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 &lt;0.001).Conclusions: Myosteatosis may be adequately assessed using either MRI-muscle signal intensity or CT-muscle radiation attenuation.</p

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

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

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

Natural Language Processing in Dutch Free Text Radiology Reports:Challenges in a Small Language Area Staging Pulmonary Oncology

Reports are the standard way of communication between the radiologist and the referring clinician. Efforts are made to improve this communication by, for instance, introducing standardization and structured reporting. Natural Language Processing (NLP) is another promising tool which can improve and enhance the radiological report by processing free text. NLP as such adds structure to the report and exposes the information, which in turn can be used for further analysis. This paper describes pre-processing and processing steps and highlights important challenges to overcome in order to successfully implement a free text mining algorithm using NLP tools and machine learning in a small language area, like Dutch. A rule-based algorithm was constructed to classify T-stage of pulmonary oncology from the original free text radiological report, based on the items tumor size, presence and involvement according to the 8th TNM classification system. PyContextNLP, spaCy and regular expressions were used as tools to extract the correct information and process the free text. Overall accuracy of the algorithm for evaluating T-stage was 0,83 in the training set and 0,87 in the validation set, which shows that the approach in this pilot study is promising. Future research with larger datasets and external validation is needed to be able to introduce more machine learning approaches and perhaps to reduce required input efforts of domain-specific knowledge. However, a hybrid NLP approach will probably achieve the best results. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10278-020-00327-z) contains supplementary material, which is available to authorized users

Myosteatosis in nonalcoholic fatty liver disease: An exploratory study.

BACKGROUND AND AIM: Insulin resistance (IR) plays a central role in the complex pathophysiology of nonalcoholic fatty liver disease (NAFLD). IR is linked to fat infiltration in skeletal muscle (myosteatosis) and loss of skeletal muscle mass and function (sarcopenia). The clinical significance of myosteatosis in NAFLD is not well investigated. In this exploratory study we aimed to investigate the association between myosteatosis and NAFLD related hepatic and systemic variables in a well characterized NAFLD cohort. METHODS: We cross-sectionally studied forty-five NAFLD patients. The muscle fat fraction (MFF) was measured with chemical shift gradient echo MRI. In addition, the hepatic fat fraction (MRI), liver stiffness (FibroScan) and appendicular skeletal muscle mass (Dual-energy X-ray absorptiometry) were analyzed. RESULTS: The median hepatic fat fraction was 15.64% (IQR 12.05-25.13) and significant (F2-F3) liver fibrosis (liver stiffness ≥7kPa) was diagnosed in 18 NAFLD patients (40%). MFF was not correlated with hepatic fat fraction (r=-0.035, P=0.823) and did not differ between subjects with or without significant fibrosis (P=0.980). No patient was diagnosed with sarcopenia based on the skeletal muscle mass index. In a linear regression model, anthropometric parameters, including body mass index (BMI) (P=0.018) and total body fat percentage (P=0.005), were positively associated with MFF while no association with insulin resistance (HOMA-IR) was observed. CONCLUSION: Myosteatosis did not correlate with the degree of hepatic steatosis or fibrosis in this well characterized NAFLD cohort, but was positively correlated with total body fat percentage and BMI.status: Published onlin