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Li Y, Huang XT, Feng YB, Fan QR, Wang DW, Lv FJ, He XQ, Li Q. Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm. Acad Radiol 2024:S1076-6332(24)00305-2. [PMID: 38806374 DOI: 10.1016/j.acra.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/27/2024] [Accepted: 05/12/2024] [Indexed: 05/30/2024]
Abstract
RATIONALE AND OBJECTIVES We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm. MATERIALS AND METHODS The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out. RESULTS Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort. CONCLUSION The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.
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Affiliation(s)
- Yuan Li
- Department of Thoracic Surgery, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, China (Y.L.); Department of Thoracic Surgery, National Cancer Center/ National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (Y.L.)
| | - Xing-Tao Huang
- Department of Radiology, the Fifth People's Hospital of Chongqing, No. 24 Renji Road, Nan'an District, Chongqing, China (X.T.H.)
| | - Yi-Bo Feng
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District. Beijing, China (B.Y.F., R.Q.F., W.D.W.)
| | - Qian-Rui Fan
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District. Beijing, China (B.Y.F., R.Q.F., W.D.W.)
| | - Da-Wei Wang
- Institute of Research, Infervision Medical Technology Co., Ltd, 25F Building E, Yuanyang International Center, Chaoyang District. Beijing, China (B.Y.F., R.Q.F., W.D.W.)
| | - Fa-Jin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, China (F.J.L., X.Q.H., Q.L.)
| | - Xiao-Qun He
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, China (F.J.L., X.Q.H., Q.L.)
| | - Qi Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, China (F.J.L., X.Q.H., Q.L.).
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Liu J, Qi L, Xu Q, Chen J, Cui S, Li F, Wang Y, Cheng S, Tan W, Zhou Z, Wang J. A Self-supervised Learning-Based Fine-Grained Classification Model for Distinguishing Malignant From Benign Subcentimeter Solid Pulmonary Nodules. Acad Radiol 2024:S1076-6332(24)00287-3. [PMID: 38777719 DOI: 10.1016/j.acra.2024.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/02/2024] [Accepted: 05/05/2024] [Indexed: 05/25/2024]
Abstract
RATIONALE AND OBJECTIVES Diagnosing subcentimeter solid pulmonary nodules (SSPNs) remains challenging in clinical practice. Deep learning may perform better than conventional methods in differentiating benign and malignant pulmonary nodules. This study aimed to develop and validate a model for differentiating malignant and benign SSPNs using CT images. MATERIALS AND METHODS This retrospective study included consecutive patients with SSPNs detected between January 2015 and October 2021 as an internal dataset. Malignancy was confirmed pathologically; benignity was confirmed pathologically or via follow-up evaluations. The SSPNs were segmented manually. A self-supervision pre-training-based fine-grained network was developed for predicting SSPN malignancy. The pre-trained model was established using data from the National Lung Screening Trial, Lung Nodule Analysis 2016, and a database of 5478 pulmonary nodules from the previous study, with subsequent fine-tuning using the internal dataset. The model's efficacy was investigated using an external cohort from another center, and its accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were determined. RESULTS Overall, 1276 patients (mean age, 56 ± 10 years; 497 males) with 1389 SSPNs (mean diameter, 7.5 ± 2.0 mm; 625 benign) were enrolled. The internal dataset was specifically enriched for malignancy. The model's performance in the internal testing set (316 SSPNs) was: AUC, 0.964 (95% confidence interval (95%CI): 0.942-0.986); accuracy, 0.934; sensitivity, 0.965; and specificity, 0.908. The model's performance in the external test set (202 SSPNs) was: AUC, 0.945 (95% CI: 0.910-0.979); accuracy, 0.911; sensitivity, 0.977; and specificity, 0.860. CONCLUSION This deep learning model was robust and exhibited good performance in predicting the malignancy of SSPNs, which could help optimize patient management.
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Affiliation(s)
- Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Qian Xu
- Department of Computed Tomography and Magnetic Resonance, The Fourth Hospital of Hebei Medical University, Shijiazhuang, He Bei, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Shulei Cui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Yawen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
| | - Weixiong Tan
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Zhen Zhou
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing 100021, China.
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He XQ, Huang XT, Luo TY, Liu X, Li Q. The differential computed tomography features between small benign and malignant solid solitary pulmonary nodules with different sizes. Quant Imaging Med Surg 2024; 14:1348-1358. [PMID: 38415140 PMCID: PMC10895103 DOI: 10.21037/qims-23-995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 11/20/2023] [Indexed: 02/29/2024]
Abstract
Background Computed tomography (CT) has been widely known to be the first choice for the diagnosis of solid solitary pulmonary nodules (SSPNs). However, the smaller the SSPN is, the less the differential CT signs between benign and malignant SSPNs there are, which brings great challenges to their diagnosis. Therefore, this study aimed to investigate the differential CT features between small (≤15 mm) benign and malignant SSPNs with different sizes. Methods From May 2018 to November 2021, CT data of 794 patients with small SSPNs (≤15 mm) were retrospectively analyzed. SSPNs were divided into benign and malignant groups, and each group was further classified into three cohorts: cohort I (diameter ≤6 mm), cohort II (6 mm < diameter ≤8 mm), and cohort III (8 mm < diameter ≤15 mm). The differential CT features of benign and malignant SSPNs in three cohorts were identified. Multivariable logistic regression analyses were conducted to identify independent factors of benign SSPNs. Results In cohort I, polygonal shape and upper-lobe distribution differed significantly between groups (all P<0.05) and multiparametric analysis showed polygonal shape [adjusted odds ratio (OR): 12.165; 95% confidence interval (CI): 1.512-97.872; P=0.019] was the most effective variation for predicting benign SSPNs, with an area under the receiver operating characteristic curve (AUC) of 0.747 (95% CI: 0.640-0.855; P=0.001). In cohort II, polygonal shape, lobulation, pleural retraction, and air bronchogram differed significantly between groups (all P<0.05), and polygonal shape (OR: 8.870; 95% CI: 1.096-71.772; P=0.041) and the absence of pleural retraction (OR: 0.306; 95% CI: 0.106-0.883; P=0.028) were independent predictors of benign SSPNs, with an AUC of 0.778 (95% CI: 0.694-0.863; P<0.001). In cohort III, 12 CT features showed significant differences between groups (all P<0.05) and polygonal shape (OR: 3.953; 95% CI: 1.508-10.361; P=0.005); calcification (OR: 3.710; 95% CI: 1.305-10.551; P=0.014); halo sign (OR: 6.237; 95% CI: 2.838-13.710; P<0.001); satellite lesions (OR: 6.554; 95% CI: 3.225-13.318; P<0.001); and the absence of lobulation (OR: 0.066; 95% CI: 0.026-0.167; P<0.001), air space (OR: 0.405; 95% CI: 0.215-0.764; P=0.005), pleural retraction (OR: 0.297; 95% CI: 0.179-0.493; P<0.001), bronchial truncation (OR: 0.165; 95% CI: 0.090-0.303; P<0.001), and air bronchogram (OR: 0.363; 95% CI: 0.208-0.633; P<0.001) were independent predictors of benign SSPNs, with an AUC of 0.869 (95% CI: 0.840-0.897; P<0.001). Conclusions CT features vary between SSPNs with different sizes. Clarifying the differential CT features based on different diameter ranges may help to minimize ambiguities and discriminate the benign SSPNs from malignant ones.
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Affiliation(s)
- Xiao-Qun He
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xing-Tao Huang
- Department of Radiology, the Fifth People’s Hospital of Chongqing, Chongqing, China
| | - Tian-You Luo
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiao Liu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Liu J, Qi L, Wang Y, Li F, Chen J, Cui S, Cheng S, Zhou Z, Li L, Wang J. Development of a combined radiomics and CT feature-based model for differentiating malignant from benign subcentimeter solid pulmonary nodules. Eur Radiol Exp 2024; 8:8. [PMID: 38228868 DOI: 10.1186/s41747-023-00400-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 10/16/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND We aimed to develop a combined model based on radiomics and computed tomography (CT) imaging features for use in differential diagnosis of benign and malignant subcentimeter (≤ 10 mm) solid pulmonary nodules (SSPNs). METHODS A total of 324 patients with SSPNs were analyzed retrospectively between May 2016 and June 2022. Malignant nodules (n = 158) were confirmed by pathology, and benign nodules (n = 166) were confirmed by follow-up or pathology. SSPNs were divided into training (n = 226) and testing (n = 98) cohorts. A total of 2107 radiomics features were extracted from contrast-enhanced CT. The clinical and CT characteristics retained after univariate and multivariable logistic regression analyses were used to develop the clinical model. The combined model was established by associating radiomics features with CT imaging features using logistic regression. The performance of each model was evaluated using the area under the receiver-operating characteristic curve (AUC). RESULTS Six CT imaging features were independent predictors of SSPNs, and four radiomics features were selected after a dimensionality reduction. The combined model constructed by the logistic regression method had the best performance in differentiating malignant from benign SSPNs, with an AUC of 0.942 (95% confidence interval 0.918-0.966) in the training group and an AUC of 0.930 (0.902-0.957) in the testing group. The decision curve analysis showed that the combined model had clinical application value. CONCLUSIONS The combined model incorporating radiomics and CT imaging features had excellent discriminative ability and can potentially aid radiologists in diagnosing malignant from benign SSPNs. RELEVANCE STATEMENT The model combined radiomics features and clinical features achieved good efficiency in predicting malignant from benign SSPNs, having the potential to assist in early diagnosis of lung cancer and improving follow-up strategies in clinical work. KEY POINTS • We developed a pulmonary nodule diagnostic model including radiomics and CT features. • The model yielded the best performance in differentiating malignant from benign nodules. • The combined model had clinical application value and excellent discriminative ability. • The model can assist radiologists in diagnosing malignant from benign pulmonary nodules.
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Affiliation(s)
- Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Yawen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Shulei Cui
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China
| | - Zhen Zhou
- Beijing Deepwise & League of PhD Technology Co. Ltd, Beijing, China
| | - Lin Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
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Liao HF, Huang XT, Li X, Lv FJ, Luo TY, Li Q. Solitary lung adenocarcinoma: follow-up CT, pathological-molecular characteristics, and surgical prognosis for different morphological classifications. Insights Imaging 2023; 14:209. [PMID: 38010599 PMCID: PMC10682316 DOI: 10.1186/s13244-023-01563-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 11/05/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVE To investigate the dynamic changes during follow-up computed tomography (CT), histological subtypes, gene mutation status, and surgical prognosis for different morphological presentations of solitary lung adenocarcinomas (SLADC). MATERIALS AND METHODS This retrospective study compared dynamic tumor changes and volume doubling time (VDT) in 228 patients with SLADC (morphological types I-IV) who had intermittent growth during follow-ups. The correlation between the morphological classification and histological subtypes, gene mutation status, and surgical prognosis was evaluated. RESULTS Among the 228 patients, 66 (28.9%) were classified as type I, 123 (53.9%) as type II, 16 (7%) as type III, and 23 (10.1%) as type IV. Type I had the shortest VDT (254 days), followed by types IV (381 days) and III (501 days), and then type II (993 days) (p < 0.05 each). Type I had a greater proportion of solid/micropapillary-predominant pattern than type II, and the lepidic-predominant pattern was more common in type II and III than in type I (p < 0.05 each). Furthermore, type II and IV SLADCs were correlated with positive epidermal growth factor receptor mutation (p < 0.05 each). Lastly, the Kaplan-Meier curves showed that the disease-free survival was longest for patients with type II tumors, followed by those with type III and IV tumors, and then those with type I tumors (p < 0.001 each). CONCLUSION A good understanding of the natural progression and pathological-molecular characteristics of different morphological SLADC types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. CRITICAL RELEVANCE STATEMENT A good understanding of the natural progression and pathological-molecular characteristics of different morphological solitary lung adenocarcinoma types can help make accurate diagnoses, develop individual treatment strategies, and predict patient outcomes. KEY POINTS • Type I-IV solitary lung adenocarcinomas exhibit varying natural progression on serial CT scans. • Morphological classification of solitary lung adenocarcinomas predicts histological subtype, gene status, and surgical prognosis. • This classification of solitary lung adenocarcinomas may help improve diagnostic, therapeutic, and prognosticating abilities.
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Affiliation(s)
- Hong-Fan Liao
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China
| | - Xing-Tao Huang
- Department of Radiology, the Fifth People's Hospital of Chongqing, Chongqing, 400062, China
| | - Xian Li
- Department of Pathology, Chongqing Medical University, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Tian-You Luo
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Qi Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, China.
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Liu J, Qi L, Wang Y, Li F, Chen J, Cheng S, Zhou Z, Yu Y, Wang J. Diagnostic performance of a deep learning-based method in differentiating malignant from benign subcentimeter (≤10 mm) solid pulmonary nodules. J Thorac Dis 2023; 15:5475-5484. [PMID: 37969262 PMCID: PMC10636433 DOI: 10.21037/jtd-23-985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 09/08/2023] [Indexed: 11/17/2023]
Abstract
Background This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority). Methods Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists. Results The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively. Conclusions The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.
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Affiliation(s)
- Jianing Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Linlin Qi
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yawen Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fenglan Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jiaqi Chen
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Sainan Cheng
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Zhen Zhou
- Beijing Deepwise & League of PhD Technology Co., Ltd., Beijing, China
| | - Yizhou Yu
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Jianwei Wang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Jiang J, Lv FJ, Tao Y, Fu BJ, Li WJ, Lin RY, Chu ZG. Differentiation of pulmonary solid nodules attached to the pleura detected by thin-section CT. Insights Imaging 2023; 14:146. [PMID: 37697104 PMCID: PMC10495292 DOI: 10.1186/s13244-023-01504-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 08/16/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Pulmonary solid pleura-attached nodules (SPANs) are not very commonly detected and thus not well studied and understood. This study aimed to identify the clinical and CT characteristics for differentiating benign and malignant SPANs. RESULTS From January 2017 to March 2023, a total of 295 patients with 300 SPANs (128 benign and 172 malignant) were retrospectively enrolled. Between benign and malignant SPANs, there were significant differences in patients' age, smoking history, clinical symptoms, CT features, nodule-pleura interface, adjacent pleural change, peripheral concomitant lesions, and lymph node enlargement. Multivariate analysis revealed that smoking history (odds ratio [OR], 2.016; 95% confidence interval [CI], 1.037-3.919; p = 0.039), abutting the mediastinal pleura (OR, 3.325; 95% CI, 1.235-8.949; p = 0.017), nodule diameter (> 15.6 mm) (OR, 2.266; 95% CI, 1.161-4.423; p = 0.016), lobulation (OR, 8.922; 95% CI, 4.567-17.431; p < 0.001), narrow basement to pleura (OR, 6.035; 95% CI, 2.847-12.795; p < 0.001), and simultaneous hilar and mediastinal lymph nodule enlargement (OR, 4.971; 95% CI, 1.526-16.198; p = 0.008) were independent predictors of malignant SPANs, and the area under the curve (AUC) of this model was 0.890 (sensitivity, 82.0%, specificity, 77.3%) (p < 0.001). CONCLUSION In patients with a smoking history, SPANs abutting the mediastinal pleura, having larger size (> 15.6 mm in diameter), lobulation, narrow basement, or simultaneous hilar and mediastinal lymph nodule enlargement are more likely to be malignant. CRITICAL RELEVANCE STATEMENT The benign and malignant SPANs have significant differences in clinical and CT features. Understanding the differences between benign and malignant SPANs is helpful for selecting the high-risk ones and avoiding unnecessary surgical resection. KEY POINTS • The solid pleura-attached nodules (SPANs) are closely related to the pleura. • Relationship between nodule and pleura and pleural changes are important for differentiating SPANs. • Benign SPANs frequently have broad pleural thickening or embed in thickened pleura. • Smoking history and lesions abutting the mediastinal pleura are indicators of malignant SPANs. • Malignant SPANs usually have larger diameters, lobulation signs, narrow basements, and lymphadenopathy.
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Affiliation(s)
- Jin Jiang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Yang Tao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
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Kato T, Iwano S, Hanamatsu Y, Nakaguro M, Emoto R, Okado S, Sato K, Noritake O, Nakanishi K, Kadomatsu Y, Ueno H, Ozeki N, Nakamura S, Fukumoto K, Takeuchi T, Karube K, Matsui S, Chen-Yoshikawa TF. Prognostic impact of highly solid component in early-stage solid lung adenocarcinoma. Quant Imaging Med Surg 2023; 13:5641-5652. [PMID: 37711790 PMCID: PMC10498257 DOI: 10.21037/qims-23-36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 07/06/2023] [Indexed: 09/16/2023]
Abstract
Background Based on computed tomography (CT) findings of lung cancer, solid nodules have a much worse prognosis than subsolid nodules, even if the nodules are subcentimeter in size. There is, however, no systematic method for determining the prognosis of solid tumors on CT. This study aimed to discover the prognostic factor of early-stage solid lung adenocarcinoma using three-dimensional CT volumetry. Methods Patients with pathological stage I solid lung adenocarcinoma who underwent complete resection between 2007 and 2012 were selected in this retrospective study. Clinicopathological data and preoperative multidetector CT findings, such as tumor size on the two-dimensional axial image, three-dimensional tumor volume between -600 and 199 HU, and three-dimensional solid volume between 0 and 199 HU, which corresponded to highly solid components, were compared between recurrence and non-recurrence. Furthermore, these radiological values were compared to pathological invasive volume (PIV). Results During this time, 709 patients had their lung cancer completely removed. From this cohort, 90 patients with pathological stage I solid lung adenocarcinoma were selected. In addition, recurrence was found in 26 patients (28.9%). Although two-dimensional axial image, serum carcinoembryonic antigen (CEA) level, and SUVmax on 18F fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) did not differ statistically between recurrent and non-recurrent patients, three-dimensional tumor and solid tumor volume did. Multivariate analysis indicated that three-dimensional solid tumor volume [hazard ratio: 2.440; 95% confidence interval (CI): 1.110-5.361, P=0.026] and epidermal growth factor receptor (EGFR) mutation (hazard ratio: 4.307; 95% CI: 1.328-13.977, P=0.015) were significantly associated with disease-free survival (DFS). When three-dimensional tumor and solid tumor volume were compared to PIV, three-dimensional solid tumor volume (3,091 mm3 on average) showed a highly similar value with PIV (2,930 mm3 on average), whereas three-dimensional tumor volume (6,175 mm3 on average) was significantly larger than PIV (P<0.001). Conclusions In patients with early-stage solid lung adenocarcinoma, the measurement of three-dimensional solid tumor volume, which is correlated with PIV, accurately predicted the postoperative outcome.
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Affiliation(s)
- Taketo Kato
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shingo Iwano
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuki Hanamatsu
- Department of Pathology and Translational Research, Gifu University School of Medicine, Gifu, Japan
| | - Masato Nakaguro
- Department of Pathology and Laboratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryo Emoto
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shoji Okado
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keiyu Sato
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Osamu Noritake
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Keita Nakanishi
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuka Kadomatsu
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Harushi Ueno
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Naoki Ozeki
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shota Nakamura
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Koichi Fukumoto
- Department of Thoracic Surgery, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Tamotsu Takeuchi
- Department of Pathology and Translational Research, Gifu University School of Medicine, Gifu, Japan
| | - Kennosuke Karube
- Department of Pathology and Laboratory Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shigeyuki Matsui
- Department of Biostatistics, Nagoya University Graduate School of Medicine, Nagoya, Japan
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9
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Zhang CR, Wang Q, Feng H, Cui YZ, Yu XB, Shi GF. Computed-tomography-based radiomic nomogram for predicting the risk of indeterminate small (5-20 mm) solid pulmonary nodules. Diagn Interv Radiol 2023; 29:283-290. [PMID: 36987938 PMCID: PMC10679690 DOI: 10.4274/dir.2022.22395] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 04/30/2022] [Indexed: 01/14/2023]
Abstract
PURPOSE This study aims to develop a diagnostic model that combines computed tomography (CT) images and radiomic features to differentiate indeterminate small (5-20 mm) solid pulmonary nodules (SSPNs). METHODS This study retrospectively enrolled 413 patients who had had SSPNs surgically removed and histologically confirmed between 2017 and 2019. The SSPNs included solid malignant pulmonary nodules (n = 210) and benign pulmonary nodules (n = 203). The least absolute shrinkage and selection operator was used for radiomic feature selection, and random forest algorithms were used for radiomic model construction. The clinical model and nomogram were established using univariate and multivariable logistic regression analyses combined with clinical symptoms, subjective CT findings, and radiomic features. The area under the curve (AUC) of the receiver operating characteristic curve was used to evaluate the performance of the models. RESULTS The AUC for the clinical model was 0.77 in the training cohort [n = 289; 95% confidence interval (CI): 0.71-0.82; P = 0.001] and 0.75 in the validation cohort (n = 124; 95% CI: 0.66-0.83; P = 0.016). The AUCs for the nomogram were 0.92 (95% CI: 0.89-0.95; P < 0.001) and 0.85 (95% CI: 0.78-0.91; P < 0.001), respectively. The radiomic score (Rad-score), sex, pleural indentation, and age were the independent predictors that were used to build the nomogram. CONCLUSION The radiomic nomogram derived from clinical features, subjective CT signs, and the Rad-score can potentially identify the risk of indeterminate SSPNs and aid in the patient's preoperative diagnosis.
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Affiliation(s)
- Chun-Ran Zhang
- Department of Radiology, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
| | - Qi Wang
- Department of Radiology, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
| | - Hui Feng
- Department of Radiology, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
| | - Yu-Zhi Cui
- Department of Radiology, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
| | - Xiao-Bo Yu
- Siemens Healthineers Ltd., Beijing, China
| | - Gao-Feng Shi
- Department of Radiology, Hebei Medical University Fourth Affiliated Hospital and Hebei Provincial Tumor Hospital, Shijiazhuang, China
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10
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Lee JE, Do LN, Jeong WG, Lee HJ, Chae KJ, Kim YH, Park I. A Radiomics Approach on Chest CT Distinguishes Primary Lung Cancer from Solitary Lung Metastasis in Colorectal Cancer Patients. J Pers Med 2022; 12:jpm12111859. [PMID: 36579596 PMCID: PMC9695650 DOI: 10.3390/jpm12111859] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 10/31/2022] [Accepted: 11/01/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE This study utilized a radiomics approach combined with a machine learning algorithm to distinguish primary lung cancer (LC) from solitary lung metastasis (LM) in colorectal cancer (CRC) patients with a solitary pulmonary nodule (SPN). MATERIALS AND METHODS In a retrospective study, 239 patients who underwent chest computerized tomography (CT) at three different institutions between 2011 and 2019 and were diagnosed as primary LC or solitary LM were included. The data from the first institution were divided into training and internal testing datasets. The data from the second and third institutions were used as an external testing dataset. Radiomic features were extracted from the intra and perinodular regions of interest (ROI). After a feature selection process, Support vector machine (SVM) was used to train models for classifying between LC and LM. The performances of the SVM classifiers were evaluated with both the internal and external testing datasets. The performances of the model were compared to those of two radiologists who reviewed the CT images of the testing datasets for the binary prediction of LC versus LM. RESULTS The SVM classifier trained with the radiomic features from the intranodular ROI and achieved the sensitivity/specificity of 0.545/0.828 in the internal test dataset, and 0.833/0.964 in the external test dataset, respectively. The SVM classifier trained with the combined radiomic features from the intra- and perinodular ROIs achieved the sensitivity/specificity of 0.545/0.966 in the internal test dataset, and 0.833/1.000 in the external test data set, respectively. Two radiologists demonstrated the sensitivity/specificity of 0.545/0.966 and 0.636/0.828 in the internal test dataset, and 0.917/0.929 and 0.833/0.929 in the external test dataset, which were comparable to the performance of the model trained with the combined radiomics features. CONCLUSION Our results suggested that the machine learning classifiers trained using radiomics features of SPN in CRC patients can be used to distinguish the primary LC and the solitary LM with a similar level of performance to radiologists.
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Affiliation(s)
- Jong Eun Lee
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Luu Ngoc Do
- Department of Radiology, Chonnam National University, Gwangju, Korea
| | - Won Gi Jeong
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Hyo Jae Lee
- Department of Radiology, Chonnam National University Hwasun Hospital, Chonnam National University Medical School, Hwasun, Korea
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Korea
| | - Yun Hyeon Kim
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, Korea
- Department of Radiology, Chonnam National University, Gwangju, Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, Korea
- Department of Data Science, Chonnam National University, Gwangju, Korea
- Correspondence: ; Tel.: +82-62-220-5744; Fax: +82-62-226-4380
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11
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Jiang J, Lv ZM, Lv FJ, Fu BJ, Liang ZR, Chu ZG. Clinical and Computed Tomography Characteristics of Solitary Pulmonary Nodules Caused by Fungi: A Comparative Study. Infect Drug Resist 2022; 15:6019-6028. [PMID: 36267266 PMCID: PMC9576936 DOI: 10.2147/idr.s382289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 10/06/2022] [Indexed: 11/05/2022] Open
Abstract
Purpose To clarify the clinical and computed tomography (CT) indicators in distinguishing pulmonary nodules caused by fungal infection from lung cancers. Methods From January 2013 to April 2022, 68 patients with solitary fungal nodules (64 were solid and 4 were mixed ground-glass nodules) and 140 cases with solid cancerous nodules with similar size were enrolled. Their clinical characteristics and CT manifestations of the solid nodules were summarized and compared, respectively. Results Compared with patients with lung cancers, cases were younger (51.2 ± 11.5 vs 61.3 ± 10.2 years) and non-smokers (72.1% vs 57.9%) and immunocompromised (44.1% vs 17.9%) individuals were more common in patients with fungal nodules (each P < 0.05). The air crescent sign (ACS) (34.4% vs 0%), halo sign (HS) (23.4% vs 4.3%), and satellite lesions (45.3% vs 2.9%) were more frequently detected in fungal nodules than in cancerous ones (each P < 0.05). Air bronchogram similarly occurred in fungal and cancerous nodules, whereas the natural ones were more common in the former (100% vs 16.7%, P = 0.000). However, the fungal nodules had a lower enhancement degree (29.0 ± 19.2 HU vs 40.3 ± 28.3 HU, P = 0.038) and frequency of hilar and/or mediastinal lymph node enlargement (2.9% vs 14.3%, P = 0.013) compared with the cancerous nodules. Conclusion In the younger, non-smoking and immunocompromised patients, a solitary pulmonary solid nodule with ACS, HS, satellite lesions and/or natural air bronchogram but without significant enhancement, fungal infection is a probable diagnosis.
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Affiliation(s)
- Jin Jiang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhuo-ma Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Department of Radiology, The Second People’s Hospital of Yubei District, Chongqing, People’s Republic of China
| | - Fa-jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Bin-jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhang-rui Liang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China,Correspondence: Zhi-gang Chu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, Chongqing, 400016, People’s Republic of China, Tel +86 18723032809, Fax +86 23 68811487, Email
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12
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Shen C, Wu Q, Xia Q, Cao C, Wang F, Li Z, Fan L. Establishment of a malignancy and benignancy prediction model of sub-centimeter pulmonary ground-glass nodules based on the inflammation-cancer transformation theory. Front Med (Lausanne) 2022; 9:1007589. [PMID: 36275807 PMCID: PMC9581285 DOI: 10.3389/fmed.2022.1007589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/20/2022] [Indexed: 12/04/2022] Open
Abstract
Background In recent years, Chinese clinicians are frequently encountered by patients with multiple lung nodules and these intensity ground-glass nodules (GGNs) are usually small in size and some of them have no spicule sign. In addition, early lung cancer is diagnosed in large numbers of non-heavy smokers and individuals with no caner history. Obviously, the Mayo model is not applicable to these patients. The aim of the present study is to develop a new and more applicable model that can predict malignancy or benignancy of pulmonary GGNs based on the inflammation-cancer transformation theory. Materials and methods Included in this study were patients who underwent surgical resection or lung puncture biopsy of GGNs in Shanghai 10th People’s Hospital between January 1, 2018 and May 31, 2021 with the inclusion criterion of the maximum diameter of GGN < 1.0 cm. All the included patients had their pulmonary GGNs diagnosed by postoperative pathology. The patient data were analyzed to establish a prediction model and the predictive value of the model was verified. Results Altogether 100 GGN patients who met the inclusion criteria were included for analysis. Based on the results of logistic stepwise regression analysis, a mathematical predication equation was established to calculate the malignancy probability as follows: Malignancy probability rate (p) = ex/(1 + ex); p > 0.5 was considered as malignant and p ≤ 0.5 as benign, where x = 0.9650 + [0.1791 × T helper (Th) cell] + [0.2921 × mixed GGN (mGGN)] + (0.4909 × vascular convergence sign) + (0.1058 × chronic inflammation). According to this prediction model, the positive prediction rate was 73.3% and the negative prediction rate was 100% versus the positive prediction rate of 0% for the Mayo model. Conclusion By focusing on four major factors (chronic inflammation history, human Th cell, imaging vascular convergence sign and mGGNs), the present prediction model greatly improves the accuracy of malignancy or benignancy prediction of sub-centimeter pulmonary GGNs. This is a breakthrough innovation in this field.
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Affiliation(s)
- Changxing Shen
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Qiong Wu
- Liangcheng Xincun Community Health Services Center, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Qing Xia
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chuanwu Cao
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Fei Wang
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Zhuang Li
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lihong Fan
- Department of Integrated Traditional Chinese and Western Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, China,*Correspondence: Lihong Fan,
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13
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Sun K, Chen S, Zhao J, Wang B, Yang Y, Wang Y, Wu C, Sun X. Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography. Front Oncol 2021; 11:792062. [PMID: 34993146 PMCID: PMC8724915 DOI: 10.3389/fonc.2021.792062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Accepted: 11/19/2021] [Indexed: 12/26/2022] Open
Abstract
PURPOSE To establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT). METHOD A total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility. RESULTS For the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89 vs. 66%, P<0.01; 87 vs. 61%, P<0.01; 85 vs. 66%, P=0.03, in the train, validation, and test sets, respectively). In addition, while there was a slight increase in sensitivity, the specificity improved significantly by an average of 42% (the corresponding improvements in the three sets ranged from 43, 33, and 42% to 82, 78, and 84%, respectively; P<0.01 for all). CONCLUSION The CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.
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Affiliation(s)
- Ke Sun
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Shouyu Chen
- Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Jiabi Zhao
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Bin Wang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yang Yang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yin Wang
- Department of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiwen Sun
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
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14
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Zhuo Y, Zhan Y, Zhang Z, Shan F, Shen J, Wang D, Yu M. Clinical and CT Radiomics Nomogram for Preoperative Differentiation of Pulmonary Adenocarcinoma From Tuberculoma in Solitary Solid Nodule. Front Oncol 2021; 11:701598. [PMID: 34712605 PMCID: PMC8546326 DOI: 10.3389/fonc.2021.701598] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/26/2021] [Indexed: 12/15/2022] Open
Abstract
Aim To investigate clinical and computed tomography (CT) radiomics nomogram for preoperative differentiation of lung adenocarcinoma (LAC) from lung tuberculoma (LTB) in patients with pulmonary solitary solid nodule (PSSN). Materials and Methods A total of 313 patients were recruited in this retrospective study, including 96 pathologically confirmed LAC and 217 clinically confirmed LTB. Patients were assigned at random to training set (n = 220) and validation set (n = 93) according to 7:3 ratio. A total of 2,589 radiomics features were extracted from each three-dimensional (3D) lung nodule on thin-slice CT images and radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) logistic regression. The predictive nomogram was established based on radiomics and clinical features. Decision curve analysis was performed with training and validation sets to assess the clinical usefulness of the prediction model. Results A total of six clinical features were selected as independent predictors, including spiculated sign, vacuole, minimum diameter of nodule, mediastinal lymphadenectasis, sex, and age. The radiomics nomogram of lung nodules, consisting of 15 selected radiomics parameters and six clinical features showed good prediction in the training set [area under the curve (AUC), 1.00; 95% confidence interval (CI), 0.99-1.00] and validation set (AUC, 0.99; 95% CI, 0.98-1.00). The nomogram model that combined radiomics and clinical features was better than both single models (p < 0.05). Decision curve analysis showed that radiomics features were beneficial to clinical settings. Conclusion The radiomics nomogram, derived from unenhanced thin-slice chest CT images, showed favorable prediction efficacy for differentiating LAC from LTB in patients with PSSN.
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Affiliation(s)
- Yaoyao Zhuo
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yi Zhan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Zhiyong Zhang
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Institute of Big Data, Fudan University, Shanghai, China.,Fudan University, Shanghai, China
| | - Fei Shan
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.,Research Institute of Big Data, Fudan University, Shanghai, China
| | - Jie Shen
- Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China
| | - Daoming Wang
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Mingfeng Yu
- Department of Thoracic Surgery, Beilun Second People's Hospital, Zhejiang, China
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15
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Xiao YD, Lv FJ, Li WJ, Fu BJ, Lin RY, Chu ZG. Solitary Pulmonary Inflammatory Nodule: CT Features and Pathological Findings. J Inflamm Res 2021; 14:2741-2751. [PMID: 34211291 PMCID: PMC8242128 DOI: 10.2147/jir.s304431] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
Purpose Solitary pulmonary inflammatory nodules (SPINs) are frequently misdiagnosed as malignancy. We aimed to investigate CT features and pathological findings of SPINs for improving diagnosis strategies. Patients and Methods In this retrospective study, 225 and 310 consecutive patients with confirmed SPINs and lung cancerous nodules were enrolled from January 2013 to December 2020. Nodules were classified into different types based on the key CT features: I, homogeneous and well-defined nodules with smooth (Ia), coarse (Ib), or spiculated margins (Ic); II, nodules with blurred boundaries, peripheral patches, or both; III, nodules exhibiting heterogeneous density; and IV, polygonal nodules. The pathological findings of SPINs were simultaneously studied and summarized. Results Among the 225 SPINs, type I (Ia, Ib, and Ic), II, III, and IV were 137 (60.9%) (47 [20.9%], 33 [14.7%], and 57 [25.3%]), 62 (27.6%), 12 (5.3%) and 14 (6.2%), respectively. Correspondingly, those in 310 cancerous nodules were 275 (88.7%) (119 [38.4%], 70 [22.6%], and 86 [27.7%]), 20 (6.5%), 15 (4.8%), and 0, respectively. Compared with lung cancers, type I nodules were less common but type II and IV nodules were more common in SPINs (each P < 0.0001). Though the frequencies of subtype I (P = 0.095) and type III (P = 0.796) nodules were similar between two groups, their specific CT features were significantly different. The main pathological findings of each type of SPINs were most extensively identical (82.2 - 100%). Conclusion Between cancerous nodules and SPINs, differences in overall or specific CT features exist. The type II and IV nodules are highly indicative of SPINs, and each type of SPINs have almost similar pathological findings.
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Affiliation(s)
- Yun-Dan Xiao
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China
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16
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Pereira T, Freitas C, Costa JL, Morgado J, Silva F, Negrão E, de Lima BF, da Silva MC, Madureira AJ, Ramos I, Hespanhol V, Cunha A, Oliveira HP. Comprehensive Perspective for Lung Cancer Characterisation Based on AI Solutions Using CT Images. J Clin Med 2020; 10:E118. [PMID: 33396348 PMCID: PMC7796087 DOI: 10.3390/jcm10010118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/28/2020] [Accepted: 12/28/2020] [Indexed: 12/16/2022] Open
Abstract
Lung cancer is still the leading cause of cancer death in the world. For this reason, novel approaches for early and more accurate diagnosis are needed. Computer-aided decision (CAD) can be an interesting option for a noninvasive tumour characterisation based on thoracic computed tomography (CT) image analysis. Until now, radiomics have been focused on tumour features analysis, and have not considered the information on other lung structures that can have relevant features for tumour genotype classification, especially for epidermal growth factor receptor (EGFR), which is the mutation with the most successful targeted therapies. With this perspective paper, we aim to explore a comprehensive analysis of the need to combine the information from tumours with other lung structures for the next generation of CADs, which could create a high impact on targeted therapies and personalised medicine. The forthcoming artificial intelligence (AI)-based approaches for lung cancer assessment should be able to make a holistic analysis, capturing information from pathological processes involved in cancer development. The powerful and interpretable AI models allow us to identify novel biomarkers of cancer development, contributing to new insights about the pathological processes, and making a more accurate diagnosis to help in the treatment plan selection.
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Affiliation(s)
- Tania Pereira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Cláudia Freitas
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - José Luis Costa
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
- Institute for Research and Innovation in Health of the University of Porto, i3S, 4200-135 Porto, Portugal
- Institute of Molecular Pathology and Immunology of the University of Porto, IPATIMUP, 4200-135 Porto, Portugal
| | - Joana Morgado
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
| | - Francisco Silva
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
| | - Eduardo Negrão
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Beatriz Flor de Lima
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Miguel Correia da Silva
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - António J. Madureira
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
| | - Isabel Ramos
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - Venceslau Hespanhol
- Centro Hospitalar e Universitário de São João, CHUSJ, 4200-319 Porto, Portugal; (C.F.); (E.N.); (B.F.d.L.); (M.C.d.S.); (A.J.M.); (I.R.); (V.H.)
- Faculty of Medicine, University of Porto, FMUP, 4200-319 Porto, Portugal;
| | - António Cunha
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Department of Engineering, University of Trás-os-Montes and Alto Douro, UTAD, 5001-801 Vila Real, Portugal
| | - Hélder P. Oliveira
- Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, 4200-465 Porto, Portugal; (J.M.); (F.S.); (A.C.); (H.P.O.)
- Faculty of Science, University of Porto, FCUP, 4169-007 Porto, Portugal
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