) was evaluated in patients with locally advanced breast cancer undergoing neoadjuvant therapy. DTI and contrast-enhanced magnetic resonance imaging were acquired at 1.5 T in 34 patients before treatment and after 3 cycles of taxane-based therapy (early treatment). Tumor fractional anisotropy (FA), principal eigenvalues (λ1, λ2, and λ3), and apparent diffusion coefficient (ADC) were estimated for tumor regions of interest drawn on DTI data. The association between DTI metrics and final tumor volume change was evaluated with Spearman rank correlation. DTI metrics were investigated as predictors of pathological complete response (pCR) by calculating the area under the receiver operating characteristic curve (AUC). Early changes in tumor FA and ADC significantly correlated with final tumor volume change post therapy (ρ = −0.38, P = .03 and ρ = −0.71, P < .001, respectively). Pretreatment tumor ADC was significantly lower in the pCR than in the non-pCR group (P = .04). At early treatment, patients with pCR had significantly higher percent changes of tumor λ1, λ2, λ3, and ADC than those without pCR. The AUCs for early percent changes in tumor FA and ADC were 0.60 and 0.83, respectively. The early percent changes in tumor eigenvalues and ADC were the strongest DTI-derived predictors of pCR. Although early percent change in tumor FA had a weak association with pCR, the significant correlation with final tumor volume change suggests that this metric changes with therapy and may merit further evaluation

Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography.

BackgroundQuantitative computed tomography (QCT) imaging is the basis for multiple assessments of bone quality in the proximal femur, including volumetric bone mineral density (vBMD), tissue volume, estimation of bone strength using finite element modeling (FEM), cortical bone thickness, and computational-anatomy-based morphometry assessments.MethodsHere, we present an automatic framework to perform a multi-parametric QCT quantification of the proximal femur. In this framework, the proximal femur is cropped from the bilateral hip scans, segmented using a multi-atlas based segmentation approach, and then assigned volumes of interest through the registration of a proximal femoral template. The proximal femur is then subjected to compartmental vBMD, compartmental tissue volume, FEM bone strength, compartmental surface-based cortical bone thickness, compartmental surface-based vBMD, local surface-based cortical bone thickness, and local surface-based cortical vBMD computations. Consequently, the template registrations together with vBMD and surface-based cortical bone parametric maps enable computational anatomy studies. The accuracy of the segmentation was validated against manual segmentations of 80 scans from two clinical facilities, while the multi-parametric reproducibility was evaluated using repeat scans with repositioning from 22 subjects obtained on CT imaging systems from two manufacturers.ResultsAccuracy results yielded a mean dice similarity coefficient of 0.976±0.006, and a modified Haussdorf distance of 0.219±0.071 mm. Reproducibility of QCT-derived parameters yielded root mean square coefficients of variation (CVRMS) between 0.89-1.66% for compartmental vBMD; 0.20-1.82% for compartmental tissue volume; 3.51-3.59% for FEM bone strength; 1.89-2.69% for compartmental surface-based cortical bone thickness; and 1.08-2.19% for compartmental surface-based cortical vBMD. For local surface-based assessments, mean CVRMS were between 3.45-3.91% and 2.74-3.15% for cortical bone thickness and vBMD, respectively.ConclusionsThe automatic framework presented here enables accurate and reproducible QCT multi-parametric analyses of the proximal femur. Our subjects were elderly, with scans obtained across multiple clinical sites and manufacturers, thus documenting its value for clinical trials and other multi-site studies

Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography.

BackgroundQuantitative computed tomography (QCT) imaging is the basis for multiple assessments of bone quality in the proximal femur, including volumetric bone mineral density (vBMD), tissue volume, estimation of bone strength using finite element modeling (FEM), cortical bone thickness, and computational-anatomy-based morphometry assessments.MethodsHere, we present an automatic framework to perform a multi-parametric QCT quantification of the proximal femur. In this framework, the proximal femur is cropped from the bilateral hip scans, segmented using a multi-atlas based segmentation approach, and then assigned volumes of interest through the registration of a proximal femoral template. The proximal femur is then subjected to compartmental vBMD, compartmental tissue volume, FEM bone strength, compartmental surface-based cortical bone thickness, compartmental surface-based vBMD, local surface-based cortical bone thickness, and local surface-based cortical vBMD computations. Consequently, the template registrations together with vBMD and surface-based cortical bone parametric maps enable computational anatomy studies. The accuracy of the segmentation was validated against manual segmentations of 80 scans from two clinical facilities, while the multi-parametric reproducibility was evaluated using repeat scans with repositioning from 22 subjects obtained on CT imaging systems from two manufacturers.ResultsAccuracy results yielded a mean dice similarity coefficient of 0.976±0.006, and a modified Haussdorf distance of 0.219±0.071 mm. Reproducibility of QCT-derived parameters yielded root mean square coefficients of variation (CVRMS) between 0.89-1.66% for compartmental vBMD; 0.20-1.82% for compartmental tissue volume; 3.51-3.59% for FEM bone strength; 1.89-2.69% for compartmental surface-based cortical bone thickness; and 1.08-2.19% for compartmental surface-based cortical vBMD. For local surface-based assessments, mean CVRMS were between 3.45-3.91% and 2.74-3.15% for cortical bone thickness and vBMD, respectively.ConclusionsThe automatic framework presented here enables accurate and reproducible QCT multi-parametric analyses of the proximal femur. Our subjects were elderly, with scans obtained across multiple clinical sites and manufacturers, thus documenting its value for clinical trials and other multi-site studies

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

ObjectiveWomen 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.MethodsWomen 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.ResultsA 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).ConclusionAmong 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