Metastatic and non-metastatic lymph nodes: quantification and different distribution of iodine uptake assessed by dual-energy CT

Rizzo, Stefania; Radice, Davide; Femia, Marco; Marco, Paolo De; Origgi, Daniela; Preda, Lorenzo; Barberis, Massimo; Vigorito, Raffaella; Mauri, Giovanni; Mauro, Alberto; Bellomi, Massimo

doi:10.1007/s00330-017-5015-5

Cited by 61 publications

(31 citation statements)

References 17 publications

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“…Secondly, in the current study, the ROI was placed within each lymph node and covered the nodal parenchyma as much as possible. However, some tumor deposits are merely located in the subcapsular region of the lymph node (33), and thus, some measurements based on ROIs could have exhibited some bias. Thirdly, our study was limited to the adenocarcinoma of CRC because more than 90% of CRCs are adenocarcinomas originating from epithelial cells of the colorectal mucosa.…”

Section: Discussionmentioning

confidence: 99%

A prospective study of dual-energy computed tomography for differentiating metastatic and non-metastatic lymph nodes of colorectal cancer

Qiu¹,

Hu²,

Weng³

et al. 2021

Quant Imaging Med Surg

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Background: Colorectal cancer (CRC) is the third most common malignancy worldwide, and lymph node metastasis is considered to be a risk factor for local recurrence and a poor prognosis in colorectal cancer.However, there remains a lack of reliable and non-invasive biomarkers to identify the lymph node status of CRC patients preoperatively. The purpose of this study was to explore the ability of dual-energy computed tomography (DECT) to differentiate metastatic from non-metastatic lymph nodes in colorectal cancer.Methods: Seventy-one patients with primary colorectal cancer underwent contrast-enhanced dual-energy computed tomography imaging preoperatively. The colorectal specimen was scanned postoperatively, and lymph nodes were matched to the pathology report. The following dual-energy computed tomography quantitative parameters were analyzed: dual-energy curve slope value (λ HU ), standardized iodine concentration (n△HU), iodine water ratio (nIWR), electron density value (nρ eff ), and effective atom-number (nZ), based on metastatic and non-metastatic lymph node differentiation. Also, sensitivity and specificity analyses were performed using receiver operating characteristic curves.Results: In all patients, one hundred and fifty lymph nodes, including 66 non-metastatic and 84 metastatic lymph nodes, were matched using the radiological-pathological correlation. Metastatic nodes had significantly greater λ HU , n△HU, and nIWR values than non-metastatic nodes in both the arterial and venous phases (P<0.01). The area under curve (AUC), sensitivity, and specificity were 0.80, 80%, and 66% for λ HU ; 0.86, 70%, and 95% for n△HU; and 0.88, 71%, and 95% for nIWR in the arterial phase. There was no significant difference in electron density and effective Z values between metastatic and non-metastatic lymph nodes.Conclusions: DECT quantitative parameters may help differentiate between metastatic and normal lymph nodes in patients with CRC.

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Section: Discussionmentioning

confidence: 99%

A prospective study of dual-energy computed tomography for differentiating metastatic and non-metastatic lymph nodes of colorectal cancer

Qiu¹,

Hu²,

Weng³

et al. 2021

Quant Imaging Med Surg

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“…Dual-energy CT can provide quantitative information with monochromatic spectral images, material decomposition images, spectrum curves, and effective atomic number images, based on the principle that different materials have their own characteristic X-ray absorption coefficient, combined with the technology of instantaneously switching 80/140 kVp (kilovolt peak) voltage[17]. The role of dual-energy CT in the evaluation of malignant lymph nodes in colorectal cancer, gynecological malignancies, hypopharyngeal cancer, and lung cancer has been reported[18-23]. However, there have been no dual-energy CT studies regarding lymph node assessment in HCC.…”

Section: Introductionmentioning

confidence: 99%

Dual energy computed tomography for detection of metastatic lymph nodes in patients with hepatocellular carcinoma

Zeng¹,

Yang²,

Liu³

et al. 2019

WJG

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BACKGROUND Regional lymph node metastasis in patients with hepatocellular carcinoma (HCC) is not uncommon, and is often under- or misdiagnosed. Regional lymph node metastasis is associated with a negative prognosis in patients with HCC, and surgical resection of lymph node metastasis is considered feasible and efficacious in improving the survival and prognosis. It is critical to characterize lymph node preoperatively. There is currently no consensus regarding the optimal method for the assessment of regional lymph nodes in patients with HCC. AIM To evaluate the diagnostic value of single source dual energy computed tomography (CT) in regional lymph node assessment for HCC patients. METHODS Forty-three patients with pathologically confirmed HCC who underwent partial hepatectomy with lymphadenectomy were retrospectively enrolled. All patients underwent dual-energy CT preoperatively. Regional lymph nodes ( n = 156) were divided into either a metastatic (group P , n = 52) or a non-metastasis group (group N , n = 104), and further, according to pathology, divided into an active hepatitis (group P1 , n = 34; group N1 , n = 73) and a non-active hepatitis group (group P2 , n = 18; group N2 , n = 31). The maximal short axis diameter (MSAD), iodine concentration (IC), normalized IC (NIC), and the slope of the spectral curve ( λ HU ) of each group in the arterial phase (AP), portal phase (PP), and delayed phase (DP) were analyzed. RESULTS Analysis of the MSAD, IC, NIC, and λ HU showed statistical differences between groups P and N ( P < 0.05) during all three phases. To distinguish benign from metastatic lymph nodes, the diagnostic efficacy of IC, NIC, and λ HU in the PP was the best among the three phases (AP, PP, and DP), with a sensitivity up to 81.9%, 83.9%, and 81.8%, and a specificity up to 82.4%, 84.1% and 84.1%, respectively. The diagnostic value of combined analyses of MSAD with IC, NIC, or λ HU in the PP was superior to the dual energy CT parameters alone, with a sensitivity up to 84.5%, 86.9%, and 86.2%, and a specificity up to 83.0%, 93.6% and 89.8%, respectively. Between groups P1 and P2 and groups N1 and N2 , only IC, NIC, and λ HU between groups N1 ...

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“…7), while improving quantitative accuracy (MAE for iodine concentration: Incept‐net 0.66 mgI/cc, U‐net 1.0 mgI/cc, TV‐MD 1.33 mgI/cc, and LS‐MD 1.57 mg/cc). Of note, the improvement on iodine quantification accuracy is very meaningful for the clinical dual‐energy CT tasks that request the differentiation of pathology with low iodine‐enhancement (e.g., metastatic lesions) 45–48 . The mean percent noise reduction of Incept‐net, U‐net, and TV‐MD was 96%, 96%, and 82% on HA inserts, respectively, compared to LS‐MD.…”

Section: Resultsmentioning

confidence: 94%

“…Of note, the improvement on iodine quantification accuracy is very meaningful for the clinical dual-energy CT tasks that request the differentiation of pathology with low iodineenhancement (e.g., metastatic lesions). [45][46][47][48] The mean percent noise reduction of Incept-net, U-net, and TV-MD was 96%, 96%, and 82% on HA inserts, respectively, compared to LS-MD. Similarly, the mean percent noise reduction of Incept-net, U-net, and TV-MD was 94%, 93%, and 86% on iodine inserts.…”

Section: C Quantitative Generalizabilitymentioning

confidence: 95%

Deep‐learning‐based direct inversion for material decomposition

et al. 2020

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Purpose To develop a convolutional neural network (CNN) that can directly estimate material density distribution from multi‐energy computed tomography (CT) images without performing conventional material decomposition. Methods The proposed CNN (denoted as Incept‐net) followed the general framework of encoder–decoder network, with an assumption that local image information was sufficient for modeling the nonlinear physical process of multi‐energy CT. Incept‐net was implemented with a customized loss function, including an in‐house‐designed image‐gradient‐correlation (IGC) regularizer to improve edge preservation. The network consisted of two types of customized multibranch modules exploiting multiscale feature representation to improve the robustness over local image noise and artifacts. Inserts with various densities of different materials [hydroxyapatite (HA), iodine, a blood–iodine mixture, and fat] were scanned using a research photon‐counting detector (PCD) CT with two energy thresholds and multiple radiation dose levels. The network was trained using phantom image patches only, and tested with different‐configurations of full field‐of‐view phantom and in vivo porcine images. Furthermore, the nominal mass densities of insert materials were used as the labels in CNN training, which potentially provided an implicit mass conservation constraint. The Incept‐net performance was evaluated in terms of image noise, detail preservation, and quantitative accuracy. Its performance was also compared to common material decomposition algorithms including least‐square‐based material decomposition (LS‐MD), total‐variation regularized material decomposition (TV‐MD), and U‐net‐based method. Results Incept‐net improved accuracy of the predicted mass density of basis materials compared with the U‐net, TV‐MD, and LS‐MD: the mean absolute error (MAE) of iodine was 0.66, 1.0, 1.33, and 1.57 mgI/cc for Incept‐net, U‐net, TV‐MD, and LS‐MD, respectively, across all iodine‐present inserts (2.0–24.0 mgI/cc). With the LS‐MD as the baseline, Incept‐net and U‐net achieved comparable noise reduction (both around 95%), both higher than TV‐MD (85%). The proposed IGC regularizer effectively helped both Incept‐net and U‐net to reduce image artifact. Incept‐net closely conserved the total mass densities (i.e., mass conservation constraint) in porcine images, which heuristically validated the quantitative accuracy of its outputs in anatomical background. In general, Incept‐net performance was less dependent on radiation dose levels than the two conventional methods; with approximately 40% less parameters, the Incept‐net achieved relatively improved performance than the comparator U‐net, indicating that performance gain by Incept‐net was not achieved by simply increasing network learning capacity. Conclusion Incept‐net demonstrated superior qualitative image appearance, quantitative accuracy, and lower noise than the conventional methods and less sensitive to dose change. Incept‐net generalized and performed well with unseen image structures and d...

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Metastatic and non-metastatic lymph nodes: quantification and different distribution of iodine uptake assessed by dual-energy CT

Cited by 61 publications

References 17 publications

A prospective study of dual-energy computed tomography for differentiating metastatic and non-metastatic lymph nodes of colorectal cancer

A prospective study of dual-energy computed tomography for differentiating metastatic and non-metastatic lymph nodes of colorectal cancer

Dual energy computed tomography for detection of metastatic lymph nodes in patients with hepatocellular carcinoma

Deep‐learning‐based direct inversion for material decomposition

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