Variability and Reproducibility of 3rd-generation dual-source dynamic volume perfusion CT Parameters in Comparison to MR-perfusion Parameters in Rectal Cancer

Sudarski, Sonja; Henzler, Thomas; Floss, Teresa; Gaa, T.; Meyer, Mathias; Haubenreisser, Holger; Schoenberg, Stefan O.; Attenberger, Ulrike

doi:10.1038/s41598-018-25307-w

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…Contrarily, the linear relationship between iodinated contrast media and CT number [27] allows accurate measurement of contrast dynamics. In line with this notion, DCE-CT offers lower variability in measured tissue perfusion parameters than DCE-MRI [44]. Therefore, DCE-CT provides a good opportunity to test the conceptual validity of the compartment model in measuring renal functional parameters using DCE-CT.…”

Section: Discussionmentioning

confidence: 99%

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

et al. 2019

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Objectives To validate and adapt a modified two-compartment model, originally developed for magnetic resonance imaging, for measuring human single-kidney glomerular filtration rate (GFR) and perfusion using dynamic contrast-enhanced computed tomography (DCE-CT). Methods This prospective study was approved by the institutional review board, and written informed consent was obtained from all patients. Thirty-eight patients with essential hypertension (EH, n = 13) or atherosclerotic renal artery stenosis (ARAS, n = 25) underwent renal DCE-CT for GFR and perfusion measurement using a modified two-compartment model. Iothalamate clearance was used to measure reference total GFR, which was apportioned into single-kidney GFR by renal blood flow. Renal perfusion was also calculated using a conventional deconvolution algorithm. Validation of GFR and perfusion and inter-observer reproducibility, were conducted by using the Pearson correlation and Bland-Altman analysis. Results Both the two-compartment model and iothalamate clearance detected in ARAS patients lower GFR in the stenotic compared to the contralateral and EH kidneys. GFRs measured by DCE-CT and iothalamate clearance showed a close match (r = 0.94, P<0.001, and mean difference 2.5±12.2mL/min). Inter-observer bias and variation in model-derived GFR (r = 0.97, P<0.001; mean difference, 0.3±7.7mL/min) were minimal. Renal perfusion by deconvolution agreed well with that by the compartment model when the blood transit delay from abdominal aorta to kidney was negligible. Conclusion The proposed two-compartment model faithfully depicts contrast dynamics using DCE-CT and may provide a reliable tool for measuring human single-kidney GFR and perfusion.

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Section: Discussionmentioning

confidence: 99%

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

et al. 2019

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“…In conventional MR imaging, it is difficult to distinguish the infiltration around rectal cancer from inflammatory edema and fibrosis after neoadjuvant therapy [ 8 ], and there are sometimes large errors in evaluation. The sensitivity and specificity of conventional MR examination sequence in evaluating the invasion of mesorectal fascia after rectal cancer N-CRT were also low [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer

Liu¹,

Sun²,

Zhao³

et al. 2024

THC

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BACKGROUND: Surgery is the preferred treatment for rectal cancer, but surgical treatment alone sometimes does not achieve satisfactory results. OBJECTIVE: To explore the value of multimodal Magnetic Resonance (MR) images in evaluating T staging of rectal cancer after neoadjuvant therapy and to compare and analyze with pathological results. METHODS: This study retrospectively analyzed 232 patients with stage T3, T4 rectal cancer between January 1, 2017 and October 31, 2022. MR examination was performed within 3 days before surgery. Different MR sequences were used for mrT staging of rectal cancer after neoadjuvant therapy and compared with pathological pT staging. The accuracy of different MR sequences in evaluating T staging of rectal cancer was calculated, and the consistency between the two was analyzed by kappa test. The sensitivity, specificity, negative predictive value and positive predictive value of different MR sequences in evaluating rectal cancer invading mesorectal fascia after neoadjuvant therapy were calculated. RESULTS: A total of 232 patients with rectal cancer were included in the study. The accuracy of high-resolution T2 WI in evaluating T staging of rectal cancer after neoadjuvant therapy was 49.57%, and the Kappa value was 0.261. The accuracy of high-resolution T2WI combined with diffusion weighted imaging (DWI) in evaluating T staging of rectal cancer after neoadjuvant therapy was 61.64%, and the Kappa value was 0.411. The accuracy of high-resolution combined with DCE-MR images in evaluating T staging of rectal cancer after neoadjuvant therapy was 80.60%, and the Kappa value was 0.706. The sensitivity and specificity of high-resolution t2-weighted imaging (HR-T2WI) combined with dynamic contrast-enhancement magnetic resonance (DCE-MR) in evaluating the invasion of mesorectal fascia were 83.46% and 95.33%, respectively. CONCLUSION: Compared with HR-T2WI combined with DWI images for mrT staging of rectal cancer after neoadjuvant chemoradiotherapy (N-CRT), HR-T2WI combined with DCE-M has the highest accuracy in evaluating mrT staging of rectal cancer after neoadjuvant therapy (80.60%), and has a high consistency with pathological pT staging. It is the best sequence for T staging of rectal cancer after neoadjuvant therapy. At the same time, the sequence has high sensitivity and specificity in evaluating mesorectal fascia invasion, which can provide accurate perioperative information for the formulation of surgical plan.

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“…Considering these limitations of morphological MRI in evaluating treatment response, several studies have since investigated the potential of functional MRI like perfusion imaging [ 12 , 13 ] and, in particular, diffusion-weighted imaging (DWI) [ 14 , 15 ]. While DWI has been shown to increase diagnostic accuracy in detecting lymph node metastases and circumferential resection margin (CRM) involvement, its value in predicting treatment response is controversial [ 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy

et al. 2022

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This retrospective study aims to evaluate the generalizability of a promising state-of-the-art multitask deep learning (DL) model for predicting the response of locally advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (nCRT) using a multicenter dataset. To this end, we retrained and validated a Siamese network with two U-Nets joined at multiple layers using pre- and post-therapeutic T2-weighted (T2w), diffusion-weighted (DW) images and apparent diffusion coefficient (ADC) maps of 83 LARC patients acquired under study conditions at four different medical centers. To assess the predictive performance of the model, the trained network was then applied to an external clinical routine dataset of 46 LARC patients imaged without study conditions. The training and test datasets differed significantly in terms of their composition, e.g., T-/N-staging, the time interval between initial staging/nCRT/re-staging and surgery, as well as with respect to acquisition parameters, such as resolution, echo/repetition time, flip angle and field strength. We found that even after dedicated data pre-processing, the predictive performance dropped significantly in this multicenter setting compared to a previously published single- or two-center setting. Testing the network on the external clinical routine dataset yielded an area under the receiver operating characteristic curve of 0.54 (95% confidence interval [CI]: 0.41, 0.65), when using only pre- and post-therapeutic T2w images as input, and 0.60 (95% CI: 0.48, 0.71), when using the combination of pre- and post-therapeutic T2w, DW images, and ADC maps as input. Our study highlights the importance of data quality and harmonization in clinical trials using machine learning. Only in a joint, cross-center effort, involving a multidisciplinary team can we generate large enough curated and annotated datasets and develop the necessary pre-processing pipelines for data harmonization to successfully apply DL models clinically.

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Variability and Reproducibility of 3rd-generation dual-source dynamic volume perfusion CT Parameters in Comparison to MR-perfusion Parameters in Rectal Cancer

Cited by 6 publications

References 36 publications

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

A modified two-compartment model for measurement of renal function using dynamic contrast-enhanced computed tomography

The value of multimodality MR in T staging evaluation after neoadjuvant therapy for rectal cancer

Are We There Yet? The Value of Deep Learning in a Multicenter Setting for Response Prediction of Locally Advanced Rectal Cancer to Neoadjuvant Chemoradiotherapy

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