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Cheng DO, Khaw CR, McCabe J, Pennycuick A, Nair A, Moore DA, Janes SM, Jacob J. Predicting histopathological features of aggressiveness in lung cancer using CT radiomics: a systematic review. Clin Radiol 2024:S0009-9260(24)00248-4. [PMID: 38853080 DOI: 10.1016/j.crad.2024.04.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 04/29/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE To examine the accuracy of CT radiomics to predict histopathological features of aggressiveness in lung cancer using a systematic review of test accuracy studies. METHODS Data sources searched included Medline, Embase, Web of Science, and Cochrane Library from up to 3 November 2023. Included studies reported test accuracy of CT radiomics models to detect the presence of: spread through air spaces (STAS), predominant adenocarcinoma pattern, adenocarcinoma grade, lymphovascular invasion (LVI), tumour infiltrating lymphocytes (TIL) and tumour necrosis, in patients with lung cancer. The primary outcome was test accuracy. Two reviewers independently assessed articles for inclusion and assessed methodological quality using the QUality Assessment of Diagnostic Accuracy Studies-2 tool. A single reviewer extracted data, which was checked by a second reviewer. Narrative data synthesis was performed. RESULTS Eleven studies were included in the final analysis. 10/11 studies were in East Asian populations. 4/11 studies investigated STAS, 6/11 investigated adenocarcinoma invasiveness or growth pattern, and 1/11 investigated LVI. No studies investigating TIL or tumour necrosis met inclusion criteria. Studies were of generally mixed to poor methodological quality. Reported accuracies for radiomic models ranged from 0.67 to 0.94. CONCLUSION Due to the high risk of bias and concerns regarding applicability, the evidence is inconclusive as to whether radiomic features can accurately predict prognostically important histopathological features of cancer aggressiveness. Many studies were excluded due to lack of external validation. Rigorously conducted prospective studies with sufficient external validity will be required for radiomic models to play a role in improving lung cancer outcomes.
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Affiliation(s)
- D O Cheng
- University College London, Department of Respiratory Medicine, UK
| | - C R Khaw
- University College London, Department of Respiratory Medicine, UK
| | - J McCabe
- University College London, Department of Respiratory Medicine, UK
| | - A Pennycuick
- University College London, Department of Respiratory Medicine, UK
| | - A Nair
- University College London, Department of Radiology, UK
| | - D A Moore
- University College London, Department of Pathology, UK
| | - S M Janes
- University College London, Department of Respiratory Medicine, UK
| | - J Jacob
- University College London, Department of Respiratory Medicine, UK; University College London, Department of Radiology, UK.
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Xing X, Li L, Sun M, Zhu X, Feng Y. A combination of radiomic features, clinic characteristics, and serum tumor biomarkers to predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Ther Adv Respir Dis 2024; 18:17534666241249168. [PMID: 38757628 PMCID: PMC11102675 DOI: 10.1177/17534666241249168] [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: 10/19/2023] [Accepted: 04/05/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Invasive lung adenocarcinoma with MPP/SOL components has a poor prognosis and often shows a tendency to recurrence and metastasis. This poor prognosis may require adjustment of treatment strategies. Preoperative identification is essential for decision-making for subsequent treatment. OBJECTIVE This study aimed to preoperatively predict the probability of MPP/SOL components in lung adenocarcinomas by a comprehensive model that includes radiomics features, clinical characteristics, and serum tumor biomarkers. DESIGN A retrospective case control, diagnostic accuracy study. METHODS This study retrospectively recruited 273 patients (males: females, 130: 143; mean age ± standard deviation, 63.29 ± 10.03 years; range 21-83 years) who underwent resection of invasive lung adenocarcinoma. Sixty-one patients (22.3%) were diagnosed with lung adenocarcinoma with MPP/SOL components. Radiomic features were extracted from CT before surgery. Clinical, radiomic, and combined models were developed using the logistic regression algorithm. The clinical and radiomic signatures were integrated into a nomogram. The diagnostic performance of the models was evaluated using the area under the curve (AUC). Studies were scored according to the Radiomics Quality Score and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines. RESULTS The radiomics model achieved the best AUC values of 0.858 and 0.822 in the training and test cohort, respectively. Tumor size (T_size), solid tumor size (ST_size), consolidation-to-tumor ratio (CTR), years of smoking, CYFRA 21-1, and squamous cell carcinoma antigen were used to construct the clinical model. The clinical model achieved AUC values of 0.741 and 0.705 in the training and test cohort, respectively. The nomogram showed higher AUCs of 0.894 and 0.843 in the training and test cohort, respectively. CONCLUSION This study has developed and validated a combined nomogram, a visual tool that integrates CT radiomics features with clinical indicators and serum tumor biomarkers. This innovative model facilitates the differentiation of micropapillary or solid components within lung adenocarcinoma and achieves a higher AUC, indicating superior predictive accuracy.
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Affiliation(s)
- Xiaowei Xing
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Liangping Li
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Mingxia Sun
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Yue Feng
- Cancer Center, Department of Radiology, Zhejiang Provincial People’s Hospital, (Affiliated People’s Hospital), Hangzhou Medical College, Hangzhou, Zhejiang, China
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Choi W, Liu CJ, Alam SR, Oh JH, Vaghjiani R, Humm J, Weber W, Adusumilli PS, Deasy JO, Lu W. Preoperative 18F-FDG PET/CT and CT radiomics for identifying aggressive histopathological subtypes in early stage lung adenocarcinoma. Comput Struct Biotechnol J 2023; 21:5601-5608. [PMID: 38034400 PMCID: PMC10681940 DOI: 10.1016/j.csbj.2023.11.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/02/2023] [Accepted: 11/03/2023] [Indexed: 12/02/2023] Open
Abstract
Lung adenocarcinoma (ADC) is the most common non-small cell lung cancer. Surgical resection is the primary treatment for early-stage lung ADC while lung-sparing surgery is an alternative for non-aggressive cases. Identifying histopathologic subtypes before surgery helps determine the optimal surgical approach. Predominantly solid or micropapillary (MIP) subtypes are aggressive and associated with a higher likelihood of recurrence and metastasis and lower survival rates. This study aims to non-invasively identify these aggressive subtypes using preoperative 18F-FDG PET/CT and diagnostic CT radiomics analysis. We retrospectively studied 119 patients with stage I lung ADC and tumors ≤ 2 cm, where 23 had aggressive subtypes (18 solid and 5 MIPs). Out of 214 radiomic features from the PET/CT and CT scans and 14 clinical parameters, 78 significant features (3 CT and 75 PET features) were identified through univariate analysis and hierarchical clustering with minimized feature collinearity. A combination of Support Vector Machine classifier and Least Absolute Shrinkage and Selection Operator built predictive models. Ten iterations of 10-fold cross-validation (10 ×10-fold CV) evaluated the model. A pair of texture feature (PET GLCM Correlation) and shape feature (CT Sphericity) emerged as the best predictor. The radiomics model significantly outperformed the conventional predictor SUVmax (accuracy: 83.5% vs. 74.7%, p = 9e-9) and identified aggressive subtypes by evaluating FDG uptake in the tumor and tumor shape. It also demonstrated a high negative predictive value of 95.6% compared to SUVmax (88.2%, p = 2e-10). The proposed radiomics approach could reduce unnecessary extensive surgeries for non-aggressive subtype patients, improving surgical decision-making for early-stage lung ADC patients.
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Affiliation(s)
- Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Chia-Ju Liu
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sadegh Riyahi Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Raj Vaghjiani
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - John Humm
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wolfgang Weber
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Prasad S. Adusumilli
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
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Li Y, Liu J, Yang X, Xu F, Wang L, He C, Lin L, Qing H, Ren J, Zhou P. Radiomic and quantitative-semantic models of low-dose computed tomography for predicting the poorly differentiated invasive non-mucinous pulmonary adenocarcinoma. LA RADIOLOGIA MEDICA 2023; 128:191-202. [PMID: 36637740 DOI: 10.1007/s11547-023-01591-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 01/04/2023] [Indexed: 01/14/2023]
Abstract
PURPOSE Poorly differentiated invasive non-mucinous pulmonary adenocarcinoma (IPA), based on the novel grading system, was related to poor prognosis, with a high risk of lymph node metastasis and local recurrence. This study aimed to build the radiomic and quantitative-semantic models of low-dose computed tomography (LDCT) to preoperatively predict the poorly differentiated IPA in nodules with solid component, and compare their diagnostic performance with radiologists. MATERIALS AND METHODS A total of 396 nodules from 388 eligible patients, who underwent LDCT scan within 2 weeks before surgery and were pathologically diagnosed with IPA, were retrospectively enrolled between July 2018 and December 2021. Nodules were divided into two independent cohorts according to scanners: primary cohort (195 well/moderate differentiated and 64 poorly differentiated) and validation cohort (104 well/moderate differentiated and 33 poorly differentiated). The radiomic and quantitative-semantic models were built using multivariable logistic regression. The diagnostic performance of the models and radiologists was assessed by area under curve (AUC) of receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity. RESULTS No significant differences of AUCs were found between the radiomic and quantitative-semantic model in primary and validation cohorts (0.921 vs. 0.923, P = 0.846 and 0.938 vs. 0.911, P = 0.161). Both the models outperformed three radiologists in the validation cohort (all P < 0.05). CONCLUSIONS The radiomic and quantitative-semantic models of LDCT, which could identify the poorly differentiated IPA with excellent diagnostic performance, might provide guidance for therapeutic decision making, such as choosing appropriate surgical method or adjuvant chemotherapy.
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Affiliation(s)
- Yong Li
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jieke Liu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Xi Yang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Fuyang Xu
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Lu Wang
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Changjiu He
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Libo Lin
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Haomiao Qing
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Jing Ren
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China
| | - Peng Zhou
- Department of Radiology, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, No.55, Section 4, South Renmin Road, Chengdu, 610041, Sichuan, China.
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Liu Y, Chang Y, Zha X, Bao J, Wu Q, Dai H, Hu C. A Combination of Radiomic Features, Imaging Characteristics, and Serum Tumor Biomarkers to Predict the Possibility of the High-Grade Subtypes of Lung Adenocarcinoma. Acad Radiol 2022; 29:1792-1801. [PMID: 35351366 DOI: 10.1016/j.acra.2022.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Revised: 02/27/2022] [Accepted: 02/28/2022] [Indexed: 01/26/2023]
Abstract
RATIONALE AND OBJECTIVES Lung adenocarcinomas (LADC) containing high-grade subtypes have a poorer prognosis. And some studies have shown that high-grade subtypes have been identified as an independent predictor of local recurrence in patients treated with limited resection. The aim of this study was to construct a combined model based on radiomic features, imaging characteristics and serum tumor biomarkers to predict the possibility of preoperative high-grade subtypes. MATERIALS AND METHODS 156 patients with LADC were retrospectively recruited in this study. These patients were randomly divided into training and validation cohorts. Radiomics features and imaging characteristics were extracted from plain CT images. A nomogram was developed in a training cohort by univariate and multivariate logistic analysis, and its performance was evaluated by receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) in the training and validation cohorts. RESULTS A total of 1316 radiomic features were extracted from the lesions in plain chest CT images. After applying the mRMR algorithm and the LASSO regression, 4 features were retained. Based on these radiomic features, Radiomic score (Radscore) was calculated for each patient. Spiculation, air bronchogram sign, CYFRA 21-1 and Radscore had been used in the construction of the combined model. The AUC of the combined model was respectively 0.88 (95% CI, 0.82-0.95) and 0.94 (95% CI, 0.86-1.00) in the training and validation cohorts. CONCLUSION The combined model based on CT images and serum tumor biomarkers, can predict the high-grade subtypes of LADC in a non-invasive manner, which may influence individual treatment planning, such as the choice of surgical approach and postoperative adjuvant therapy.
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Affiliation(s)
- Yuanqing Liu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China
| | - Yue Chang
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China
| | - Xinyi Zha
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China
| | - Jiayi Bao
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China
| | - Qian Wu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China
| | - Hui Dai
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, P.R. China; Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, Jiangsu Province, P.R. China
| | - Chunhong Hu
- Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu Province, P.R. China; Institute of Medical Imaging, Soochow University, Suzhou, Jiangsu Province, P.R. China; Suzhou Key Laboratory of Intelligent Medicine and Equipment, Suzhou, Jiangsu Province, P.R. China.
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Li D, Yu H, Hu J, Li S, Yan Y, Li S, Sun L, Jiang G, Hou L, Zhang L, Zhang P. Comparative profiling of single-cell transcriptome reveals heterogeneity of tumor microenvironment between solid and acinar lung adenocarcinoma. J Transl Med 2022; 20:423. [PMID: 36138435 PMCID: PMC9502652 DOI: 10.1186/s12967-022-03620-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Accepted: 09/01/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The diversity of histologic composition reflects the inter- and intra-tumor heterogeneity of lung adenocarcinomas (LUADs) macroscopically. Insights into the oncological characteristics and tumor microenvironment (TME) of different histologic subtypes of LUAD at the single-cell level can help identify potential therapeutic vulnerabilities and combinational approaches to improve the survival of LUAD patients. METHODS Through comparative profiling of cell communities defined by scRNA-seq data, we characterized the TME of LUAD samples of distinct histologic subtypes, with relevant results further confirmed in multiple bulk transcriptomic, proteomic datasets and an independent immunohistochemical validation cohort. RESULTS We find that the hypoxic and acidic situation is the worst in the TME of solid LUADs compared to other histologic subtypes. Besides, the tumor metabolic preferences vary across histologic subtypes and may correspondingly impinge on the metabolism and function of immune cells. Remarkably, tumor cells from solid LUADs upregulate energy and substance metabolic activities, particularly the folate-mediated one-carbon metabolism and the key gene MTHFD2, which could serve as a potential therapeutic target. Additionally, ubiquitination modifications may also be involved in the progression of histologic patterns. Immunologically, solid LUADs are characterized by a predominance of exhausted T cells and immunosuppressive myeloid cells, where the hypoxic, acidified and nutrient-deprived TME has a non-negligible impact. Discrepancies in stromal cell function, evidenced by varying degrees of stromal remodeling and fibrosis, may also contribute to the specific immune phenotype of solid LUADs. CONCLUSIONS Overall, our research proposes several potential entry points to improve the immunosuppressive TME of solid LUADs, thereby synergistically potentiating their immunotherapeutic efficacy, and may provide precise therapeutic strategies for LUAD patients of distinct histologic subtype constitution.
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Affiliation(s)
- Dianke Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Huansha Yu
- Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China
| | - Junjie Hu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Shaoling Li
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Yilv Yan
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Shuangyi Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Liangdong Sun
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
| | - Lele Zhang
- Central Laboratory, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China.
| | - Peng Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, No. 507 Zhengmin Road, Shanghai, 200433, China. .,Department of Thoracic Surgery, The First Affiliated Hospital of Shihezi University Medical College, Shihezi, 832000, Xinjiang, China.
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Zhao M, Kluge K, Papp L, Grahovac M, Yang S, Jiang C, Krajnc D, Spielvogel CP, Ecsedi B, Haug A, Wang S, Hacker M, Zhang W, Li X. Multi-lesion radiomics of PET/CT for non-invasive survival stratification and histologic tumor risk profiling in patients with lung adenocarcinoma. Eur Radiol 2022; 32:7056-7067. [PMID: 35896836 DOI: 10.1007/s00330-022-08999-7] [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: 05/11/2022] [Revised: 06/15/2022] [Accepted: 06/27/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES This study investigates the ability of machine learning (ML) models trained on clinical data and 2-deoxy-2-[18F]fluoro-D-glucose(FDG) positron emission tomography/computed tomography (PET/CT) radiomics to predict overall survival (OS), tumor grade (TG), and histologic growth pattern risk (GPR) in lung adenocarcinoma (LUAD) patients. METHODS A total of 421 treatment-naive patients with histologically-proven LUAD and available FDG PET/CT imaging were retrospectively included. Four cohorts were assessed for predicting 4-year OS (n = 276), 3-year OS (n = 280), TG (n = 298), and GPR (n = 265). FDG-avid lesions were delineated, and 2082 radiomics features were extracted and combined with endpoint-specific clinical parameters. ML models were built for the prediction of 4-year OS (M4OS), 3-year OS (M3OS), tumor grading (MTG), and histologic growth pattern risk (MGPR). A 100-fold Monte Carlo cross-validation with 80:20 training to validation split was employed as a performance evaluation for all models. The association between the M4OS and M3OS predictions with OS was assessed by the Kaplan-Meier survival analysis. RESULTS The area under the receiver operator characteristics curve (AUC) was the highest for M4OS (AUC 0.88, 95% confidence interval (CI) 86.7-88.7), followed by M3OS (AUC 0.84, CI 82.9-84.9), while MTG and MGPR performed equally well (AUC 0.76, CI 74.4-77.9, CI 74.6-78, respectively). Predictions of M4OS (hazard ratio (HR) -2.4, CI -2.47 to -1.64, p < 0.05) and M3OS (HR -2.36, CI -2.79 to -1.93, p < 0.05) were independently associated with OS. CONCLUSION ML models are able to predict long-term survival outcomes in LUAD patients with high accuracy. Furthermore, histologic grade and predominant growth pattern risk can be predicted with satisfactory accuracy. KEY POINTS • Machine learning models trained on pre-therapeutic PET/CT radiomics enable highly accurate long-term survival prediction of patients with lung adenocarcinoma. • Highly accurate survival predictions are achieved in lung adenocarcinoma patients despite heterogenous histologies and treatment regimens. • Radiomic machine learning models are able to predict lung adenocarcinoma tumor grade and histologic growth pattern risk with satisfactory accuracy.
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Affiliation(s)
- Meixin Zhao
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Kilian Kluge
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Laszlo Papp
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Marko Grahovac
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria
| | - Shaomin Yang
- Department of Pathology, Peking University Health Science Center, Beijing, China
| | - Chunting Jiang
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China
| | - Denis Krajnc
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Clemens P Spielvogel
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Boglarka Ecsedi
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Alexander Haug
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.,Christian Doppler Laboratory for Applied Metabolomics (CDLAM), Vienna, Austria
| | - Shiwei Wang
- Evomics Medical Technology Co., Ltd., Shanghai, China
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria
| | - Weifang Zhang
- Department of Nuclear Medicine, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing, 100191, People's Republic of China.
| | - Xiang Li
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Währinger Gürtel 18-20, Floor 3L, 1090, Vienna, Austria.
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8
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Chen LW, Yang SM, Chuang CC, Wang HJ, Chen YC, Lin MW, Hsieh MS, Antonoff MB, Chang YC, Wu CC, Pan T, Chen CM. Solid Attenuation Components Attention Deep Learning Model to Predict Micropapillary and Solid Patterns in Lung Adenocarcinomas on Computed Tomography. Ann Surg Oncol 2022; 29:7473-7482. [PMID: 35789301 DOI: 10.1245/s10434-022-12055-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 06/08/2022] [Indexed: 11/18/2022]
Abstract
BACKGROUND High-grade adenocarcinoma subtypes (micropapillary and solid) treated with sublobar resection have an unfavorable prognosis compared with those treated with lobectomy. We investigated the potential of incorporating solid attenuation component masks with deep learning in the prediction of high-grade components to optimize surgical strategy preoperatively. METHODS A total of 502 patients with pathologically confirmed high-grade adenocarcinomas were retrospectively enrolled between 2016 and 2020. The SACs attention DL model was developed to apply solid-attenuation-component-like subregion masks (tumor area ≥ - 190 HU) to guide the DL model for predicting high-grade subtypes. The SACA-DL was assessed using 5-fold cross-validation and external validation in the training and testing sets, respectively. The performance, which was evaluated using the area under the curve (AUC), was compared between SACA-DL and the DL model without SACs attention (DLwoSACs), the prior radiomics model, or the model based on the consolidation/tumor (C/T) diameter ratio. RESULTS We classified 313 and 189 patients into training and testing cohorts, respectively. The SACA-DL achieved an AUC of 0.91 for the cross-validation, which was significantly superior to those of the DLwoSACs (AUC = 0.88; P = 0.02), prior radiomics model (AUC = 0.85; P = 0.004), and C/T ratio (AUC = 0.84; P = 0.002). An AUC of 0.93 was achieved for external validation in the SACA-DL and was significantly better than those of the DLwoSACs (AUC = 0.89; P = 0.04), prior radiomics model (AUC = 0.85; P < 0.001), and C/T ratio (AUC = 0.85; P < 0.001). CONCLUSIONS The combination of solid-attenuation-component-like subregion masks with the DL model is a promising approach for the preoperative prediction of high-grade adenocarcinoma subtypes.
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Affiliation(s)
- Li-Wei Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shun-Mao Yang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, Zhubei City, Hsinchu County, Taiwan
| | - Ching-Chia Chuang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Hao-Jen Wang
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan
| | - Yi-Chang Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.,Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Mara B Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Carol C Wu
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Tinsu Pan
- Department of Imaging Physics, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Chung-Ming Chen
- Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan.
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9
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Li Z, Wu W, Pan X, Li F, Zhu Q, He Z, Chen L. Serum tumor markers level and their predictive values for solid and micropapillary components in lung adenocarcinoma. Cancer Med 2022; 11:2855-2864. [PMID: 35289087 PMCID: PMC9302275 DOI: 10.1002/cam4.4645] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 01/13/2022] [Accepted: 01/19/2022] [Indexed: 12/23/2022] Open
Abstract
Background This study aims to reveal the serum tumor marker (STM) levels in lung adenocarcinoma (LUAD) histological subtypes and evaluate their values in predicting the solid and micropapillary components (SMC). Methods We retrospectively analyzed 3100 invasive LUAD patients between January 2017 and December 2020. Associations between preoperative STMs (CEA, CYFRA21‐1, CA199, CA724, NSE, AFP) and LUAD subtypes were evaluated. Multivariate regression analyses were used to determine the independent predictors. Predictive models for SMC were constructed and AUC (area under the curve) was calculated. Results CEA and CYFRA21‐1 levels differed across the LUAD histological subtypes, with the SPA (solid‐predominant adenocarcinoma) having the highest level and the LPA (lepidic‐predominant adenocarcinoma) harboring the lowest level (p <0.001). Tumors with SMC also had higher CEA and CYFRA21‐1 levels than those absence of SMC. Gender, tumor size, CEA, Ki‐67, EGFR mutation (solid components only), and tumor differentiation were significantly independently associated with the containing of SMC. Patients were split into two data sets (training set: 2017–2019 and validation set: 2020). The model with gender and tumor size yielded an AUC of 0.723 (training set) and 0.704 (validation set) for the solid component. Combination of CEA, gender, and tumor size led to a significant increase in the predictive accuracy (training set: 0.771, p = 0.009; validation set: 0.747, p = 0.034). The AUC of the model for micropapillary component with only gender and tumor size was 0.699 and 0.711 in the training set and validation set, respectively. Integration of CEA with gender and tumor size significantly improved the predictive performance with an AUC of 0.746 (training set, p = 0.045) and 0.753 (validation set, p <0.001). Conclusion Serum CEA and CYFRA21‐1 varied considerably according to LUAD histological subtypes. The combination of serum CEA and other factors showed prominent values in predicting the SMC.
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Affiliation(s)
- Zhihua Li
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weibing Wu
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xianglong Pan
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Fang Li
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Quan Zhu
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhicheng He
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liang Chen
- Department of Thoracic Surgery, Jiangsu Province Hospital, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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10
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Novel Genetic Prognostic Signature for Lung Adenocarcinoma Identified by Differences in Gene Expression Profiles of Low- and High-Grade Histological Subtypes. Biomolecules 2022; 12:biom12020160. [PMID: 35204661 PMCID: PMC8961607 DOI: 10.3390/biom12020160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/05/2022] [Accepted: 01/15/2022] [Indexed: 02/05/2023] Open
Abstract
The 2021 WHO classification proposed a pattern-based grading system for early-stage invasive non-mucinous lung adenocarcinoma. Lung adenocarcinomas with high-grade patterns have poorer outcomes than those with lepidic-predominant patterns. This study aimed to establish genetic prognostic signatures by comparing differences in gene expression profiles between low- and high-grade adenocarcinomas. Twenty-six (9 low- and 17 high-grade adenocarcinomas) patients with histologically “near-pure” patterns (predominant pattern comprising >70% of tumor areas) were selected retrospectively. Using RNA sequencing, gene expression profiles between the low- and high-grade groups were analyzed, and genes with significantly different expression levels between these two groups were selected for genetic prognostic signatures. In total, 196 significant candidate genes (164 upregulated and 32 upregulated in the high- and low-grade groups, respectively) were identified. After intersection with The Cancer Genome Atlas–Lung Adenocarcinoma prognostic genes, three genes, exonuclease 1 (EXO1), family with sequence similarity 83, member A (FAM83A), and disks large-associated protein 5 (DLGAP5), were identified as prognostic gene signatures. Two independent cohorts were used for validation, and the areas under the time-dependent receiver operating characteristic were 0.784 and 0.703 in the GSE31210 and GSE30219 cohorts, respectively. Our result showed the feasibility and accuracy of this novel three-gene prognostic signature for predicting the clinical outcomes of lung adenocarcinoma.
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11
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Li M, Ruan Y, Feng Z, Sun F, Wang M, Zhang L. Preoperative CT-Based Radiomics Combined With Nodule Type to Predict the Micropapillary Pattern in Lung Adenocarcinoma of Size 2 cm or Less: A Multicenter Study. Front Oncol 2021; 11:788424. [PMID: 34926304 PMCID: PMC8674565 DOI: 10.3389/fonc.2021.788424] [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: 10/02/2021] [Accepted: 11/15/2021] [Indexed: 11/16/2022] Open
Abstract
Purpose To construct an optimal radiomics model for preoperative prediction micropapillary pattern (MPP) in adenocarcinoma (ADC) of size ≤ 2 cm, nodule type was used for stratification to construct two radiomics models based on high-resolution computed tomography (HRCT) images. Materials and Methods We retrospectively analyzed patients with pathologically confirmed ADC of size ≤ 2 cm who presented to three hospitals. Patients presenting to the hospital with the greater number of patients were included in the training set (n = 2386) and those presenting to the other two hospitals were included in the external validation set (n = 119). HRCT images were used for delineation of region of interest of tumor and extraction of radiomics features; dimensionality reduction was performed for the features. Nodule type was used to stratify the data and the random forest method was used to construct two models for preoperative prediction MPP in ADC of size ≤ 2 cm. Model 1 included all nodule types and model 2 included only solid nodules. The receiver operating characteristic curve was used to assess the prediction performance of the two models and independent validation was used to assess its generalizability. Results Both models predicted ADC with MPP preoperatively. The area under the curve (AUC) of prediction performance of models 1 and 2 were 0.91 and 0.78, respectively. The prediction performance of model 2 was lower than that of model 1. The AUCs in the external validation set were 0.81 and 0.72, respectively. The DeLong test showed statistically significant differences between the training and validation sets in model 1 (p = 0.0296) with weak generalizability. There was no statistically significant difference between the training and validation sets in model 2 (p = 0.2865) with some generalizability. Conclusion Nodule type is an important factor that affects the performance of radiomics predictor model for MPP with ADC of size ≤ 2 cm. The radiomics prediction model constructed based on solid nodules alone, can be used to evaluate MPP and may contribute to proper surgical planning in patients with ADC of size ≤ 2 cm.
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Affiliation(s)
- Meirong Li
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Yachao Ruan
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Zhan Feng
- Department of Radiology, The First Affiliated Hospital, Zhejiang University, School of Medicine, Hangzhou, China
| | - Fangyu Sun
- Department of Radiology, Xiaoshan Hospital of Traditional Chinese Medicine, Hangzhou, China
| | - Minhong Wang
- Department of Radiology, First Affiliated Hospital of Wannan Medical College, Wuhu, China
| | - Liang Zhang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
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12
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Chen LW, Lin MW, Hsieh MS, Yang SM, Wang HJ, Chen YC, Chen HY, Hu YH, Lee CE, Chen JS, Chang YC, Chen CM. Radiomic values from high-grade subtypes to predict spread through air spaces in lung adenocarcinoma. Ann Thorac Surg 2021; 114:999-1006. [PMID: 34454902 DOI: 10.1016/j.athoracsur.2021.07.075] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 07/19/2021] [Accepted: 07/20/2021] [Indexed: 11/01/2022]
Abstract
BACKGROUND We aimed to establish a radiomic prediction model for tumor spread through air spaces (STAS) in lung adenocarcinoma using radiomic values from high-grade subtypes (solid and micropapillary). METHODS We retrospectively reviewed 327 patients with lung adenocarcinoma from two institutes (Cohort 1: 227 patients; Cohort 2: 100 patients) between March 2017 and March 2019. STAS was identified in 113 (34.6%) patients. A high-grade likelihood prediction model was constructed based on a historical cohort of 82 patients with "near-pure" pathological subtype. The STAS prediction model based on the patch-wise mechanism identified the high-grade likelihood area for each voxel within the internal border of the tumor. STAS presence was indirectly predicted by a volume percentage threshold of the high-grade likelihood area. Performance was evaluated by receiver operating curve analysis with 10-repetition, 3-fold cross-validation in Cohort 1, and was individually tested in Cohort 2. RESULTS Overall, 227 patients (STAS-positive: 77 [33.9%]) were enrolled for cross-validation (Cohort 1) while 100 (STAS-positive: 36 [36.0%]) underwent individual testing (Cohort 2). The gray level co-occurrence matrix (variance) and histogram (75th percentile) features were selected to construct the high-grade likelihood prediction model, which was used as the STAS prediction model. The proposed model achieved good performance in Cohort 1 with an area under the curve, sensitivity, and specificity, of 81.44%, 86.75%, and 62.60%, respectively, and correspondingly, in Cohort 2, they were 83.16%, 83.33%, and 63.90%, respectively. CONCLUSIONS The proposed computed tomography-based radiomic prediction model could help guide preoperative prediction of STAS in early-stage lung adenocarcinoma and relevant surgeries.
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Affiliation(s)
- Li-Wei Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Shun-Mao Yang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec. 1, Shengyi Rd., Zhubei City, Hsinchu 302 Taiwan
| | - Hao-Jen Wang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Yi-Chang Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan; Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Hsin-Yi Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Yu-Hsuan Hu
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Chi-En Lee
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan; Department of Surgical Oncology, National Taiwan University Cancer Center, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei 100, Taiwan
| | - Chung-Ming Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan.
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13
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Chen LW, Yang SM, Wang HJ, Chen YC, Lin MW, Hsieh MS, Song HL, Ko HJ, Chen CM, Chang YC. Prediction of micropapillary and solid pattern in lung adenocarcinoma using radiomic values extracted from near-pure histopathological subtypes. Eur Radiol 2021; 31:5127-5138. [PMID: 33389033 DOI: 10.1007/s00330-020-07570-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/01/2020] [Accepted: 11/26/2020] [Indexed: 12/25/2022]
Abstract
OBJECTIVES Near-pure lung adenocarcinoma (ADC) subtypes demonstrate strong stratification of radiomic values, providing basic information for pathological subtyping. We sought to predict the presence of high-grade (micropapillary and solid) components in lung ADCs using quantitative image analysis with near-pure radiomic values. METHODS Overall, 103 patients with lung ADCs of various histological subtypes were enrolled for 10-repetition, 3-fold cross-validation (cohort 1); 55 were enrolled for testing (cohort 2). Histogram and textural features on computed tomography (CT) images were assessed based on the "near-pure" pathological subtype data. Patch-wise high-grade likelihood prediction was performed for each voxel within the tumour region. The presence of high-grade components was then determined based on a volume percentage threshold of the high-grade likelihood area. To compare with quantitative approaches, consolidation/tumour (C/T) ratio was evaluated on CT images; we applied radiological invasiveness (C/T ratio > 0.5) for the prediction. RESULTS In cohort 1, patch-wise prediction, combined model (C/T ratio and patch-wise prediction), whole-lesion-based prediction (using only the "near-pure"-based prediction model), and radiological invasiveness achieved a sensitivity and specificity of 88.00 ± 2.33% and 75.75 ± 2.82%, 90.00 ± 0.00%, and 77.12 ± 2.67%, 66.67% and 90.41%, and 90.00% and 45.21%, respectively. The sensitivity and specificity, respectively, for cohort 2 were 100.0% and 95.35% using patch-wise prediction, 100.0% and 95.35% using combined model, 75.00% and 95.35% using whole-lesion-based prediction, and 100.0% and 69.77% using radiological invasiveness. CONCLUSION Using near-pure radiomic features and patch-wise image analysis demonstrated high levels of sensitivity and moderate levels of specificity for high-grade ADC subtype-detecting. KEY POINTS • The radiomic values extracted from lung adenocarcinoma with "near-pure" histological subtypes provide useful information for high-grade (micropapillary and solid) components detection. • Using near-pure radiomic features and patch-wise image analysis, high-grade components of lung adenocarcinoma can be predicted with high sensitivity and moderate specificity. • Using near-pure radiomic features and patch-wise image analysis has potential role in facilitating the prediction of the presence of high-grade components in lung adenocarcinoma prior to surgical resection.
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Affiliation(s)
- Li-Wei Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan
| | - Shun-Mao Yang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.,Department of Surgery, National Taiwan University Hospital Biomedical Park Hospital, No. 2, Sec.1, Shengyi Rd., Zhubei City, Hsinchu County, 302, Taiwan
| | - Hao-Jen Wang
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan
| | - Yi-Chang Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.,Department of Medical Imaging, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital and National Taiwan University College of Medicine, No. 1, Sec. 1, Jen - Ai Rd., Taipei, 100, Taiwan
| | - Hsiang-Lin Song
- Department of Pathology, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan
| | - Huan-Jang Ko
- Department of Surgery, National Taiwan University Hospital, Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu, 300, Taiwan
| | - Chung-Ming Chen
- Institute of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan.
| | - Yeun-Chung Chang
- Department of Medical Imaging, National Taiwan University Hospital, 7 Chung-Shan South Road, Taipei, Taiwan.
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14
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Wang X, Zhang L, Yang X, Tang L, Zhao J, Chen G, Li X, Yan S, Li S, Yang Y, Kang Y, Li Q, Wu N. Deep learning combined with radiomics may optimize the prediction in differentiating high-grade lung adenocarcinomas in ground glass opacity lesions on CT scans. Eur J Radiol 2020; 129:109150. [PMID: 32604042 DOI: 10.1016/j.ejrad.2020.109150] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 06/09/2020] [Accepted: 06/21/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE Adenocarcinoma (ADC) is the most common histological subtype of lung cancers in non-small cell lung cancer (NSCLC) in which ground glass opacifications (GGOs) found on computed tomography (CT) scans are the most common lesions. However, the presence of a micropapillary or a solid component is identified as an independent predictor of prognosis, suggesting a more extensive resection. The purpose of our study is to explore imaging phenotyping using a method combining radiomics with deep learning (RDL) to predict high-grade patterns within lung ADC. METHODS Included in this study were 111 patients differentiated as having GGOs and pathologically confirmed ADC. Four different groups of methods were compared to classify the GGOs for the prediction of the pathological subtypes of high-grade lung ADCs in definitive hematoxylin and eosin stain, including radiomics with gray-level features, radiomics with textural features, deep learning method, and the RDL. RESULTS We evaluated the performance of different models on 111 NSCLC patients using 4-fold cross-validation. The proposed RDL has achieved an overall accuracy of 0.913, which significantly outperforms the other methods (p < 0.01, analysis of variation, ANOVA). In addition, we also verified the generality and practical effectiveness of these models on an independent validation dataset of 28 patients. The results showed that our RDL framework with an accuracy of 0.966 significantly surpassed other methods. CONCLUSION High-grade lung ADC based on histologic pattern spectrum in GGO lesions might be predicted by the framework combining radiomics with deep learning, which reveals advantage over radiomics alone.
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Affiliation(s)
- Xing Wang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Li Zhang
- Center for Data Science, Peking University, Beijing, China
| | - Xin Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Lei Tang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, Beijing, China
| | - Jie Zhao
- Center for Data Science in Health and Medicine, Peking University, Beijing, China
| | - Gaoxiang Chen
- Center for Data Science, Peking University, Beijing, China
| | - Xiang Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shi Yan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shaolei Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yue Yang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China
| | - Yue Kang
- Linkdoc AI Research (LAIR), Building A, Sinosteel International Plaza, No.8 Haidian Street, Haidian District, Beijing, China
| | - Quanzheng Li
- MGH/BWH Center for Clinical Data Science, Boston, MA 02115, USA.
| | - Nan Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
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15
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Sollini M, Gelardi F, Matassa G, Delgado Bolton RC, Chiti A, Kirienko M. Interdisciplinarity: an essential requirement for translation of radiomics research into clinical practice – a systematic review focused on thoracic oncology. Rev Esp Med Nucl Imagen Mol 2020. [DOI: 10.1016/j.remnie.2019.11.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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16
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Park S, Lee SM, Noh HN, Hwang HJ, Kim S, Do KH, Seo JB. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol 2020; 30:4883-4892. [PMID: 32300970 DOI: 10.1007/s00330-020-06805-w] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 02/28/2020] [Accepted: 03/11/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVES To develop a model for differentiating the predominant subtype-based prognostic groups of lung adenocarcinoma using CT radiomic features, and to validate its performance in comparison with radiologists' assessments. METHODS A total of 993 patients presenting with invasive lung adenocarcinoma between March 2010 and June 2016 were identified. Predominant histologic subtypes were categorized into three groups according to their prognosis (group 0: lepidic; group 1: acinar/papillary; group 2: solid/micropapillary). Seven hundred eighteen radiomic features were extracted from segmented lung cancers on contrast-enhanced CT. A model-development set was formed from the images of 893 patients, while 100 image sets were reserved for testing. A least absolute shrinkage and selection operator method was used for feature selection. Performance of the radiomic model was evaluated using receiver operating characteristic curve analysis, and accuracy on the test set was compared with that of three radiologists with varying experiences (6, 7, and 19 years in chest CT). RESULTS Our model differentiated the three groups with areas under the curve (AUCs) of 0.892 and 0.895 on the development and test sets, respectively. In pairwise discrimination, the AUC was highest for group 0 vs. 2 (0.984). The accuracy of the model on the test set was higher than the averaged accuracy of the three radiologists without statistical significance (73.0% vs. 61.7%, p = 0.059). For group 2, the model achieved higher PPV than the observers (85.7% vs. 35.0-48.4%). CONCLUSIONS Predominant subtype-based prognostic groups of lung adenocarcinoma were classified by a CT-based radiomic model with comparable performance to radiologists. KEY POINTS • A CT-based radiomic model differentiated three prognosis-based subtype groups of lung adenocarcinoma with areas under the curve (AUCs) of 0.892 and 0.895 on development and test sets, respectively. • The CT-based radiomic model showed near perfect discrimination between group 0 and group 2 (AUCs, 0.984-1.000). • The accuracy of the CT-based radiomic model was comparable to the averaged accuracy of the three radiologists with 6, 7, and 19 years of clinical experience in chest CT (73.0% vs. 61.7%, p = 0.059), achieving a higher positive predictive value for group 2 than the observers (85.7% vs. 35.0-48.4%).
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Affiliation(s)
- Sohee Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea
| | - Sang Min Lee
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea.
| | - Han Na Noh
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea
| | - Seonok Kim
- Department of Medical Statistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kyung-Hyun Do
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea
| | - Joon Beom Seo
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-ro 43 Gil, Songpa-gu, Seoul, 138-736, South Korea
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Sollini M, Gelardi F, Matassa G, Delgado Bolton RC, Chiti A, Kirienko M. Interdisciplinarity: An essential requirement for translation of radiomics research into clinical practice -a systematic review focused on thoracic oncology. Rev Esp Med Nucl Imagen Mol 2020; 39:146-156. [PMID: 32278786 DOI: 10.1016/j.remn.2019.10.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2019] [Revised: 10/22/2019] [Accepted: 10/24/2019] [Indexed: 02/08/2023]
Abstract
BACKGROUND Recently, evidence has accumulated that demonstrates the potential for future applications of radiomics in many clinical settings, including thoracic oncology. Methodological reasons for the immaturity of image mining (radiomics and artificial intelligence-based) studies have been identified. However, data on the influence of the composition of the research team on the quality of investigations in radiomics are lacking. AIM This review aims to evaluate the interdisciplinarity within studies on radiomics in thoracic oncology in order to assess its influence on the quality of research (QUADAS-2 score) in the image mining field. METHODS We considered for inclusion radiomics investigations with objectives relating to clinical practice in thoracic oncology. Subsequently, we interviewed the corresponding authors. The field of expertise and/or educational degree was then used to assess interdisciplinarity. Subsequently, all studies were evaluated applying the QUADAS-2 score and assigned to a research phase from 0 to IV. RESULTS Overall, 27 studies were included. The study quality according to the QUADAS-2 score was low (score ≤5) in 8, moderate (=6) in 12, and high (≥7) in 7 papers. An interdisciplinary team (at least 3 different expertise categories) was involved in half of the papers without any type of validation and in all papers with independent validation. Clinicians were not involved in phase 0 studies while they contributed to all papers classified as phase I and to 4/5 papers classified as phase II with independent validation. CONCLUSIONS The composition of the research team influences the quality of investigations in radiomics. Also, growth in interdisciplinarity appears to reflect research development from the early phase to a more mature, clinically oriented stage of investigation.
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Affiliation(s)
- M Sollini
- Nuclear Medicine, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56 - 20089, Rozzano (Milán), Italia; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4 - 20090, Pieve Emanuele (Milán), Italia
| | - F Gelardi
- Training Program in Nuclear Medicine, Humanitas University, via Rita Levi Montalcini, 4 - 20090, Pieve Emanuele (Milán), Italia
| | - G Matassa
- Training Program in Nuclear Medicine, Humanitas University, via Rita Levi Montalcini, 4 - 20090, Pieve Emanuele (Milán), Italia
| | - R C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, San Pedro University Hospital and Centre for Biomedical Research of La Rioja (CIBIR), calle Piqueras, 98, Logroño (La Rioja) 26006, España
| | - A Chiti
- Nuclear Medicine, Diagnostic Imaging Department, Humanitas Clinical and Research Center - IRCCS, via Manzoni, 56 - 20089, Rozzano (Milán), Italia; Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4 - 20090, Pieve Emanuele (Milán), Italia
| | - M Kirienko
- Department of Biomedical Sciences, Humanitas University, via Rita Levi Montalcini, 4 - 20090, Pieve Emanuele (Milán), Italia.
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Abstract
PURPOSE OF REVIEW To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine. RECENT FINDINGS Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data. Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.
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Abstract
OBJECTIVE Radiologic texture is the variation in image intensities within an image and is an important part of radiomics. The objective of this article is to discuss some parameters that affect the performance of texture metrics and propose recommendations that can guide both the design and evaluation of future radiomics studies. CONCLUSION A variety of texture-extraction techniques are used to assess clinical imaging data. Currently, no consensus exists regarding workflow, including acquisition, extraction, or reporting of variable settings leading to poor reproducibility.
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Bai JH, Hsieh MS, Liao HC, Lin MW, Chen JS. Prediction of pleural invasion using different imaging tools in non-small cell lung cancer. ANNALS OF TRANSLATIONAL MEDICINE 2019; 7:33. [PMID: 30854386 DOI: 10.21037/atm.2019.01.15] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Clinical staging of non-small cell lung cancer (NSCLC) is used for planning therapeutic strategies. In particular, pleural invasion is regarded as an indicator for upstaging to T2 or T3 in the current 8th TNM staging system; patients with pleural invasion should be indicated for lobectomy rather than sublobar resection. Therefore, accurate preoperative prediction of pleural invasion is important for surgical planning. In recent years, different radiological investigations for patients with NSCLC have been widely used, and methods for more precise detection have been developed in the current medical imaging studies. Therefore, several radiological investigation tools have been used for the prediction of pleural invasion. In this article, to identify the imaging modalities for accurate prediction of pleural invasion, we reviewed the different methods used for this purpose and discussed their advantages and limitations.
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Affiliation(s)
- Jhih-Hao Bai
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Min-Shu Hsieh
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Hsien-Chi Liao
- Department of Traumatology, National Taiwan University Hospital, Taipei, Taiwan
| | - Mong-Wei Lin
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
| | - Jin-Shing Chen
- Department of Surgery, National Taiwan University Hospital and National Taiwan University College of Medicine, Taipei, Taiwan
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