Histogram analysis of apparent diffusion coefficient from whole-body diffusion-weighted MRI to predict early response to chemotherapy in patients with metastatic colorectal cancer: preliminary results.

AIM: To evaluate apparent diffusion coefficient (ADC) histogram analysis parameters, acquired from whole-body diffusion-weighted magnetic resonance imaging (DW-MRI), as very early predictors of response to chemotherapy in patients with metastatic colorectal cancer (mCRC). MATERIALS AND METHODS: This was a single-institution prospective study, approved by the West Midlands-South Birmingham research ethics committee. All patients gave fully informed consent prior to imaging. Sixteen patients with histologically confirmed mCRC were enrolled to the study and 11 were successfully scanned with whole-body DW-MRI before (baseline) and 10.8±2.7 days after commencing chemotherapy (follow-up). Therapy response was assessed by RECIST 1.1. Mean ADC and histogram parameters (skewness, kurtosis, 25th, 50th, and 75th percentiles) were compared between progressors and non-progressors at baseline and follow-up. Receiver operating characteristics (ROC) analysis was performed for the statistically significant parameters. Data from metastases were also compared to normative tissue data acquired from healthy volunteers. RESULTS: Three patients had progressive disease (progressors) and eight had partial response/stable disease (non-progressors). Mean, 25th, 50th, and 75th percentiles were significantly lower for progressors at baseline (p=0.012, 0.012, 0.012 and 0.025 respectively) with areas under the ROC curves (AUC)=0.58, 0.50, 0.58 and 0.63, respectively. Skewness and kurtosis were significantly lower for non-progressors at follow-up (p=0.001 and 0.003 respectively) with AUC=0.67 and 0.79 respectively. CONCLUSION: ADC histogram analysis shows potential in discriminating progressive from non-progressive disease in patients with mCRC, who underwent whole-body DW-MRI. The technique can potentially be tested as a response assessment methodology in larger trials.

An open-source musculoskeletal model of the lumbar spine and lower limbs: a validation for movements of the lumbar spine.

Musculoskeletal models of the lumbar spine have been developed with varying levels of detail for a wide range of clinical applications. Providing consistency is ensured throughout the modelling approach, these models can be combined with other computational models and be used in predictive modelling studies to investigate bone health deterioration and the associated fracture risk. To provide precise physiological loading conditions for such predictive modelling studies, a new full-body musculoskeletal model including a detailed and consistent representation of the lower limbs and the lumbar spine was developed. The model was assessed against in vivo measurements from the literature for a range of spine movements representative of daily living activities. Comparison between model estimations and electromyography recordings was also made for a range of lifting tasks. This new musculoskeletal model will provide a comprehensive physiological mechanical environment for future predictive finite element modelling studies on bone structural adaptation. It is freely available on https://simtk.org/projects/llsm/.

Age independently affects myelin integrity as detected by magnetization transfer magnetic resonance imaging in multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is a heterogeneous disorder with a progressive course that is difficult to predict on a case-by-case basis. Natural history studies of MS have demonstrated that age influences clinical progression independent of disease duration. OBJECTIVE: To determine whether age would be associated with greater CNS injury as detected by magnetization transfer MRI. MATERIALS AND METHODS: Forty MS patients were recruited from out-patient clinics into two groups stratified by age but with similar clinical disease duration as well as thirteen controls age-matched to the older MS group. Images were segmented by automated programs and blinded readers into normal appearing white matter (NAWM), normal appearing gray matter (NAGM), and white matter lesions (WMLs) and gray matter lesions (GMLs) in the MS groups. WML and GML were delineated on T2-weighted 3D fluid-attenuated inversion recovery (FLAIR) and T1 weighted MRI volumes. Mean magnetization transfer ratio (MTR), region volume, as well as MTR histogram skew and kurtosis were calculated for each region. RESULTS: All MTR measures in NAGM and MTR histogram metrics in NAWM differed between MS subjects and controls, as expected and previously reported by several studies, but not between MS groups. However, MTR measures in the WML did significantly differ between the MS groups, in spite of no significant differences in lesion counts and volumes. CONCLUSIONS: Despite matching for clinical disease duration and recording no significant WML volume difference, we demonstrated strong MTR differences in WMLs between younger and older MS patients. These data suggest that aging-related processes modify the tissue response to inflammatory injury and its clinical outcome correlates in MS.

CT colonography: computer-assisted detection of colorectal cancer.

OBJECTIVES: Computer-aided detection (CAD) for CT colonography (CTC) has been developed to detect benign polyps in asymptomatic patients. We aimed to determine whether such a CAD system can also detect cancer in symptomatic patients. METHODS: CTC data from 137 symptomatic patients subsequently proven to have colorectal cancer were analysed by a CAD system at 4 different sphericity settings: 0, 50, 75 and 100. CAD prompts were classified by an observer as either true-positive if overlapping a cancer or false-positive if elsewhere. Colonoscopic data were used to aid matching. RESULTS: Of 137 cancers, CAD identified 124 (90.5%), 122 (89.1%), 119 (86.9%) and 102 (74.5%) at a sphericity of 0, 50, 75 and 100, respectively. A substantial proportion of cancers were detected on either the prone or supine acquisition alone. Of 125 patients with prone and supine acquisitions, 39.3%, 38.3%, 43.2% and 50.5% of cancers were detected on a single acquisition at a sphericity of 0, 50, 75 and 100, respectively. CAD detected three cancers missed by radiologists at the original clinical interpretation. False-positive prompts decreased with increasing sphericity value (median 65, 57, 45, 24 per patient at values of 0, 50, 75, 100, respectively) but many patients were poorly prepared. CONCLUSION: CAD can detect symptomatic colorectal cancer but must be applied to both prone and supine acquisitions for best performance.

Computer-aided detection for CT colonography: incremental benefit of observer training.

The purpose of this study was to investigate the incremental effect of focused training on observer performance when using computer-assisted detection (CAD) software to interpret CT colonography (CTC). Six radiologists who were relatively inexperienced with CTC interpretation underwent 1 day of focused training before reading 20 patient datasets with the assistance of CAD software (ColonCAR 1.3, Medicsight PLC). Sensitivity, specificity and interpretation times were determined and compared with previous performance when reading the same datasets but without the benefit of focused training, using the binomial exact test and Wilcoxon's signed rank test. Per-polyp sensitivity improved after training by 18% overall (95% confidence interval (CI): 14-24%, p<0.001) and was greatest for polyps of 6-9 mm (26%, 95% CI: 18-34%, p<0.001). Absolute sensitivity was 23% (9-36%), 51% (33-71%) and 74% (44-100%) for polyps of or=10 mm, respectively. Specificity fell significantly after focused training (median of 5.5 false positives per 20 datasets (interquartile range (IQR): 4-6) post-training vs median of 2.5 (IQR: 1-5) pre-training, p = 0.03). Interpretation time also increased significantly after training (from a median of 9.3 min (IQR: 9.3-14.5 min) to a median of 17.1 min (IQR: 15.4-19.4 min), p = 0.03). In conclusion, one day of training increases observer polyp sensitivity when using CAD for CTC at the expense of increased reporting time and reduction in specificity.

Virtual colonoscopy: effect of computer-assisted detection (CAD) on radiographer performance.

AIM: To investigate the effect of a virtual colonoscopy (VC) computed-assisted detection (CAD) system on polyp detection by trained radiographers. MATERIALS AND METHODS: Four radiographers trained in VC interpretation and utilization of CAD systems read a total of 62 endoscopically validated VC examinations containing 150 polyps (size range 5-50mm) in four sessions, recording any polyps found and the examination interpretation time, first without and then with the addition of CAD as a "second reader". After a temporal separation of 6 weeks to reduce recall bias, VC examinations were re-read using "concurrent reader" CAD. Interpretation times, polyp detection, and number of false-positives were compared between the different reader paradigms using paired t and paired exact tests. RESULTS: Overall, use of "second reader" CAD significantly improved polyp detection by 12% (p<0.001, CI 6%,17%)) from 48 to 60%. There was no significant improvement using CAD as a concurrent reader (p=0.20; difference of 7%, CI -3%, 16%) and no significant overall difference in recorded false-positives with second reader or concurrent CAD paradigms compared with unassisted reading (p=0.25 and 0.65, respectively). The mean interpretation time was 21.7 min for unassisted reading, 29.6 (p<0.001) min for second reader and 19.1 min (p=0.12) for concurrent reading paradigms. CONCLUSION: CAD, when used as a second reader, can significantly improve radiographer reading performance with only a moderate increase in interpretation times.

CT colonography: automatic measurement of polyp diameter compared with manual assessment - an in-vivo study.

AIM: To investigate whether automated diameter assessment was feasible for CT colonography. MATERIALS AND METHODS: Two experienced observers independently measured the maximum diameter of 50 polyps (colonoscopic reference size range 5-12 mm) from colonography datasets using conventionally placed software callipers and a variety of two-dimensional (2D) computed tomography (CT) window settings (colon, abdominal, bone, lung), and also three-dimensional (3D) perspective rendering. Polyps were also measured using automated polyp-segmentation software. Agreement between observers and with the colonoscopic reference measurement was determined using Bland-Altman, Wilcoxon, and Mann-Whitney U analyses. RESULTS: Inter-observer agreement was similar for all window displays: mean difference in millimetres (SD difference; 95% limits of agreement) ranged from 0 (1.7, -3.3, 3.3) for 2D colon to -1.1mm (1.6, -4.3, 2.0) for 3D, compared with -0.5 (2.09, -4.6, 3.6) for automated measurement. When compared to colonoscopy, the largest discrepancy occurred using the 3D display (mean difference 1.3mm, 2.5mm for each observer). There was also a significant difference between estimates and reference size when using the 2D abdominal and 3D displays (p=0.03, <0.001). CONCLUSION: Automated polyp measurement is possible in vivo. Automated and conventional methods have comparable inter-observer agreement. The greatest measurement error is encountered when using a 3D display for estimates of diameter.