Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Karino, Tsukasa; Ohira, Satoshi; Kanayama, Naoyuki; Wada, Kentaro; Ikawa, Toshiki; Nitta, Yuya; Washio, Hayate; Miyazaki, Michihiko; Teshima, Teruki

doi:10.1259/bjr.20180850

Cited by 9 publications

(7 citation statements)

References 23 publications

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“…Quantitative iodine parameters have been considered a promising tool for distinguishing malignant from benign tumors (12)(13)(14)(15). Spectral curves and the effective atomic number (Z eff ) have helped identify benign and malignant tumors, histological type, and differentiation degree (16)(17)(18)(19). Quantitative dualenergy CT (DECT) analysis has distinguished invasive adenocarcinoma from noninvasive or minimally invasive adenocarcinoma among pulmonary ground-glass opacity nodules (13).…”

Section: Introductionmentioning

confidence: 99%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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Background: This study aimed to investigate the ability of quantitative parameter-derived dual-source dual-energy computed tomography (DS-DECT) combined with machine learning to distinguish between benign and malignant thyroid nodules.Methods: Patients with thyroid nodules and pathological surgical results who underwent preoperative DS-DECT were selected. Quantitative parameter-derived DS-DECT was applied to classify benign and malignant nodules. Then, machine learning and binary logistic regression analysis models were constructed using the DS-DECT quantitative parameters to distinguish between benign and malignant nodules. The receiver operating characteristic curve was used to assess the diagnostic performance. The DeLong test was used to compare the diagnostic efficacy. Results: One hundred and thirty patients with 139 confirmed thyroid nodules were involved in the study. The malignant group had a significantly higher iodine concentration nodule (arterial phase) (P=0.001), normalized iodine concentration (arterial phase) (P=0.002), iodine concentration difference (P<0.001), spectral curve slope (nonenhancement) (P=0.007), spectral curve slope (arterial phase) (P=0.001), effective atomic number (nonenhancement) (P<0.001), and effective atomic number (arterial phase) (P=0.039) than the benign group. The binary logistic regression analysis model had an AUC (area under the curve) of 0.76, a sensitivity of 0.821, and a specificity of 0.667. The machine learning model had an AUC of 0.86, a sensitivity of 0.822, specificity of 0.791 in the training cohort, an AUC of 0.84, a sensitivity of 0.727, and specificity of 0.750 in the testing cohort. Conclusions: Multiple quantitative parameters of DS-DECT combined with machine learning could differentiate between benign and malignant thyroid nodules.

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Section: Introductionmentioning

confidence: 99%

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Jiang¹,

Liu²,

Long³

et al. 2022

Quant Imaging Med Surg

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“…DECT shows greater image enhancement at low tube voltages (15). Karino et al (16) analyzed energy spectral images of virtual monochromatic images (VMI) with energy levels ranging from 40 to 140 KeV in increments of 1 keV gradient and found that a VMI of 63 KeV significantly improved the overall image quality and BM boundary display. Kraft et al (17) further compared the differences between 63 keV reconstructed VMI and 120 kV CT in BM imaging and confirmed that the image quality of VMI was significantly better than that of conventional CT, which could improve the reliability and accuracy of GTV determination in BMs.…”

Section: Dual-energy Computed Tomographybased Determinationmentioning

confidence: 99%

“…Karino et al. ( 16 ) analyzed energy spectral images of virtual monochromatic images (VMI) with energy levels ranging from 40 to 140 KeV in increments of 1 keV gradient and found that a VMI of 63 KeV significantly improved the overall image quality and BM boundary display. Kraft et al.…”

Section: Advances In Gtv Determination Of Bms Based On Multimodal Ima...mentioning

confidence: 99%

Advances in determining the gross tumor target volume for radiotherapy of brain metastases

Du,

Gong,

Liu

et al. 2024

Front. Oncol.

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Brain metastases (BMs) are the most prevalent intracranial malignant tumors in adults and are the leading cause of mortality attributed to malignant brain diseases. Radiotherapy (RT) plays a critical role in the treatment of BMs, with local RT techniques such as stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) showing remarkable therapeutic effectiveness. The precise determination of gross tumor target volume (GTV) is crucial for ensuring the effectiveness of SRS/SBRT. Multimodal imaging techniques such as CT, MRI, and PET are extensively used for the diagnosis of BMs and GTV determination. With the development of functional imaging and artificial intelligence (AI) technology, there are more innovative ways to determine GTV for BMs, which significantly improve the accuracy and efficiency of the determination. This article provides an overview of the progress in GTV determination for RT in BMs.

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“…However, for correct assessment of hyperdense findings, it is essential to discriminate iodine from other contrasts to determine malignancy. 32 Spectral CT can be very useful for the detection and assessment of lung, 127 liver, 128 bone, 129 brain, 130 and adrenal metastasis, 131 as well as local invasion of gastric 132 and lung cancer. 133 This is also the case for determination of lymph node involvement.…”

Section: Staging Of Lymph Nodes and Metastasismentioning

confidence: 99%

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual‐energy CT

Kruis

2021

J Applied Clin Med Phys

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Over the past decade, spectral or dual-energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT. In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs-at-risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

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Determination of optimal virtual monochromatic energy level for target delineation of brain metastases in radiosurgery using dual-energy CT

Cited by 9 publications

References 23 publications

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Dual-source dual-energy computed tomography-derived quantitative parameters combined with machine learning for the differential diagnosis of benign and malignant thyroid nodules

Advances in determining the gross tumor target volume for radiotherapy of brain metastases

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual‐energy CT

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