[Establishment and Verification of A Novel Predictive Model of Malignancy
for Non-solid Pulmonary Nodules].
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Abstract:
Mathematical predictive model is an effective method for preliminarily identifying the malignant pulmonary nodules. As the epidemiological trend of lung cancer changes, the detection rate of ground-glass-opacity (GGO) like early stage lung cancer is increasing rapidly, timely and proper clinical management can effectively improve the patients' prognosis. Our study aims to establish a novel predictive model of malignancy for non-solid pulmonary nodules, which would provide an objective evidence for invasive procedure and avoid unnecessary operation and the consequences.We retrospectively analyzed the basic demographics, serum tumor markers and imaging features of 362 cases of non-solid pulmonary nodule from January 2013 to April 2018. All nodules received biopsy or surgical resection, and got pathological diagnosis. Cases were randomly divided into two groups. The modeling group was used for univariate analysis and logistic regression to determine independent risk factors and establish the predictive model. Data of the validation group was used to validate the predictive value and make a comparison with other models.Of the 362 cases with non-solid pulmonary nodule, 313 (86.5%) cases were diagnosed as AAH/AIS, MIA or invasive adenocarcinoma, 49 cases were diagnosed as benign lesions. Age, serum tumor markers CEA and Cyfra21-1, consolidation tumor ratio value, lobulation and calcification were identified as independent risk factors. The AUC value of the ROC curve was 0.894, the predictive sensitivity and specificity were 87.6%, 69.7%, the positive and negative predictive value were 94.8%, 46.9%. The validated predictive value is significantly better than that of the VA, Brock and GMUFH models.Proved with high predictive sensitivity and positive predictive value, this novel model could help enable preliminarily screening of "high-risk" non-solid pulmonary nodules before biopsy or surgical excision, and minimize unnecessary invasive procedure. This model achieved preferable predictive value, might have great potential for clinical application.【中文题目：新型非实性肺小结节恶性概率预测模型 的构建与验证】 【中文摘要：背景与目的 数学预测模型是判断肺小结节恶性概率的有效工具。伴随肺癌流行病学趋势的改变，以非实性肺小结节为影像学表现的早期肺癌检出率逐年升高，准确鉴别并及时治疗干预可有效改善预后。本研究旨在专门针对非实性肺小结节构建新型恶性概率预测模型，为有创诊疗提供客观依据，并尽量避免不必要的侵袭性操作及其可能造成的严重后果。方法 回顾性分析自2013年1月-2018年4月，单中心经穿刺活检或手术切除获得明确病理诊断的362例非实性肺小结节病例资料，包括临床基本资料、血清肿瘤标记物和影像学特征等。病例分两组，应用建模组数据做单因素分析和二分类Logistic回归，判定独立危险因素，建立预测模型；应用验证组数据验证模型预测价值并与其他模型比较。结果 362例非实性肺小结节病例中，313例（86.5%）确诊为非典型腺瘤样增生（atypical adenomatous hyperplasia, AAH）/原位腺癌（adenocarcinoma in situ, AIS）、微浸润腺癌（minimally invasive adenocarcinoma, MIA）或浸润性腺癌，49例诊断为良性病变。年龄、血清肿瘤标记物癌胚抗原（carcino-embryonic antigen, CEA）和Cyfra21-1、肿瘤实性成分比值（consolidation tumor ratio, CTR）、分叶征和钙化被确定为独立危险因素。模型受试者工作曲线下面积为0.894。预测灵敏度为87.6%，特异度为69.7%，阳性预测94.8%，阴性预测值为46.9%。经验证模型预测价值显著优于VA、Brock和GMUFH模型。结论 本研究建立的新型非实性肺小结节恶性概率预测模型具备较高的诊断灵敏度和阳性预测值。经初步验证，其预测价值优于传统模型。未来经大样本验证后，可用作高危非实性肺小结节活检或手术切除前的初筛方法，具备临床应用价值。】 【中文关键词：肺小结节；肺肿瘤；预测模型；恶性概率】.Keywords:
Nodule (geology)
Univariate analysis
Solitary pulmonary nodule
Ground-glass opacity
The challenge presented by a solitary pulmonary nodule has faced physicians and patients since the advent of the chest radiograph. Is the nodule malignant or benign? When should something be done about it and what should that be? The majority of solitary nodules are benign, but the detection of a nodule may be the first and only chance for cure in the patient with lung cancer. The expanding availability and use of computed tomography are leading to increased numbers and decreased size of nodules detected. Surgical resection remains the most sensitive and specific method of analysis but introduces morbidity and mortality that may be unnecessary and avoidable. Advances in radiographic techniques have improved the ability to noninvasively identify whether a nodule is likely malignant or benign. Application of these techniques may ease the decision making and reduce the incision making.
Nodule (geology)
Solitary pulmonary nodule
Chest radiograph
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Atypical adenomatous hyperplasia
Nodule (geology)
Ground-glass opacity
Solitary pulmonary nodule
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Citations (74)
Nodule (geology)
Solitary pulmonary nodule
Histopathology
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Nodule (geology)
Ground-glass opacity
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Citations (244)
A pulmonary nodule is a single, nearly spherical, well-circumscribed pulmonary opacity up to 30 mm in diameter and surrounded by aerated lung tissue. In radiographs, pulmonary nodules may appear as solid, completely obscuring the lung parenchyma, or as subsolid, not completely obscuring adjacent tissues. A subsolid pulmonary nodule may be further subclassified as a pure ground glass nodule (pGGN) or a part solid nodule, a mixture of ground glass components and focal opacity obscuring the adjacent tissues. Guidelines for evaluation of solid pulmonary nodules are based on nodule size, recommending vigilance and non-operative management for small nodules (less than 8 mm in diameter) and diagnostic biopsy for nodules with a diameter of 8 mm or more. However, subsolid ground glass pulmonary nodules are an exception to this rule. Although small in size, persistent subsolid nodules are potentially premalignant or malignant. We present the case of a non-smoker who was found to have an incidental pulmonary pGGN. We then discuss the radiologic appearance, histology, clinical outcomes, and evaluation and management strategy of subsolid pulmonary nodules compared with solid nodules.
Nodule (geology)
Ground-glass opacity
Parenchyma
Solitary pulmonary nodule
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Nodule (geology)
Ground-glass opacity
Solitary pulmonary nodule
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Objective To evaluate spiral CT in solitary pulmonary nodule follow-up of value.Methods Spiral CT in our hospital and outside hospital 12 cases of solitary pulmonary nodules in patients with follow-up,measured nodule diameter and comparative study before and after lesion morphology,and with the final pathological findings or clinical diagnosis for comparison.Results The volume increase of 12 cases in 7 cases,the volume change in 3 cases,smaller in size in 2 cases,nodule diameter increased more than 1.25 times in 3 cases; emerging spine-like processes in 1 case,the density increased in 1 cases,leaf deepen in 2 cases; diagnosed lung cancer,7 cases of inflammatory pseudotumor in 3 cases,the nature to be determined in 2 cases.Conclusion The solitary pulmonary nodule regular follow-up,the nodule diameter measured by spiral CT predicted the doubling time of nodules aud lesions observed before and after contrast to evaluate nodule growth patterns of solitary pulmonary nodules,especially in the diagnosis of small nodules and prognostic significance.
Key words:
Solitary pulmonary nodule; computer-aided diagnosis; Tomography; X-ray computed
Nodule (geology)
Solitary pulmonary nodule
Spiral computed tomography
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A small, solitary, predominantly solid pulmonary nodule (7 x 6 mm) was found in a 63-year-old woman during a CT screening for lung cancer. After 7 years, another chest CT examination revealed that the lesion had grown to a size of 15 x 10 mm. The patient then underwent surgery to remove the nodule, because primary lung cancer was strongly suspected. The resected specimen proved to be a poorly differentiated adenocarcinoma of type D according to the criteria of Noguchi et al. The tumor doubling time (TDT) in this case was estimated to be 661 days, which was longer than in other reported cases of Noguchi type D adenocarcinoma. High-resolution CT (HRCT) of the nodule revealed a predominantly solid lesion with a polygonal shape. No further changes were observed in a one-year follow-up CT, suggesting a benign tumor. We therefore suggest that the follow-up of small, solitary pulmonary nodules is of diagnostic value.
Nodule (geology)
Solitary pulmonary nodule
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Management of the solitary pulmonary nodule confronts physicians routinely. The key to management is to send patients with a malignant nodule for resection early, at the same time avoiding unnecessary surgery for patients with a benign nodule. After obtaining a thorough history and prior radiographs, the next best step is to obtain a chest CT. Depending on the probability of malignancy, the appropriate strategy can be determined and carried out.
Nodule (geology)
Solitary pulmonary nodule
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The features of GGO nodules need to be obtained such as volume, mean, variance of Ground-Glass Opacity Nodules by boundaries of GGO nodules to judge malignant or benign of lung tumors. However, radiologists need to look for the slices including the GGO nodule in CT volume data. It is time-consuming. This paper proposes a semi-supervised learning method based on the label propagation. First, a GGO nodule was labeled in one slice. Secondly, similarities were found by comparing with the labeled GGO nodule using the values of pixels. Finally, the GGO nodule of the other slices was labeled by iteration. Experimental results showed that the approach of this paper can find slices including the GGO nodule. The approach is better than the nearest neighbor algorithm in performance.
Ground-glass opacity
Nodule (geology)
Opacity
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