Objective To explore the correlation between the quantitative and qualitative features of CT images and the invasiveness of pulmonary ground-glass nodules, providing reference value for preoperative planning of patients with ground-glass nodules. MethodsThe patients with ground-glass nodules who underwent surgical treatment and were diagnosed with pulmonary adenocarcinoma from September 2020 to July 2022 at the Third Affiliated Hospital of Kunming Medical University were collected. Based on the pathological diagnosis results, they were divided into two groups: a non-invasive adenocarcinoma group with in situ and minimally invasive adenocarcinoma, and an invasive adenocarcinoma group. Imaging features were collected, and a univariate logistic regression analysis was conducted on the clinical and imaging data of the patients. Variables with statistical difference were selected for multivariate logistic regression analysis to establish a predictive model of invasive adenocarcinoma based on independent risk factors. Finally, the sensitivity and specificity were calculated based on the Youden index. Results A total of 555 patients were collected. The were 310 patients in the non-invasive adenocarcinoma group, including 235 females and 75 males, with a meadian age of 49 (43, 58) years, and 245 patients in the invasive adenocarcinoma group, including 163 females and 82 males, with a meadian age of 53 (46, 61) years. The binary logistic regression analysis showed that the maximum diameter (OR=4.707, 95%CI 2.060 to 10.758), consolidation/tumor ratio (CTR, OR=1.027, 95%CI 1.011 to 1.043), maximum CT value (OR=1.025, 95%CI 1.004 to 1.047), mean CT value (OR=1.035, 95%CI 1.008 to 1.063), spiculation sign (OR=2.055, 95%CI 1.148 to 3.679), and vascular convergence sign (OR=2.508, 95%CI 1.345 to 4.676) were independent risk factors for the occurrence of invasive adenocarcinoma (P<0.05). Based on the independent predictive factors, a predictive model of invasive adenocarcinoma was constructed. The formula for the model prediction was: Logit(P)=–1.293+1.549×maximum diameter of lesion+0.026×CTR+0.025×maximum CT value+0.034×mean CT value+0.72×spiculation sign+0.919×vascular convergence sign. The area under the receiver operating characteristic curve of the model was 0.910 (95%CI 0.885 to 0.934), indicating that the model had good discrimination ability. The calibration curve showed that the predictive model had good calibration, and the decision analysis curve showed that the model had good clinical utility. Conclusion The predictive model combining quantitative and qualitative features of CT has a good predictive ability for the invasiveness of ground-glass nodules. Its predictive performance is higher than any single indicator.
With the development and improved availability of low-dose computed tomography (LDCT), an increasing number of patients are clinically diagnosed with lung cancer manifesting as ground-glass nodules. Although radical surgery is currently the mainstay of treatment for patients with early-stage lung cancer, traditional anatomic lobectomy and mediastinal lymph node dissection (MLND) are not ideal for every patient. Clinically, it is critical to adopt an appropriate approach to pulmonary lobectomy, determine whether it is necessary to perform MLND, establish standard criteria to define the scope of lymph node dissection, and optimize the decision-making process. Thereby avoiding over- and under-treatment of lung cancer with surgical intervention and achieving optimal results from clinical diagnosis and treatment are important issues before us.
Stage ⅠA lung adenocarcinoma presented as ground glass dominant on thin-section high-resolution CT scan is a special subtype of lung cancer. The characteristics of this subtype are quite different from the other patients, which presented as lower malignancy and better prognosis. Clinical, pathological and imaging studies have revealed that the proportion of the solid component in part-solid ground glass nodule is closely related with the pathological type and the prognosis of lung cancer. The methods for the assessment of the solid components in the ground glass nodule can be classified into three types, including subjective assessment, two dimensional measurement and three dimensional measurement. This review summarized the advantages and the limitations of these three methods. We also reviewed the clinical application of these techniques.
Whether anatomical segmentectomy can replace lobectomy in the treatment of early-stage lung cancer remains controversial. A large number of studies have been conducted for decades to explore whether pulmonary segmentectomy can treat early-stage lung cancer, which is actually to explore the indications of intentional segmentectomy. With the development of scientific researches, it is found that many characteristics affect the malignancy of lung cancer, and the different grades of each characteristic affect the prognosis of patients. It is worth exploring whether different surgical approaches can be used for early-stage lung cancer with different characteristics and different grades. This article reviews the literature and studies to discuss the advances in indications of segmentectomy for early-stage lung in terms of tumor size, consolidation-to-tumor ratio, pathological classification and tumor location, respectively. The objective of this review is to help thoracic surgeons to objectively and scientifically select the surgical method according to the clinical characteristics of early-stage lung cancer.
Objective To explore the correlation between the imaging features of peripheral ground-glass pulmonary nodules and the invasion degree of lung adenocarcinoma, and the high risk factors for infiltrating lung adenocarcinoma under thin-slice CT, which provides some reference for clinicians to plan the surgical methods of pulmonary nodules before operation and to better communicate with patients, and assists in building a clinical predictive model for invasive adenocarcinoma. MethodsClinical data of the patients with peripheral ground-glass pulmonary nodules (diameter≤3 cm) in thin-slice chest CT in the First Affiliated Hospital of Soochow University from January 2019 to January 2020 were continuously collected. All patients underwent thin-slice CT scan and thoracoscopic surgery in our center. According to the pathological examination results, they were divided into two groups: an adenocarcinoma lesions before infiltration group, and an invasive lung adenocarcinoma group. The thin-slice CT imaging parameters of pulmonary nodules were collected. The nodular diameter, mean CT value, consolidation tumor ratio (CTR), nodular shape, vacuolar sign, bronchial air sign, lobulation sign, burr sign, lesion boundary, pleural depression sign, vascular cluster sign and other clinical data were collected. Univariate and multivariate analyses were conducted to analyze the independent risk factors for the infiltrating lung adenocarcinoma, and to analyze the threshold value and efficacy of each factor for the identification of infiltrating lung adenocarcinoma. Results Finally 190 patients were enrolled. There were 110 patients in the adenocarcinoma lesions before infiltration group, including 21 males and 89 females with a mean age of 53.57±10.90 years, and 80 patients in the invasive lung adenocarcinoma group, including 31 males and 49 females with a mean age of 56.45±11.30 years. There was a statistical difference in the mean CT value, nodular diameter, CTR, gender, smoking, nodular type, nodular shape, vacuolar sign, lobulation sign, burr sign, lesion boundary, pleural depression sign, vascular cluster sign between the two groups (P＜0.05). However, there was no statistical difference between the two groups in age (P=0.081), lesion site (P=0.675), and bronchial air sign (P=0.051). Multiple logistic regression analysis showed that nodular diameter, mean CT value, CTR and lobulation sign were independent risk factors for differentiating preinvasive adenocarcinoma from invasive adenocarcinoma. At the same time, the threshold value was calculated by Youden index, indicating that the CTR was 0.45, the nodal diameter was 10.5 mm and the mean CT value was –452 Hu. Conclusion In the peripheral ground-glass pulmonary nodules, according to the patient's CT imaging features, such as mixed ground-glass nodules, irregular shapes, vacuoles, short burrs, clear boundaries, pleural indentations, and vascular clusters, have a certain reference value in the discrimination of the invasion degree of ground-glass pulmonary nodules. At the same time, it is found in this research that peripheral ground-glass pulmonary nodules with diameter greater than 10.5 mm, CT value greater than –452 Hu, CTR greater than 0.45 and lobulation sign are more likely to be infiltrating lung adenocarcinoma.
With the development of multi-slice spiral computed tomography (CT) technology and the popularization of low-dose spiral CT screening, more and more adenocarcinomas presenting ground-glass nodule (GGN) are found. Pathological invasiveness is one of the important factors affecting the choice of treatment strategy and prognosis of patients with early lung adenocarcinoma. Imaging features have attracted wide attention due to their unique advantages in predicting the pathologic invasiveness of early lung adenocarcinoma. The imaging characteristics of GGN can be used to predict the pathologic invasiveness of lung adenocarcinoma and provide evidence for clinical decisions. However, the imaging parameters and numerical values for predicting pathologic invasiveness are still controversial, which will be reviewed in this paper.
We reported a 32 years female patient in whom lung metastasis from breast cancer was presented as solitary pulmonary pure ground-glass opacity (GGO) lesion. The patient received rational preoperative examinations and surgery though the preoperative diagnosis was not accurate. Because of different therapy strategies and purposes, it is crucial to make distinction of atypical metastases from primary cancers. Thus, for patients with a history of malignancy, possible metastasis should be taken into consideration if new GGO was found on the CT. Besides this, the follow-up interval of CT should be shortened appropriately, preoperative examinations and surgical procedures should be made according to the suggestions of multidisciplinary team.
Objective To analyze the relationship between the epidermal growth factor receptor(EGFR) gene mutation and malignant pulmonary focal ground-glass lesion (fGGL). Methods We retrospectively collected the clinical data of 86 patients with surgical treatment in the department of cardiothoracic surgery of Changzheng Hospital from August 2012 to February 2015. There were 26 males and 60 females with a mean age of 56.14±10.55 years. We analyzed the relationship between the EGFR gene mutation and the related clinical data. Results Postoperative pathology showed atypical adenomatous hyperplasia (AAH) combined with focal adenocarcinoma in situ (AIS) or AIS in 10 patients, minimally invasive adenocarcinoma (MIA) in 15, and lepidic predominant adenocarcinoma (LPA) in 61. The EGFR gene mutation reports showed the exon 19 19-del mutation in 14 patients, exon 21 L858R mutation in 27, and exon 21 L861Q mutation in 2. There was no difference between the mutation of EGFR gene and clinical factors except age and smoking (P>0.05). Till June 30, 2015, all patients were alive and follow-up was 440.48±186.61 days. Conclusion The EGFR gene in patients with malignant pulmonary fGGL shows a higher mutation rate, which provides important clinical reference data for the basic research and the clinical treatment.
Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters of pulmonary ground-glass nodules (GGN) in predicting the degree of infiltration. Methods A retrospective analysis of 168 consecutive patients with 178 GGNs in our hospital from October 2019 to May 2021 was performed, including 43 males and 125 females, aged 21-78 (55.76±10.88) years. Different lesions of the same patient were analyzed as independent samples. Totally, 178 GGNs were divided into two groups, a non-invasive group (24 adenocarcinoma in situ and 77 minimally invasive adenocarcinoma), and an invasive group (77 invasive adenocarcinoma). We compared the difference of AI quantitative parameters between the two groups, and evaluated predictive valve by receiver operating characteristic curve and binary logistic regression model. Results (1) Except for the gender (P=0.115), the other parameters, such as maximal diameter [15.10 (11.50, 21.60) mm vs. 8.90 (7.65, 11.15) mm], minimum diameter [10.80 (8.85, 15.20) mm vs. 7.40 (6.10, 8.95) mm], proportion of consolidation/tumor ratio [13.58% (1.61%, 63.76%) vs. 0.00% (0.00%, 0.67%)], mean CT value [–347.00 (–492.00, –101.50) Hu vs. –598.00 (–657.50, –510.00) Hu], CT maximum value [40.00 (–40.00, 94.50) Hu vs. –218.00 (–347.00, –66.50) Hu], CT minimum value [–584.00 (–690.50, –350.00) Hu vs. –753.00 (–786.00, –700.00) Hu], danger rating (proportion of high-risk nodules, 92.2% vs. 66.3%), malignant probability [91.66% (85.62%, 94.92%) vs. 81.81% (59.98%, 90.29%)] and age (59.93±8.53 years vs. 52.04±12.10 years) were statistically significant between the invasive group and the non-invasive group (all P<0.001). (2) The highest predictive value of a single quantitative parameter was the maximal diameter (area under the curve=0.843), the lowest one was the risk classification (area under the curve=0.627), the combination of two among the three parameters (maximal diameter, mean CT value, and consolidation/tumor ratio) improved the predictive value entirely. (3) Logistic regression analysis showed that maximal diameter and mean CT value both were the independent risk factor for predicting invasive adenocarcinoma. (4) When the threshold of v was 1.775%, the diagnostic sensitivity of invasive adenocarcinoma was 0.753 and the specificity was 0.851. Conclusion AI quantitative parameters can effectively predict the degree of infiltration of GGNs and provide a reliable reference basis for clinicians.
ObjectiveTo establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). MethodsWe retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the model. ResultsA total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. ConclusionThe SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.