Clinical significance of dual-energy CT-derived iodine quantification in the diagnosis of metastatic LN in colorectal cancer

Kato, Takehiro; Uehara, Kay; Ishigaki, Satoko; Nishizaki, Takashi; Arimoto, Atsuki; Nakamura, Hayato; Kamiya, Toru; Oshiro, Taihei; Ebata, Tomoki; Nagino, Masato

doi:10.1016/j.ejso.2015.08.154

Cited by 27 publications

(19 citation statements)

References 29 publications

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“…First of all, the low number of subjects in each group does not allow us to conclude any statistical difference except for the z-value measurements. Studies have demonstrated good results regarding DECTs abilities to distinguish benign from malignant tissue in different organs, but these studies had a larger sample than ours [34][35][36].…”

Section: Discussionmentioning

confidence: 73%

Differentiation between malignant and benign rectal tumors by dual-energy computed tomography - a feasibility study

2019

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Introduction: To assess the performance of Dual Energy Computed Tomography (DECT) in the differentiation between benign and malignant tumors in the rectum. Material and methods: We enrolled 8 subjects with rectal tumors suspected to be an early rectal cancer during colonoscopy. All subjects underwent Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and Endorectal Ultrasound (ERUS) for staging. Furthermore, all subjects underwent fast switching of tube voltage between 80 and 140 kVp DECT of the pelvis. The 8 subjects had histopathological verified benign adenomas after transanal endoscopic microsurgery resection (TEM). The 8 subjects were matched with 8 consecutively selected subjects with histopathologically verified malignant rectal tumors. The DECT images were analyzed to assess the difference between malignant and benign rectal tumors. All DECT images were reviewed by experienced radiologists. In each DECT scanning, we applied three regions of interest (ROIs) for the acquisition of the DECT unique quantitative parameters. The mean atomic mass (effective Z value), iodine concentration, dual energy ratio (DER) and dualenergy index (DEI) was determined in both groups. Results: The comparison of the 2 groups showed a significant difference in effective Z and a nonsignificant difference regarding iodine concentration, DER, and DEI in the two groups. Conclusion: Dual-energy CT demonstrated a difference in the mean atomic mass in benign colorectal tumors in comparison to malignant colorectal tumors.

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Section: Discussionmentioning

confidence: 73%

Differentiation between malignant and benign rectal tumors by dual-energy computed tomography - a feasibility study

2019

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“…While the exact reason for reduced iodine content within metastatic lymph nodes compared with control nodes in squamous cell carcinoma of the head and neck is not definitely known, the finding likely relates to pathologic changes within the metastatic nodes. Reduced iodine content within metastatic lymph nodes compared with that within nonmetastatic lymph nodes has also been described in lung and colorectal carcinomas ( 11 , 12 ). One possible explanation for the decreased iodine content is internal regions of necrosis that are not visibly perceptible.…”

Section: Discussionmentioning

confidence: 83%

Dual-Energy CT-Derived Iodine Content and Spectral Attenuation Analysis of Metastatic versus Nonmetastatic Lymph Nodes in Squamous Cell Carcinoma of the Oropharynx

et al. 2018

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The presence of a single nodal metastasis has significant prognostic and treatment implications for patients with head and neck cancer. This study aims to investigate whether dual-energy computed tomography (DECT)-derived iodine content and spectral attenuation curve analysis can improve detection of nodal metastasis in oropharyngeal carcinoma. Eight patients with newly diagnosed oropharyngeal squamous cell carcinoma and pathologically proven nodal metastatic disease (n = 13 metastatic nodes; n = 16 nonmetastatic nodes) who underwent contrast-enhanced DECT of the neck were retrospectively evaluated. DECT-derived iodine content (mg/mL) and monoenergetic attenuation values at 40 keV and 100 keV were obtained via circular regions of interest within metastatic and nonmetastatic cervical lymph nodes. Iodine content was significantly lower in metastatic nodes (0.96 ± 0.28 mg/mL) than in nonmetastatic nodes (1.65 ± 0.38 mg/mL; P = .002). Iodine spectral attenuation slope was significantly lower in metastatic nodes (1.33 ± 0.49 mg/mL) than in nonmetastatic nodes (1.91 ± 0.64 mg/mL; P = .015). A nodal iodine threshold of ≤1.3 mg/mL showed a sensitivity of 84.6% and a specificity of 75.0%, with an area under the curve of 0.839, P < .0001. At a threshold value of ≤1.95 for nodal spectral attenuation slope, an optimized specificity of 92.3% and specificity of 50.0% was achieved, with an area under the curve of 0.68 (P = .049). DECT-derived quantitative iodine data and spectral attenuation curves may improve the diagnostic accuracy of computed tomography for nodal metastasis in patients with squamous cell carcinoma of the oropharynx.

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“…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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Clinical significance of dual-energy CT-derived iodine quantification in the diagnosis of metastatic LN in colorectal cancer

Cited by 27 publications

References 29 publications

Differentiation between malignant and benign rectal tumors by dual-energy computed tomography - a feasibility study

Differentiation between malignant and benign rectal tumors by dual-energy computed tomography - a feasibility study

Dual-Energy CT-Derived Iodine Content and Spectral Attenuation Analysis of Metastatic versus Nonmetastatic Lymph Nodes in Squamous Cell Carcinoma of the Oropharynx

Deep‐learning‐based direct inversion for material decomposition

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