61 research outputs found
Myosteatosis predicts survival after surgery for periampullary cancer::a novel method using MRI
Background: Myosteatosis, characterized by inter-and intramyocellular fat deposition, is strongly related to poor overall survival after surgery for periampullary cancer. It is commonly assessed by calculating the muscle radiation attenuation on computed tomography (CT) scans. However, since magnetic resonance imaging (MRI) is replacing CT in routine diagnostic work-up, developing methods based on MRI is important. We developed a new method using MRI-muscle signal intensity to assess myosteatosis and compared it with CT-muscle radiation attenuation.Methods: Patients were selected from a prospective cohort of 236 surgical patients with periampullary cancer. The MRI-muscle signal intensity and CT-muscle radiation attenuation were assessed at the level of the third lumbar vertebra and related to survival.Results: Forty-seven patients were included in the study. Inter-observer variability for MRI assessment was low (R-2 = 0.94). MRI-muscle signal intensity was associated with short survival: median survival 9.8 (95%-CI: 1.5-18.1) vs. 18.2 (95%-CI: 10.7-25.8) months for high vs. low intensity, respectively (p = 0.038). Similar results were found for CT-muscle radiation attenuation (low vs. high radiation attenuation: 10.8 (95%-CI: 8.5-13.1) vs. 15.9 (95%-CI: 10.2-21.7) months, respectively; p = 0.046). MRI-signal intensity correlated negatively with CT-radiation attenuation (r=-0.614, p <0.001).Conclusions: Myosteatosis may be adequately assessed using either MRI-muscle signal intensity or CT-muscle radiation attenuation.</p
Diffusion-Weighted MRI for Selection of Complete Responders After Chemoradiation for Locally Advanced Rectal Cancer: A Multicenter Study
PURPOSE: In 10-24% of patients with rectal cancer who are treated with neoadjuvant chemoradiation, no residual tumor is found after surgery (ypT0). When accurately selected, these complete responders might be considered for less invasive treatments instead of standard surgery. So far, no imaging method has proven reliable. This study was designed to assess the accuracy of diffusion-weighted MRI (DWI) in addition to standard rectal MRI for selection of complete responders after chemoradiation. METHODS: A total of 120 patients with locally advanced rectal cancer from three university hospitals underwent chemoradiation followed by a restaging MRI (1.5T), consisting of standard T2W-MRI and DWI (b0-1000). Three independent readers first scored the standard MRI only for the likelihood of a complete response using a 5-point confidence score, after which the DWI images were added and the scoring was repeated. Histology (ypT0 vs. ypT1-4) was the standard reference. Diagnostic performance for selection of complete responders and interobserver agreement were compared for the two readings. RESULTS: Twenty-five of 120 patients had a complete response (ypT0). Areas under the ROC-curve for the three readers improved from 0.76, 0.68, and 0.58, using only standard MRI, to 0.8, 0.8, and 0.78 after addition of DWI (P = 0.39, 0.02, and 0.002). Sensitivity for selection of complete responders ranged from 0-40% on standard MRI versus 52-64% after addition of DWI. Specificity was equally high (89-98%) for both reading sessions. Interobserver agreement improved from kappa 0.2-0.32 on standard MRI to 0.51-0.55 after addition of DWI. CONCLUSIONS: Addition of DWI to standard rectal MRI improves the selection of complete responders after chemoradiation
Value of ADC measurements for nodal staging after chemoradiation in locally advanced rectal cancer—a per lesion validation study
OBJECTIVES: To evaluate the performance of diffusion-weighted MRI (DWI) in addition to T2-weighted (T2W) MRI for nodal restaging after chemoradiation in rectal cancer. METHODS: Thirty patients underwent chemoradiation followed by MRI (1.5 T) and surgery. Imaging consisted of T2W-MRI and DWI (b0, 500, 1000). On T2W-MRI, nodes were scored as benign/malignant by two independent readers (R1, R2). Mean apparent diffusion coefficient (ADC) was measured for each node. Diagnostic performance was compared for T2W-MRI, ADC and T2W+ADC, using a per lesion histological validation. RESULTS: ADC was higher for the malignant nodes (1.43 +/- 0.38 vs 1.19 +/- 0.27 *10(-3) mm(2)/s, p < 0.001). Area under the ROC curve/sensitivity/specificity were 0.88/65%/93% (R1) and 0.95/71%/91% (R2) using T2W-MRI; 0.66/53%/82% using ADC (mean of two readers); and 0.91/56%/98% (R1) and 0.96/56%/99% (R2) using T2W+ADC. There was no significant difference between T2W-MRI and T2W+ADC. Interobserver reproducibility was good for T2W-MRI (kappa0.73) and ADC (intraclass correlation coefficient 0.77). CONCLUSIONS: After chemoradiation, ADC measurements may have potential for nodal characterisation, but DWI on its own is not reliable. Addition of DWI to T2W-MRI does not improve accuracy and T2W-MRI is already sufficiently accurate
Development and multicenter validation of a multiparametric imaging model to predict treatment response in rectal cancer
Funding Information: This study has received funding from the Dutch Cancer Society (project number 10138). Publisher Copyright: © 2023, The Author(s).Objectives: To develop and validate a multiparametric model to predict neoadjuvant treatment response in rectal cancer at baseline using a heterogeneous multicenter MRI dataset. Methods: Baseline staging MRIs (T2W (T2-weighted)-MRI, diffusion-weighted imaging (DWI) / apparent diffusion coefficient (ADC)) of 509 patients (9 centres) treated with neoadjuvant chemoradiotherapy (CRT) were collected. Response was defined as (1) complete versus incomplete response, or (2) good (Mandard tumor regression grade (TRG) 1–2) versus poor response (TRG3-5). Prediction models were developed using combinations of the following variable groups: (1) Non-imaging: age/sex/tumor-location/tumor-morphology/CRT-surgery interval (2) Basic staging: cT-stage/cN-stage/mesorectal fascia involvement, derived from (2a) original staging reports, or (2b) expert re-evaluation (3) Advanced staging: variables from 2b combined with cTN-substaging/invasion depth/extramural vascular invasion/tumor length (4) Quantitative imaging: tumour volume + first-order histogram features (from T2W-MRI and DWI/ADC) Models were developed with data from 6 centers (n = 412) using logistic regression with the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, internally validated using repeated (n = 100) random hold-out validation, and externally validated using data from 3 centers (n = 97). Results: After external validation, the best model (including non-imaging and advanced staging variables) achieved an area under the curve of 0.60 (95%CI=0.48–0.72) to predict complete response and 0.65 (95%CI=0.53–0.76) to predict a good response. Quantitative variables did not improve model performance. Basic staging variables consistently achieved lower performance compared to advanced staging variables. Conclusions: Overall model performance was moderate. Best results were obtained using advanced staging variables, highlighting the importance of good-quality staging according to current guidelines. Quantitative imaging features had no added value (in this heterogeneous dataset). Clinical relevance statement: Predicting tumour response at baseline could aid in tailoring neoadjuvant therapies for rectal cancer. This study shows that image-based prediction models are promising, though are negatively affected by variations in staging quality and MRI acquisition, urging the need for harmonization. Key Points: This multicenter study combining clinical information and features derived from MRI rendered disappointing performance to predict response to neoadjuvant treatment in rectal cancer. Best results were obtained with the combination of clinical baseline information and state-of-the-art image-based staging variables, highlighting the importance of good quality staging according to current guidelines and staging templates. No added value was found for quantitative imaging features in this multicenter retrospective study. This is likely related to acquisition variations, which is a major problem for feature reproducibility and thus model generalizability.Peer reviewe
Morphological MRI criteria improve the detection of lymph node metastases in head and neck squamous cell carcinoma: multivariate logistic regression analysis of MRI features of cervical lymph nodes
Diagnostic accuracy of preoperative tests for lymph node status in endometrial cancer: a systematic review
Background: Approximately 72% of endometrial cancers are FIGO stage I at diagnosis and about 10% have lymph node metastases. An ideal diagnostic test for nodal disease would be able to prevent both overtreatment (i.e. unnecessary lymphadenectomy) and undertreatment (i.e. withholding lymphadenectomy or adjuvant postoperative treatment to patients with lymph node metastases). Objectives: In this review we compare the accuracy of preoperative tests (computed tomography, magnetic resonance imaging, positron emission tomography-computed tomography, CA-125 serum levels, and ultrasonography) for the detection of lymph node metastases in endometrial cancers with the final histopathologic diagnosis after complete pelvic and para-aortic lymphadenectomy as the gold standard. Method: A systematic search in MEDLINE (using PubMed), Embase and The Cochrane Library was performed up to 23 July 2012. Results: We found one article that met our inclusion criteria for computed tomography, none for magnetic resonance imaging, 2 for positron emission tomography/computed tomography), 2 for CA-125 and none for ultrasonography. Conclusions: Due to the lack of high-quality articles on a preoperative test for lymph node status in endometrial cancer, no proper comparison between these modalities can be made
Assessment of parametrial invasion of cervical carcinoma, the role of T2-weighted MRI and diffusion weighted imaging with or without fusion
Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models.
[This corrects the article DOI: 10.1371/journal.pone.0184197.]
Correction: Diffusion weighted imaging in patients with rectal cancer: Comparison between Gaussian and non-Gaussian models
Diffusion weighted imaging in patients with rectal cancer:Comparison between Gaussian and non-Gaussian models
The purpose of this study was to compare the performance of four diffusion models, including mono and bi-exponential both Gaussian and non-Gaussian models, in diffusion weighted imaging of rectal cancer.Nineteen patients with rectal adenocarcinoma underwent MRI examination of the rectum before chemoradiation therapy including a 7 b-value diffusion sequence (0, 25, 50, 100, 500, 1000 and 2000 s/mm2) at a 1.5T scanner. Four different diffusion models including mono- and bi-exponential Gaussian (MG and BG) and non-Gaussian (MNG and BNG) were applied on whole tumor volumes of interest. Two different statistical criteria were recruited to assess their fitting performance, including the adjusted-R2 and Root Mean Square Error (RMSE). To decide which model better characterizes rectal cancer, model selection was relied on Akaike Information Criteria (AIC) and F-ratio.All candidate models achieved a good fitting performance with the two most complex models, the BG and the BNG, exhibiting the best fitting performance. However, both criteria for model selection indicated that the MG model performed better than any other model. In particular, using AIC Weights and F-ratio, the pixel-based analysis demonstrated that tumor areas better described by the simplest MG model in an average area of 53% and 33%, respectively. Non-Gaussian behavior was illustrated in an average area of 37% according to the F-ratio, and 7% using AIC Weights. However, the distributions of the pixels best fitted by each of the four models suggest that MG failed to perform better than any other model in all patients, and the overall tumor area.No single diffusion model evaluated herein could accurately describe rectal tumours. These findings probably can be explained on the basis of increased tumour heterogeneity, where areas with high vascularity could be fitted better with bi-exponential models, and areas with necrosis would mostly follow mono-exponential behavior
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