A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan.

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Radiological Physics and Technology Pub Date : 2024-03-01 Epub Date: 2023-10-27 DOI:10.1007/s12194-023-00750-1
Takaomi Hanaoka, Hisanori Matoba, Jun Nakayama, Shotaro Ono, Kayoko Ikegawa, Mitsuyo Okada
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Abstract

The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.

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CT扫描检测到的不确定肺结节生长的时空图像分析。
目的是评估计算图像分类对CT扫描按时间顺序检测到的不确定肺结节(IPN)的性能。本研究共纳入483例670个肺部异常结节的患者,这些患者至少两次接受胸部薄层CT(TSCT)检查,并作为可疑结节在我院切除。每个病例的初始和最新TSCT图像中的结节区域都是手动切割的,并由Python开发环境使用开源cv2库进行处理,以测量结节变化率(NCR)。将这些NCR与临床病理因素进行统计学比较,然后对该鉴别器的临床表现进行评估。NCR显示结节一致性之间存在显著差异。就组织学亚型而言,侵袭性腺癌(ADC)的NCR与其他病变有显著区别,但与微创ADC无明显区别。仅对于癌症,NCR与局部侵袭性、p53免疫反应性和Ki67免疫反应性显著相关。关于ADC相关结节的表皮生长因子受体基因突变,NCR显示出显著的负相关。在癌症病例的分期中,NCR随着从pTis-stage 0到pT1b-stage IA2的进展而显著增加。对于临床共享决策(SDM),无论是紧急切除还是警惕等待,受试者操作特征(ROC)分析显示ROC曲线下面积为0.686。对于CT扫描检测到的小尺寸IPN,这种方法有望成为改善危及生命的癌症筛查的潜在导航器,并在手术前帮助SDM。
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来源期刊
Radiological Physics and Technology
Radiological Physics and Technology RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
3.00
自引率
12.50%
发文量
40
期刊介绍: The purpose of the journal Radiological Physics and Technology is to provide a forum for sharing new knowledge related to research and development in radiological science and technology, including medical physics and radiological technology in diagnostic radiology, nuclear medicine, and radiation therapy among many other radiological disciplines, as well as to contribute to progress and improvement in medical practice and patient health care.
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