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He S, Zhao Y, Shi L, Yang X, Wang X, Luo Y, Wang M, Zhang X, Li X, Yu D, Feng X. Utilizing radiomics for differential diagnosis of inverted papilloma and chronic rhinosinusitis with polyps based on unenhanced CT scans. Sci Rep 2024; 14:19299. [PMID: 39164351 PMCID: PMC11336076 DOI: 10.1038/s41598-024-70134-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2024] [Accepted: 08/13/2024] [Indexed: 08/22/2024] Open
Abstract
To evaluate whether radiomics models based on unenhanced paranasal sinuses CT images could be a useful tool for differentiating inverted papilloma (IP) from chronic rhinosinusitis with polyps (CRSwNP). This retrospective study recruited 240 patients with CRSwNP and 106 patients with IP from three centers. 253 patients from Qilu Hospital were randomly divided into the training set (n = 151) and the internal validation set (n = 102) with a ratio of 6:4. 93 patients from the other two centers were used as the external validation set. The patients with the unilateral disease (n = 115) from Qilu Hospital were selected to further develop a subgroup analysis. Lesion segmentation was manually delineated in CT images. Least absolute shrinkage and selection operator algorithm was performed for feature reduction and selection. Decision tree, support vector machine, random forest, and adaptive boosting regressor were employed to establish the differential diagnosis models. 43 radiomic features were selected for modeling. Among the models, RF achieved the best results, with an AUC of 0.998, 0.943, and 0.934 in the training set, the internal validation set, and the external validation set, respectively. In the subgroup analysis, RF achieved an AUC of 0.999 in the training set and 0.963 in the internal validation set. The proposed radiomics models offered a non-invasion and accurate differential approach between IP and CRSwNP and has some significance in guiding clinicians determining the best treatment plans, as well as predicting the prognosis.
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Affiliation(s)
- Shaojuan He
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuxuan Zhao
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Lei Shi
- Department of Otorhinolaryngology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Xiaorong Yang
- Clinical Epidemiology Unit, Qilu Hospital of Shandong University, Jinan, China
| | - Xuehai Wang
- Department of Otorhinolaryngology, Weihai Municipal Hospital, Weihai, China
| | - Yang Luo
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Mingming Wang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Xianxing Zhang
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Xuezhong Li
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China
| | - Dexin Yu
- Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xin Feng
- Department of Otorhinolaryngology, Qilu Hospital of Shandong University, NHC Key Laboratory of Otorhinolaryngology (Shandong University), Jinan, China.
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Xing X, Li L, Sun M, Yang J, Zhu X, Peng F, Du J, Feng Y. Deep-learning-based 3D super-resolution CT radiomics model: Predict the possibility of the micropapillary/solid component of lung adenocarcinoma. Heliyon 2024; 10:e34163. [PMID: 39071606 PMCID: PMC11279278 DOI: 10.1016/j.heliyon.2024.e34163] [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: 01/30/2024] [Revised: 07/03/2024] [Accepted: 07/04/2024] [Indexed: 07/30/2024] Open
Abstract
Objective Invasive lung adenocarcinoma(ILA) with micropapillary (MPP)/solid (SOL) components has a poor prognosis. Preoperative identification is essential for decision-making for subsequent treatment. This study aims to construct and evaluate a super-resolution(SR) enhanced radiomics model designed to predict the presence of MPP/SOL components preoperatively to provide more accurate and individualized treatment planning. Methods Between March 2018 and November 2023, patients who underwent curative intent ILA resection were included in the study. We implemented a deep transfer learning network on CT images to improve their resolution, resulting in the acquisition of preoperative super-resolution CT (SR-CT) images. Models were developed using radiomic features extracted from CT and SR-CT images. These models employed a range of classifiers, including Logistic Regression (LR), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Multilayer Perceptron (MLP). The diagnostic performance of the models was assessed by measuring the area under the curve (AUC). Result A total of 245 patients were recruited, of which 109 (44.5 %) were diagnosed with ILA with MPP/SOL components. In the analysis of CT images, the SVM model exhibited outstanding effectiveness, recording AUC scores of 0.864 in the training group and 0.761 in the testing group. When this SVM approach was used to develop a radiomics model with SR-CT images, it recorded AUCs of 0.904 in the training and 0.819 in the test cohorts. The calibration curves indicated a high goodness of fit, while decision curve analysis (DCA) highlighted the model's clinical utility. Conclusion The study successfully constructed and evaluated a deep learning(DL)-enhanced SR-CT radiomics model. This model outperformed conventional CT radiomics models in predicting MPP/SOL patterns in ILA. Continued research and broader validation are necessary to fully harness and refine the clinical potential of radiomics when combined with SR reconstruction technology.
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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
| | - Jiahu Yang
- Department of Radiology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Xinhai Zhu
- Department of Thoracic Surgery, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Fang Peng
- Department of Pathology, Zhejiang Hospital, Hangzhou, Zhejiang, China
| | - Jianzong Du
- Department of Respiratory Medicine, 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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Yoon DW, Kang D, Jeon YJ, Lee J, Shin S, Cho JH, Choi YS, Zo JI, Kim J, Shim YM, Cho J, Kim HK, Lee HY. Computed tomography characteristics of cN0 primary non-small cell lung cancer predict occult lymph node metastasis. Eur Radiol 2024:10.1007/s00330-024-10835-z. [PMID: 38850308 DOI: 10.1007/s00330-024-10835-z] [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: 10/29/2023] [Revised: 04/23/2024] [Accepted: 05/10/2024] [Indexed: 06/10/2024]
Abstract
RATIONALE Occult lymph node metastasis (OLNM) is frequently found in patients with resectable non-small cell lung cancer (NSCLC), despite using diagnostic methods recommended by guidelines. OBJECTIVES To evaluate the risk of OLNM in NSCLC patients using the radiologic characteristics of the primary tumor on computed tomography (CT). METHODS We retrospectively reviewed clinicopathologic features of 2042 clinical T1-4N0 NSCLC patients undergoing curative intent pulmonary resection. Unique radiological features (i.e., air-bronchogram throughout the whole tumor, heterogeneous ground-glass opacity (GGO), mainly cystic appearance, endobronchial location), percentage of solid portion, and shape of tumor margin were analyzed via a stepwise approach. We used multivariable logistic regression to assess the relationship between OLNM and tumor characteristics. RESULTS Compared with the other unique features, endobronchial tumors were associated with the highest risk of OLNM (OR = 3.9, 95% confidence interval (CI) = 2.29-6.62), and heterogeneous GGO and mainly cystic tumors were associated with a low risk of OLNM. For tumors without unique features, the percentage of the solid portion was measured, and solid tumors were associated with OLNM (OR = 2.49, 95% CI = 1.86-3.35). Among part-solid tumors with solid proportion > 50%, spiculated margin, and peri-tumoral GGO were associated with OLNM. CONCLUSIONS The risk of OLNM could be assessed using radiologic characteristics on CT. This could allow us to adequately select optimal candidates for invasive nodal staging procedures (INSPs) and complete systematic lymph node dissection. CLINICAL RELEVANCE STATEMENT These data may be helpful for clinicians to select appropriate candidates for INSPs and complete surgical systematic lymph node dissection in NSCLC patients. KEY POINTS Lymph node metastasis status plays a key role in both prognostication and treatment planning. Solid tumors, particularly endobronchial tumors, were associated with occult lymph node metastasis (OLNM). The risk of OLNM can be assessed using radiologic characteristics acquired from CT images.
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Affiliation(s)
- Dong Woog Yoon
- Department of Thoracic and Cardiovascular Surgery, Chungang-University Hospital, Seoul, South Korea
| | - Danbee Kang
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea
| | - Yeong Jeong Jeon
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Junghee Lee
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sumin Shin
- Department of Thoracic and Cardiovascular Surgery, School of Medicine, Ewha Womans University, Seoul, South Korea
| | - Jong Ho Cho
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Yong Soo Choi
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jae Ill Zo
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Jhingook Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Young Mog Shim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Juhee Cho
- Department of Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences and Technology (SAIHST), Sungkyunkwan University, Seoul, South Korea
- Center for Clinical Epidemiology, Samsung Medical Center, Seoul, South Korea
- Departments of Epidemiology and Health, Behavior, and Society, Baltimore, MD, USA
| | - Hong Kwan Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea.
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Ninomiya K, Yanagawa M, Tsubamoto M, Sato Y, Suzuki Y, Hata A, Kikuchi N, Yoshida Y, Yamagata K, Doi S, Ogawa R, Tokuda Y, Kido S, Tomiyama N. Prediction of solid and micropapillary components in lung invasive adenocarcinoma: radiomics analysis from high-spatial-resolution CT data with 1024 matrix. Jpn J Radiol 2024; 42:590-598. [PMID: 38413550 PMCID: PMC11139717 DOI: 10.1007/s11604-024-01534-2] [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: 11/12/2023] [Accepted: 01/13/2024] [Indexed: 02/29/2024]
Abstract
PURPOSE To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT). MATERIALS AND METHODS For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant. RESULTS Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists. CONCLUSION Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma.
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Affiliation(s)
- Keisuke Ninomiya
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan.
| | - Mitsuko Tsubamoto
- Nishinomiya Municipal Central Hospital, 8-24 Hayashidacho, Nishinomiya, Hyogo, 663-8014, Japan
| | - Yukihisa Sato
- Suita Municipal Hospital, 5-7 Kishibeshinmachi, Suita, Osaka, 564-0018, Japan
| | - Yuki Suzuki
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Akinori Hata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriko Kikuchi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yuriko Yoshida
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Kazuki Yamagata
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shuhei Doi
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Ryo Ogawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Yukiko Tokuda
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Shoji Kido
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
| | - Noriyuki Tomiyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan
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Steiner D, Park JA, Singh S, Potter A, Scalera J, Beane J, Suzuki K, Lenburg ME, Burks EJ. A computed tomography-based score indicative of lung cancer aggression (SILA) predicts lung adenocarcinomas with low malignant potential or vascular invasion. Cancer Biomark 2024:CBM230456. [PMID: 39058440 DOI: 10.3233/cbm-230456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/28/2024]
Abstract
BACKGROUND Histologic grading of lung adenocarcinoma (LUAD) is predictive of outcome but is only possible after surgical resection. A radiomic biomarker predictive of grade has the potential to improve preoperative management of early-stage LUAD. OBJECTIVE Validate a prognostic radiomic score indicative of lung cancer aggression (SILA) in surgically resected stage I LUAD (n= 161) histologically graded as indolent low malignant potential (LMP), intermediate, or aggressive vascular invasive (VI) subtypes. METHODS The SILA scores were generated from preoperative CT-scans using the previously validated Computer-Aided Nodule Assessment and Risk Yield (CANARY) software. RESULTS Cox proportional regression showed significant association between the SILA and 7-year recurrence-free survival (RFS) in a univariate (p< 0.05) and multivariate (p< 0.05) model incorporating age, gender, smoking status, pack years, and extent of resection. The SILA was positively correlated with invasive size (spearman r= 0.54, p= 8.0 × 10 - 14) and negatively correlated with percentage of lepidic histology (spearman r=-0.46, p= 7.1 × 10 - 10). The SILA predicted indolent LMP with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.74 and aggressive VI with an AUC of 0.71, the latter remaining significant when invasive size was included as a covariate in a logistic regression model (p< 0.01). CONCLUSIONS The SILA scoring of preoperative CT scans was prognostic and predictive of resected pathologic grade.
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Affiliation(s)
- Dylan Steiner
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Ju Ae Park
- Thoracic Surgery, Inova Schar Cancer Institute, Fairfax, VA, USA
| | - Sarah Singh
- Department of Radiology, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Austin Potter
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Jonathan Scalera
- Department of Radiology, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Jennifer Beane
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Kei Suzuki
- Thoracic Surgery, Inova Schar Cancer Institute, Fairfax, VA, USA
| | - Marc E Lenburg
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
- Department of Pathology, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
| | - Eric J Burks
- Department of Pathology, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
- Department of Medicine, Section of Computational Biomedicine, Boston University Chobanian and Avedisian School of Medicine, Boston, USA
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Pan C, Wang Q, Wang H, Deng X, Chen L, Li Z. LncRNA CARD8-AS1 suppresses lung adenocarcinoma progression by enhancing TRIM25-mediated ubiquitination of TXNRD1. Carcinogenesis 2024; 45:311-323. [PMID: 38153696 DOI: 10.1093/carcin/bgad097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 12/06/2023] [Accepted: 12/27/2023] [Indexed: 12/29/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in the tumorigenesis and progression of lung adenocarcinoma (LUAD). However, little was known about the role of lncRNAs in high-risk LUAD subtypes: micropapillary-predominant adenocarcinoma (MPA) and solid-predominant adenocarcinoma (SPA). In this study, we conducted a systematic screening of differentially expressed lncRNAs using RNA sequencing in 10 paired MPA/SPA tumor tissues and adjacent normal tissues. Consequently, 110 significantly up-regulated lncRNAs and 288 aberrantly down-regulated lncRNAs were identified (|Log2 Foldchange| ≥ 1 and corrected P < 0.05). The top 10 lncRNAs were further analyzed in 89 MPA/SPA tumor tissues and 59 normal tissues from The Cancer Genome Atlas database. Among them, CARD8-AS1 showed the most significant differential expression, and decreased expression of CARD8-AS1 was significantly associated with a poorer prognosis. Functionally, CARD8-AS1 overexpression remarkably suppressed the proliferation, migration and invasion of LUAD cells both in vitro and in vivo. Conversely, inhibition of CARD8-AS1 yielded opposite effects. Mechanistically, CARD8-AS1 acted as a scaffold to facilitate the interaction between TXNRD1 and E3 ubiquitin ligase TRIM25, thereby promoting the degradation of TXNRD1 through the ubiquitin-proteasome pathway. Additionally, TXNRD1 was found to promote LUAD cell proliferation, migration and invasion in vitro. Furthermore, the suppressed progression of LUAD cells resulting from CARD8-AS1 overexpression could be significantly reversed by simultaneous overexpression of TXNRD1. In conclusion, this study revealed that the lncRNA CARD8-AS1 played a suppressive role in the progression of LUAD by enhancing TRIM25-mediated ubiquitination of TXNRD1. The CARD8-AS1-TRIM25-TXNRD1 axis may represent a promising therapeutic target for LUAD.
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Affiliation(s)
- Cheng Pan
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Qi Wang
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Hongshun Wang
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Xiaheng Deng
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Liang Chen
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
| | - Zhihua Li
- Department of Thoracic Surgery, First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China
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Lee S, Lee CY, Kim NY, Suh YJ, Lee HJ, Yong HS, Kim HR, Kim YJ. Feasibility of UTE-MRI-based radiomics model for prediction of histopathologic subtype of lung adenocarcinoma: in comparison with CT-based radiomics model. Eur Radiol 2024; 34:3422-3430. [PMID: 37840100 DOI: 10.1007/s00330-023-10302-1] [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: 04/03/2023] [Revised: 08/09/2023] [Accepted: 08/28/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVES To assess the feasibility of the UTE-MRI radiomic model in predicting the micropapillary and/or solid (MP/S) patterns of surgically resected lung adenocarcinoma. MATERIALS AND METHODS We prospectively enrolled 74 lesions from 71 patients who underwent UTE-MRI and CT before curative surgery for early lung adenocarcinoma. For conventional radiologic analysis, we analyzed the longest lesion diameter and lesion characteristics at both UTE-MRI and CT. Radiomic features were extracted from the volume of interest of the lesions and Rad-scores were generated using the least absolute shrinkage and selection operator with fivefold cross-validation. Six models were constructed by combining the conventional radiologic model, UTE-MRI Rad-score, and CT Rad-score. The areas under the curves (AUCs) of each model were compared using the DeLong method. Early recurrence after curative surgery was analyzed, and Kaplan-Meier survival analysis was performed. RESULTS Twenty-four lesions were MP/S-positive, and 50 were MP/S-negative. The longitudinal size showed a small systematic difference between UTE-MRI and CT, with fair intermodality agreement of lesion characteristic (kappa = 0.535). The Rad-scores of the UTE-MRI and CT demonstrated AUCs of 0.84 and 0.841, respectively (p = 0.98). Among the six models, mixed conventional, UTE-MRI, and CT Rad-score model showed the highest diagnostic performance (AUC = 0.879). In the survival analysis, the high- and low-risk groups were successfully divided by the Rad-score in UTE-MRI (p = 0.01) and CT (p < 0.01). CONCLUSION UTE-MRI radiomic model predicting MP/S positivity is feasible compared with the CT radiomic model. Also, it was associated with early recurrence in the survival analysis. CLINICAL RELEVANCE STATEMENT A radiomic model utilizing UTE-MRI, which does not present a radiation hazard, was able to successfully predict the histopathologic subtype of lung adenocarcinoma, and it was associated with the patient's recurrence-free survival. KEY POINTS • No studies have reported the ultrashort echo time (UTE)-MRI-based radiomic model for lung adenocarcinoma. • The UTE-MRI Rad-score showed comparable diagnostic performance with CT Rad-score for predicting micropapillary and/or solid histopathologic pattern. • UTE-MRI is feasible not only for conventional radiologic analysis, but also for radiomics analysis.
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Affiliation(s)
- Suji Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, South Korea
| | - Chang Young Lee
- Department of Thoracic and Cardiovascular Surgery, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, South Korea
| | - Na Young Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, South Korea
| | - Yong Joo Suh
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, South Korea
| | - Hye-Jeong Lee
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, South Korea
| | - Hwan Seok Yong
- Department of Radiology, Guro Hospital, Korea University College of Medicine, Seoul, South Korea
| | - Hye Ryun Kim
- Department of Oncology, Severance Hospital, Yonsei University College of Medicine, Seoul, 03722, South Korea
| | - Young Jin Kim
- Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, 50-1 Yonsei-Ro Seodaemun-Gu, Seoul, 03722, South Korea.
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Sheikh TS, Cho M. Segmentation of Variants of Nuclei on Whole Slide Images by Using Radiomic Features. Bioengineering (Basel) 2024; 11:252. [PMID: 38534526 DOI: 10.3390/bioengineering11030252] [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: 01/02/2024] [Revised: 02/10/2024] [Accepted: 02/26/2024] [Indexed: 03/28/2024] Open
Abstract
The histopathological segmentation of nuclear types is a challenging task because nuclei exhibit distinct morphologies, textures, and staining characteristics. Accurate segmentation is critical because it affects the diagnostic workflow for patient assessment. In this study, a framework was proposed for segmenting various types of nuclei from different organs of the body. The proposed framework improved the segmentation performance for each nuclear type using radiomics. First, we used distinct radiomic features to extract and analyze quantitative information about each type of nucleus and subsequently trained various classifiers based on the best input sub-features of each radiomic feature selected by a LASSO operator. Second, we inputted the outputs of the best classifier to various segmentation models to learn the variants of nuclei. Using the MoNuSAC2020 dataset, we achieved state-of-the-art segmentation performance for each category of nuclei type despite the complexity, overlapping, and obscure regions. The generalized adaptability of the proposed framework was verified by the consistent performance obtained in whole slide images of different organs of the body and radiomic features.
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Affiliation(s)
- Taimoor Shakeel Sheikh
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
| | - Migyung Cho
- AIMI-Artificial Intelligence and Medical Imaging Laboratory, Department of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of Korea
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Yang Z, Dong H, Fu C, Zhang Z, Hong Y, Shan K, Ma C, Chen X, Xu J, Pang Z, Hou M, Zhang X, Zhu W, Liu L, Li W, Sun J, Zhao F. A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study. Front Oncol 2024; 14:1289555. [PMID: 38313797 PMCID: PMC10834705 DOI: 10.3389/fonc.2024.1289555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
Background The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.
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Affiliation(s)
- Zebin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Hao Dong
- Department of Radiology, Affiliated Xiaoshan Hospital of Wenzhou Medical University, Hangzhou, China
| | - Chunlong Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zening Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Hong
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
| | - Kangfei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Chijun Ma
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xiaolu Chen
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jieping Xu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhenzhu Pang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaowei Zhang
- Department of Pathology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Weihua Zhu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Linjiang Liu
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Weihua Li
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Fenhua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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Zhou L, Sun J, Long H, Zhou W, Xia R, Luo Y, Fang J, Wang Y, Chen X. Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma. Insights Imaging 2024; 15:5. [PMID: 38185779 PMCID: PMC10772036 DOI: 10.1186/s13244-023-01573-9] [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: 09/10/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. METHODS Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without. RESULTS PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit. CONCLUSION 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.
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Affiliation(s)
- Linyi Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - He Long
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Luo
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China.
| | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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11
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Zhong Y, Cai C, Chen T, Gui H, Chen C, Deng J, Yang M, Yu B, Song Y, Wang T, Chen Y, Shi H, Xie D, Chen C, She Y. PET/CT-based deep learning grading signature to optimize surgical decisions for clinical stage I invasive lung adenocarcinoma and biologic basis under its prediction: a multicenter study. Eur J Nucl Med Mol Imaging 2024; 51:521-534. [PMID: 37725128 DOI: 10.1007/s00259-023-06434-7] [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: 04/26/2023] [Accepted: 09/06/2023] [Indexed: 09/21/2023]
Abstract
PURPOSE No consensus on a grading system for invasive lung adenocarcinoma had been built over a long period of time. Until October 2020, a novel grading system was proposed to quantify the whole landscape of histologic subtypes and proportions of pulmonary adenocarcinomas. This study aims to develop a deep learning grading signature (DLGS) based on positron emission tomography/computed tomography (PET/CT) to personalize surgical treatments for clinical stage I invasive lung adenocarcinoma and explore the biologic basis under its prediction. METHODS A total of 2638 patients with clinical stage I invasive lung adenocarcinoma from 4 medical centers were retrospectively included to construct and validate the DLGS. The predictive performance of the DLGS was evaluated by the area under the receiver operating characteristic curve (AUC), its potential to optimize surgical treatments was investigated via survival analyses in risk groups defined by the DLGS, and its biological basis was explored by comparing histologic patterns, genotypic alternations, genetic pathways, and infiltration of immune cells in microenvironments between risk groups. RESULTS The DLGS to predict grade 3 achieved AUCs of 0.862, 0.844, and 0.851 in the validation set (n = 497), external cohort (n = 382), and prospective cohort (n = 600), respectively, which were significantly better than 0.814, 0.810, and 0.806 of the PET model, 0.813, 0.795, and 0.824 of the CT model, and 0.762, 0.734, and 0.751 of the clinical model. Additionally, for DLGS-defined high-risk population, lobectomy yielded an improved prognosis compared to sublobectomy p = 0.085 for overall survival [OS] and p = 0.038 for recurrence-free survival [RFS]) and systematic nodal dissection conferred a superior prognosis to limited nodal dissection (p = 0.001 for OS and p = 0.041 for RFS). CONCLUSION The DLGS harbors the potential to predict the histologic grade and personalize the surgical treatments for clinical stage I invasive lung adenocarcinoma. Its applicability to other territories should be further validated by a larger international study.
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Affiliation(s)
- Yifan Zhong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chuang Cai
- School of Computer Science and Communication Engineering , Jiangsu University, Zhenjiang, Jiangsu, China
| | - Tao Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Hao Gui
- Graduate School at Shenzhen, Tsinghua University, Shenzhen, China
| | - Cheng Chen
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China
| | - Jiajun Deng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Minglei Yang
- Department of Thoracic Surgery, Ningbo HwaMei Hospital, Chinese Academy of Sciences, Zhejiang, China
| | - Bentong Yu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yongxiang Song
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Guizhou, China
| | - Tingting Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yangchun Chen
- Department of Nuclear Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huazheng Shi
- Shanghai Universal Cloud Medical Imaging Diagnostic Center, Shanghai, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
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12
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Xu H, Wu W, Zhao Y, Liu Z, Bao D, Li L, Lin M, Zhang Y, Zhao X, Luo D. Analysis of preoperative computed tomography radiomics and clinical factors for predicting postsurgical recurrence of papillary thyroid carcinoma. Cancer Imaging 2023; 23:118. [PMID: 38098119 PMCID: PMC10722708 DOI: 10.1186/s40644-023-00629-9] [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: 07/17/2023] [Accepted: 10/19/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Postsurgical recurrence is of great concern for papillary thyroid carcinoma (PTC). We aim to investigate the value of computed tomography (CT)-based radiomics features and conventional clinical factors in predicting the recurrence of PTC. METHODS Two-hundred and eighty patients with PTC were retrospectively enrolled and divided into training and validation cohorts at a 6:4 ratio. Recurrence was defined as cytology/pathology-proven disease or morphological evidence of lesions on imaging examinations within 5 years after surgery. Radiomics features were extracted from manually segmented tumor on CT images and were then selected using four different feature selection methods sequentially. Multivariate logistic regression analysis was conducted to identify clinical features associated with recurrence. Radiomics, clinical, and combined models were constructed separately using logistic regression (LR), support vector machine (SVM), k-nearest neighbor (KNN), and neural network (NN), respectively. Receiver operating characteristic analysis was performed to evaluate the model performance in predicting recurrence. A nomogram was established based on all relevant features, with its reliability and reproducibility verified using calibration curves and decision curve analysis (DCA). RESULTS Eighty-nine patients with PTC experienced recurrence. A total of 1218 radiomics features were extracted from each segmentation. Five radiomics and six clinical features were related to recurrence. Among the 4 radiomics models, the LR-based and SVM-based radiomics models outperformed the NN-based radiomics model (P = 0.032 and 0.026, respectively). Among the 4 clinical models, only the difference between the area under the curve (AUC) of the LR-based and NN-based clinical model was statistically significant (P = 0.035). The combined models had higher AUCs than the corresponding radiomics and clinical models based on the same classifier, although most differences were not statistically significant. In the validation cohort, the combined models based on the LR, SVM, KNN, and NN classifiers had AUCs of 0.746, 0.754, 0.669, and 0.711, respectively. However, the AUCs of these combined models had no significant differences (all P > 0.05). Calibration curves and DCA indicated that the nomogram have potential clinical utility. CONCLUSIONS The combined model may have potential for better prediction of PTC recurrence than radiomics and clinical models alone. Further testing with larger cohort may help reach statistical significance.
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Affiliation(s)
- Haijun Xu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Wenli Wu
- Medical Imaging Center, Liaocheng Tumor Hospital, Liaocheng, 252000, China
| | - Yanfeng Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
| | - Zhou Liu
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Dan Bao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Lin Li
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Meng Lin
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Ya Zhang
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China
| | - Xinming Zhao
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Dehong Luo
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
- Department of Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, 518116, China.
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13
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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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Chen X, Tang Y, Wu D, Li R, Lin Z, Zhou X, Wang H, Zhai H, Xu J, Shi X, Zhang G. From imaging to clinical outcome: dual-region CT radiomics predicting FOXM1 expression and prognosis in hepatocellular carcinoma. Front Oncol 2023; 13:1278467. [PMID: 37817774 PMCID: PMC10561750 DOI: 10.3389/fonc.2023.1278467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 09/07/2023] [Indexed: 10/12/2023] Open
Abstract
Background Liver cancer, especially hepatocellular carcinoma (HCC), remains a significant global health challenge. Traditional prognostic indicators for HCC often fall short in providing comprehensive insights for individualized treatment. The integration of genomics and radiomics offers a promising avenue for enhancing the precision of HCC diagnosis and prognosis. Methods From the Cancer Genome Atlas (TCGA) database, we categorized mRNA of HCC patients by Forkhead Box M1 (FOXM1) expression and performed univariate and multivariate studies to pinpoint autonomous HCC risk factors. We deployed subgroup, correlation, and interaction analyses to probe FOXM1's link with clinicopathological elements. The connection between FOXM1 and immune cells was evaluated using the CIBERSORTx database. The functions of FOXM1 were investigated through analyses of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). After filtering through TCGA and the Cancer Imaging Archive (TCIA) database, we employed dual-region computed tomography (CT) radiomics technology to noninvasively predict the mRNA expression of FOXM1 in HCC tissues. Radiomic features were extracted from both tumoral and peritumoral regions, and a radiomics score (RS) was derived. The performance and robustness of the constructed models were evaluated using 10-fold cross-validation. A radiomics nomogram was developed by incorporating RS and clinical variables from the TCGA database. The models' discriminative abilities were assessed using metrics such as the area under the curve (AUC) of the receiver operating characteristic curves (ROC) and precision-recall (PR) curves. Results Our findings emphasized the overexpression of FOXM1 as a determinant of poor prognosis in HCC and illustrated its impact on immune cell infiltration. After selecting arterial phase CT, we chose 7 whole-tumor features and 3 features covering both the tumor and its surroundings to create WT and WP models for FOXM1 prediction. The WT model showed strong predictive capabilities for FOXM1 expression by PR curve. Conversely, the WP model did not demonstrate the good predictive ability. In our study, the radiomics score (RS) was derived from whole-tumor regions on CT images. The RS was significantly associated with FOXM1 expression, with an AUC of 0.918 in the training cohort and 0.837 in the validation cohort. Furthermore, the RS was correlated with oxidative stress genes and was integrated with clinical variables to develop a nomogram, which demonstrated good calibration and discrimination in predicting 12-, 36-, and 60-month survival probabilities. Additionally, bioinformatics analysis revealed FOXM1's potential role in shaping the immune microenvironment, with its expression linked to immune cell infiltration. Conclusion This study highlights the potential of integrating FOXM1 expression and radiomics in understanding HCC's complexity. Our approach offers a new perspective in utilizing radiomics for non-invasive tumor characterization and suggests its potential in providing insights into molecular profiles. Further research is needed to validate these findings and explore their clinical implications in HCC management.
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Affiliation(s)
- Xianyu Chen
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Yongsheng Tang
- Department of Hepatic Surgery, Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Donghao Wu
- Department of Medical Oncology, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Ruixi Li
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Zhiqun Lin
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xuhui Zhou
- Department of Radiology, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Hezhen Wang
- Department of Radiology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Hang Zhai
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Junming Xu
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Xianjie Shi
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Guangquan Zhang
- Department of Hepatobiliary and Pancreatic Surgery, The Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
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Yang Z, Cai Y, Chen Y, Ai Z, Chen F, Wang H, Han Q, Feng Q, Xiang Z. A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma. Acad Radiol 2023; 30:1946-1961. [PMID: 36567145 DOI: 10.1016/j.acra.2022.12.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/24/2022] [Accepted: 12/03/2022] [Indexed: 12/25/2022]
Abstract
RATIONALE AND OBJECTIVES The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. MATERIALS AND METHODS This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. RESULTS Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. CONCLUSION Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.
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Affiliation(s)
- Zhihe Yang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.); School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Yuqin Cai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Yirong Chen
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Zhu Ai
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Fang Chen
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Hao Wang
- Department of Pathology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R.China,(F.C.,H.W.)
| | - Qijia Han
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.)
| | - Qili Feng
- School of Life Sciences, South China Normal University, Guangzhou, GD, P.R.China,(Z.Y.,Q.F.)
| | - Zhiming Xiang
- Department of Radiology, Guangzhou Panyu Central Hospital, Guangzhou, GD, P.R. China,(Z.Y.,Y.C.,Y.C.,Z.A.,Q.H.,Z.X.).
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Pei G, Wang D, Sun K, Yang Y, Tang W, Sun Y, Yin S, Liu Q, Wang S, Huang Y. Deep learning-enhanced radiomics for histologic classification and grade stratification of stage IA lung adenocarcinoma: a multicenter study. Front Oncol 2023; 13:1224455. [PMID: 37546407 PMCID: PMC10400286 DOI: 10.3389/fonc.2023.1224455] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/03/2023] [Indexed: 08/08/2023] Open
Abstract
Background Preoperative prediction models for histologic subtype and grade of stage IA lung adenocarcinoma (LUAD) according to the update of the WHO Classification of Tumors of the Lung in 2021 and the 2020 new grade system are yet to be explored. We aim to develop the noninvasive pathology and grade evaluation approach for patients with stage IA LUAD via CT-based radiomics approach and evaluate their performance in clinical practice. Methods Chest CT scans were retrospectively collected from patients who were diagnosed with stage IA LUAD and underwent complete resection at two hospitals. A deep learning segmentation algorithm was first applied to assist lesion delineation. Expansion strategies such as bounding-box annotations were further applied. Radiomics features were then extracted and selected followed by radiomics modeling based on four classic machine learning algorithms for histologic subtype classification and grade stratification. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. Results The study included 294 and 145 patients with stage IA LUAD from two hospitals for radiomics analysis, respectively. For classification of four histological subtypes, multilayer perceptron (MLP) algorithm presented no annotation strategy preference and achieved the average AUC of 0.855, 0.922, and 0.720 on internal, independent, and external test sets with 1-pixel expansion annotation. Bounding-box annotation strategy also enabled MLP an acceptable and stable accuracy among test sets. Meanwhile, logistic regression was selected for grade stratification and achieved the average AUC of 0.928, 0.837, and 0.748 on internal, independent, and external test sets with optimal annotation strategies. Conclusions DL-enhanced radiomics models had great potential to predict the fine histological subtypes and grades of early-stage LUADs based on CT images, which might serve as a promising noninvasive approach for the diagnosis and management of early LUADs.
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Affiliation(s)
- Guotian Pei
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Kunkun Sun
- Department of Pathology, Peking University People’s Hospital, Beijing, China
| | - Yingshun Yang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Wen Tang
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Yanfeng Sun
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Siyuan Yin
- Institute of Advanced Research, Infervision Medical Technology Co. Ltd., Beijing, China
| | - Qiang Liu
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Shuai Wang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
| | - Yuqing Huang
- Department of Thoracic Surgery, Beijing Haidian Hospital (Haidian Section of Peking University Third Hospital), Beijing, China
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Wang F, Wang CL, Yi YQ, Zhang T, Zhong Y, Zhu JJ, Li H, Yang G, Yu TF, Xu H, Yuan M. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep 2023; 13:9302. [PMID: 37291251 PMCID: PMC10250309 DOI: 10.1038/s41598-023-36409-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 06/02/2023] [Indexed: 06/10/2023] Open
Abstract
To investigate whether the combination scheme of deep learning score (DL-score) and radiomics can improve preoperative diagnosis in the presence of micropapillary/solid (MPP/SOL) patterns in lung adenocarcinoma (ADC). A retrospective cohort of 514 confirmed pathologically lung ADC in 512 patients after surgery was enrolled. The clinicoradiographic model (model 1) and radiomics model (model 2) were developed with logistic regression. The deep learning model (model 3) was constructed based on the deep learning score (DL-score). The combine model (model 4) was based on DL-score and R-score and clinicoradiographic variables. The performance of these models was evaluated with area under the receiver operating characteristic curve (AUC) and compared using DeLong's test internally and externally. The prediction nomogram was plotted, and clinical utility depicted with decision curve. The performance of model 1, model 2, model 3 and model 4 was supported by AUCs of 0.848, 0.896, 0.906, 0.921 in the Internal validation set, that of 0.700, 0.801, 0.730, 0.827 in external validation set, respectively. These models existed statistical significance in internal validation (model 4 vs model 3, P = 0.016; model 4 vs model 1, P = 0.009, respectively) and external validation (model 4 vs model 2, P = 0.036; model 4 vs model 3, P = 0.047; model 4 vs model 1, P = 0.016, respectively). The decision curve analysis (DCA) demonstrated that model 4 predicting the lung ADC with MPP/SOL structure would be more beneficial than the model 1and model 3 but comparable with the model 2. The combined model can improve preoperative diagnosis in the presence of MPP/SOL pattern in lung ADC in clinical practice.
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Affiliation(s)
- Fen Wang
- Department of Medical Imaging Center, The Affiliated Huaian NO.1 People's Hospital of Nanjing Medical University, No. 1 West Huanghe Road, Huaian, 223300, China
| | - Cheng-Long Wang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Yin-Qiao Yi
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Teng Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Yan Zhong
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Jia-Jia Zhu
- Department of Radiology, Jiangsu Province Official Hospital, Nanjing, 210024, China
| | - Hai Li
- Department of Pathology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, 210029, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Tong-Fu Yu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China
| | - Hai Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China.
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province, 300, Guangzhou Road, Nanjing, 210029, China.
| | - Mei Yuan
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, 300 GuangZhou Road, Nanjing, 210029, China.
- Department of Radiology, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province, 300, Guangzhou Road, Nanjing, 210029, China.
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Xu L, Su H, Zhao S, Si H, Xie H, Ren Y, Gao J, Wang F, Xie X, Dai C, Wu C, Zhao D, Chen C. Development of the semi-dry dot-blot method for intraoperative detecting micropapillary component in lung adenocarcinoma based on proteomics analysis. Br J Cancer 2023; 128:2116-2125. [PMID: 37016102 PMCID: PMC10206083 DOI: 10.1038/s41416-023-02241-x] [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: 08/16/2022] [Revised: 03/06/2023] [Accepted: 03/16/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Micropapillary (MIP) component was a major concern in determining surgical strategy in lung adenocarcinoma (LUAD). We sought to develop a novel method for detecting MIP component during surgery. METHODS Differentially expressed proteins between MIP-positive and MIP-negative LUAD were identified through proteomics analysis. The semi-dry dot-blot (SDB) method which visualises the targeted protein was developed to detect MIP component. RESULTS Cellular retinoic acid-binding protein 2 (CRABP2) was significantly upregulated in MIP-positive LUAD (P < 0.001), and the high CRABP2 expression zone showed spatial consistency with MIP component. CRABP2 expression was also associated with decreased recurrence-free survival (P < 0.001). In the prospective cohort, the accuracy and sensitivity of detecting MIP component using SDB method by visualising CRABP2 were 82.2% and 72.7%, which were comparable to these of pathologist. Pathologist with the aid of SDB method would improve greatly in diagnostic accuracy (86.4%) and sensitivity (78.2%). In patients with minor MIP component (≤5%), the sensitivity of SDB method (63.6%) was significantly higher than pathologist (45.4%). CONCLUSIONS Intraoperative examination of CRABP2 using SDB method to detect MIP component reached comparable performance to pathologist, and SDB method had notable superiority than pathologist in detecting minor MIP component.
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Affiliation(s)
- Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Shengnan Zhao
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Haojie Si
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Jiani Gao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Fang Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiaofeng Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chenyang Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Clinical Center for Thoracic Surgery Research, Tongji University, Shanghai, People's Republic of China.
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Fan J, Yao J, Si H, Xie H, Ge T, Ye W, Chen J, Yin Z, Zhuang F, Xu L, Su H, Zhao S, Xie X, Zhao D, Wu C, Zhu Y, Ren Y, Xu N, Chen C. Frozen sections accurately predict the IASLC proposed grading system and prognosis in patients with invasive lung adenocarcinomas. Lung Cancer 2023; 178:123-130. [PMID: 36822017 DOI: 10.1016/j.lungcan.2023.02.010] [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: 11/18/2022] [Revised: 02/09/2023] [Accepted: 02/14/2023] [Indexed: 02/19/2023]
Abstract
INTRODUCTION The International Association for the Study of Lung Cancer (IASLC) newly proposed grading system for lung adenocarcinomas (ADC) has been shown to be of prognostic significance. Hence, intraoperative consultation for the grading system was important regarding the surgical decision-making. Here, we evaluated the accuracy and interobserver agreement for IASLC grading system on frozen section (FS), and further investigated the prognostic performance. METHODS FS and final pathology (FP) slides were reviewed by three pathologists for tumor grading in 373 stage I lung ADC following surgical resection from January to June 2013 (retrospective cohort). A prospective multicenter cohort (January to June 2021, n = 212) were included to confirm the results. RESULTS The overall concordance rates between FS and FP were 79.1% (κ = 0.650) and 89.6% (κ = 0.729) with substantial agreement in retrospective and prospective cohorts, respectively. Presence of complex gland was the only independent predictor of discrepancy between FS and FP (presence versus. absence: odds ratio, 2.193; P = 0.015). The interobserver agreement for IASLC grading system on FS among three pathologists were satisfactory (κ = 0.672 for retrospective cohort; κ = 0.752 for prospective cohort). Moreover, the IASLC grading system by FS diagnosis could well predict recurrence-free survival and overall survival for patients with stage I invasive lung ADC. CONCLUSIONS Our results suggest that FS had high diagnostic accuracy and satisfactory interobserver agreement for IASLC grading system. Future prospective studies are merited to validate the feasibility of using FS to match patients into appropriate surgical type.
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Affiliation(s)
- Junqiang Fan
- Department of Thoracic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Jie Yao
- Department of Thoracic Surgery, the Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, People's Republic of China
| | - Haojie Si
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Tengfei Ge
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, People's Republic of China
| | - Wei Ye
- Department of Pathology, Anhui Chest Hospital, Hefei, People's Republic of China
| | - Jianle Chen
- Department of Cardiothoracic Surgery, Affiliated Hospital of Nantong University, Nantong, People's Republic of China
| | - Zhongbo Yin
- Department of Pathology, the Sixth People's Hospital of Nantong, Nantong, People's Republic of China
| | - Fenghui Zhuang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Hang Su
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Shengnan Zhao
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Xiaofeng Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yuming Zhu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China
| | - Yijiu Ren
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, People's Republic of China.
| | - Ning Xu
- Department of Thoracic Surgery, Anhui Chest Hospital, Hefei, People's Republic of China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Clinical Center for Thoracic Surgery Research, Tongji University, Shanghai, People's Republic of China; The First Hospital of Lanzhou University, Lanzhou, Gansu Province, People's Republic of China.
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An ordinal radiomic model to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma based on low-dose computed tomography in lung cancer screening. Eur Radiol 2023; 33:3072-3082. [PMID: 36790469 DOI: 10.1007/s00330-023-09453-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/16/2022] [Accepted: 01/18/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES To construct a radiomic model of low-dose CT (LDCT) to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma (IPA) and compare its diagnostic performance with quantitative-semantic model and radiologists. METHODS A total of 682 pulmonary nodules were divided into the primary cohort (181 grade 1; 254 grade 2; 64 grade 3) and validation cohort (69 grade 1; 99 grade 2; 15 grade 3) according to scanners. The radiomic and quantitative-semantic models were built using ordinal logistic regression. The diagnostic performance of the models and radiologists was assessed by the area under the curve (AUC) of the receiver operating characteristic curve and accuracy. RESULTS The radiomic model demonstrated excellent diagnostic performance in the validation cohort (AUC, 0.900 (95%CI: 0.847-0.939) for Grade 1 vs. Grade 2/Grade 3; AUC, 0.929 (95%CI: 0.882-0.962) for Grade 1/Grade 2 vs. Grade 3; accuracy, 0.803 (95%CI: 0.737-0.857)). No significant difference in diagnostic performance was found between the radiomic model and radiological expert (AUC, 0.840 (95%CI: 0.779-0.890) for Grade 1 vs. Grade 2/Grade 3, p = 0.130; AUC, 0.852 (95%CI: 0.793-0.900) for Grade 1/Grade 2 vs. Grade 3, p = 0.170; accuracy, 0.743 (95%CI: 0.673-0.804), p = 0.079), but the radiomic model outperformed the quantitative-semantic model and inexperienced radiologists (all p < 0.05). CONCLUSIONS The radiomic model of LDCT can be used to predict the differentiation grade of IPA in lung cancer screening, and its diagnostic performance is comparable to that of radiological expert. KEY POINTS • Early identifying the novel differentiation grade of invasive non-mucinous pulmonary adenocarcinoma may provide guidance for further surveillance, surgical strategy, or more adjuvant treatment. • The diagnostic performance of the radiomic model is comparable to that of a radiological expert and superior to that of the quantitative-semantic model and inexperienced radiologists. • The radiomic model of low-dose CT can be used to predict the differentiation grade of invasive non-mucinous pulmonary adenocarcinoma in lung cancer screening.
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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: 3] [Impact Index Per Article: 3.0] [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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Huang S, Yang J, Shen N, Xu Q, Zhao Q. Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective. Semin Cancer Biol 2023; 89:30-37. [PMID: 36682439 DOI: 10.1016/j.semcancer.2023.01.006] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.
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Affiliation(s)
- Shigao Huang
- Department of Radiation Oncology, The First Affiliated Hospital, Air Force Medical University, Xi'an, Shanxi, China
| | - Jie Yang
- Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Na Shen
- Hong Kong Shue Yan University, Hong Kong, China
| | - Qingsong Xu
- Faculty of Science and Technology, University of Macau, Taipa, Macau SAR, China
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China; MoE Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau SAR, China.
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Zhao X, Zhao Y, Zhang J, Zhang Z, Liu L, Zhao X. Predicting PD-L1 expression status in patients with non-small cell lung cancer using [ 18F]FDG PET/CT radiomics. EJNMMI Res 2023; 13:4. [PMID: 36682020 PMCID: PMC9868196 DOI: 10.1186/s13550-023-00956-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 01/17/2023] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND In recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC. METHODS A total of 334 patients with NSCLC who underwent [18F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model. RESULTS Patients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220-0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively. CONCLUSION PET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.
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Affiliation(s)
- Xiaoqian Zhao
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China
| | - Yan Zhao
- grid.452582.cDepartment of Oncology, The Fourth Hospital of Hebei Medical University and Hebei Provincial Tumor Hospital, Shijiazhuang, Hebei China ,grid.452582.cDepartment of Tumor Immunotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei China
| | - Jingmian Zhang
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China ,Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei China
| | - Zhaoqi Zhang
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China
| | - Lihua Liu
- grid.452582.cDepartment of Tumor Immunotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011 Hebei China
| | - Xinming Zhao
- grid.452582.cDepartment of Nuclear Medicine, The Fourth Hospital of Hebei Medical University, 12 Jiankang Road, Shijiazhuang, 050011 Hebei China ,Hebei Provincial Key Laboratory of Tumor Microenvironment and Drug Resistance, Shijiazhuang, Hebei China
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Park C, Jeong DY, Choi Y, Oh YJ, Kim J, Ryu J, Paeng K, Lee SH, Ock CY, Lee HY. Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors. Front Immunol 2023; 13:1038089. [PMID: 36660547 PMCID: PMC9844154 DOI: 10.3389/fimmu.2022.1038089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/13/2022] [Indexed: 01/04/2023] Open
Abstract
Background Enrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through computed tomography (CT) radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we assess TIL enrichment objectively using an artificial intelligence-powered TIL analysis in hematoxylin and eosin (H&E) image and analyze its association with quantitative radiomic features (RFs). Clinical significance of the selected RFs is then validated in the independent NSCLC patients who received ICI. Methods In the training cohort containing both tumor tissue samples and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. The TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density divided by the whole TME area, as measured on an H&E slide. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was applied to CT images from the validation cohort, which included NSCLC patients who received ICI monotherapy. Results A total of 220 NSCLC samples were included in the training cohort. After filtering the RFs, two features, gray level variance (coefficient 1.71 x 10-3) and large area low gray level emphasis (coefficient -2.48 x 10-5), were included in the model. The two features were both computed from the size-zone matrix, which has strength in reflecting intralesional texture heterogeneity. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared to those with low predicted TILes (median 4.0 months [95% CI 2.2-5.7] versus 2.1 months [95% CI 1.6-3.1], p = 0.002). Patients who experienced a response to ICI or stable disease with ICI had higher predicted TILes compared with the patients who experienced progressive disease as the best response (p = 0.001, p = 0.036, respectively). Predicted TILes was significantly associated with progression-free survival independent of PD-L1 status. Conclusions In this CT radiomics model, predicted TILes was significantly associated with ICI outcomes in NSCLC patients. Analyzing TME through radiomics may overcome the limitations of tissue-based analysis and assist clinical decisions regarding ICI.
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Affiliation(s)
- Changhee Park
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Dong Young Jeong
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yeonu Choi
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - You Jin Oh
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea
| | - Jonghoon Kim
- Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | | | | | - Se-Hoon Lee
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea,Division of Hematology Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Chan-Young Ock
- Lunit, Seoul, Republic of Korea,*Correspondence: Chan-Young Ock, ; Ho Yun Lee,
| | - Ho Yun Lee
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea,Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea,*Correspondence: Chan-Young Ock, ; Ho Yun Lee,
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Li B, Jiang L, Lin D, Dong J. Registered Clinical Trials for Artificial Intelligence in Lung Disease: A Scoping Review on ClinicalTrials.gov. Diagnostics (Basel) 2022; 12:diagnostics12123046. [PMID: 36553052 PMCID: PMC9777443 DOI: 10.3390/diagnostics12123046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 11/06/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Clinical trials are the most effective tools to evaluate the advantages of various diagnostic and treatment modalities. AI used in medical issues, including screening, diagnosis, and treatment decisions, improves health outcomes and patient experiences. This study's objective was to investigate the traits of registered trials on artificial intelligence for lung disease. Clinical studies on AI for lung disease that were present in the ClinicalTrials.gov database were searched, and fifty-three registered trials were included. Forty-six (72.1%) were observational trials, compared to seven (27.9%) that were interventional trials. Only eight trials (15.4%) were completed. Thirty (56.6%) trials were accepting applicants. Clinical studies often included a large number of cases; for example, 24 (32.0%) trials included samples of 100-1000 cases, while 14 (17.5%) trials included samples of 1000-2000 cases. Of the interventional trials, twenty (15.7%) were retrospective studies and twenty (65.7%) were prospective studies.
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Affiliation(s)
- Bingjie Li
- Department of Thoracic Oncology and State Key Laboratory of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Lisha Jiang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Dan Lin
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
| | - Jingsi Dong
- Lung Cancer Center, West China Hospital, Sichuan University, Chengdu 610017, China
- Correspondence:
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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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Incremental benefits of size-zone matrix-based radiomics features for the prognosis of lung adenocarcinoma: advantage of spatial partitioning on tumor evaluation. Eur Radiol 2022; 32:7691-7699. [PMID: 35554645 DOI: 10.1007/s00330-022-08818-z] [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: 12/02/2021] [Revised: 04/04/2022] [Accepted: 04/13/2022] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Prognostic models of lung adenocarcinoma (ADC) can be built using radiomics features from various categories. The size-zone matrix (SZM) features have a strong biological basis related to tumor partitioning, but their incremental benefits have not been fully explored. In our study, we aimed to evaluate the incremental benefits of SZM features for the prognosis of lung ADC. METHODS A total of 298 patients were included and their pretreatment computed tomography images were analyzed in fivefold cross-validation. We built a risk model of overall survival using SZM features and compared it with a conventional radiomics risk model and a clinical variable-based risk model. We also compared it with other models incorporating various combinations of SZM features, other radiomics features, and clinical variables. A total of seven risk models were compared and evaluated using the hazard ratio (HR) on the left-out test fold. RESULTS As a baseline, the clinical variable risk model showed an HR of 2.739. Combining the radiomics signature with SZM feature was better (HR 4.034) than using radiomics signature alone (HR 3.439). Combining radiomics signature, SZM feature, and clinical variable (HR 6.524) fared better than just combining radiomics signature and clinical variables (HR 4.202). These results confirmed the added benefits of SZM features for prognosis in lung ADC. CONCLUSION Combining SZM feature with the radiomics signature was better than using the radiomics signature alone and the benefits of SZM features were maintained when clinical variables were added confirming the incremental benefits of SZM features for lung ADC prognosis. KEY POINTS • Size-zone matrix (SZM) features provide incremental benefits for the prognosis of lung adenocarcinoma. • Combining the radiomics signature with SZM features performed better than using a radiomics signature alone.
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Xie D, Xu F, Zhu W, Pu C, Huang S, Lou K, Wu Y, Huang D, He C, Hu H. Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy. Front Oncol 2022; 12:990608. [PMID: 36276082 PMCID: PMC9583844 DOI: 10.3389/fonc.2022.990608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/22/2022] [Indexed: 11/22/2022] Open
Abstract
Objective To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001). Conclusions The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.
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Affiliation(s)
- Dong Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaoyu Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong He
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Hongjie Hu,
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29
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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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30
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Zhu YQ, Liu C, Mo Y, Dong H, Huang C, Duan YN, Tang LL, Chu YY, Qin J. Radiomics for differentiating minimally invasive adenocarcinoma from precursor lesions in pure ground-glass opacities on chest computed tomography. Br J Radiol 2022; 95:20210768. [PMID: 35262392 PMCID: PMC10996418 DOI: 10.1259/bjr.20210768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Revised: 02/27/2022] [Accepted: 03/04/2022] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVE To explore the correlation between radiomic features and the pathology of pure ground-glass opacities (pGGOs), we established a radiomics model for predicting the pathological subtypes of minimally invasive adenocarcinoma (MIA) and precursor lesions. METHODS CT images of 1521 patients with lung adenocarcinoma or precursor lesions appearing as pGGOs on CT in our hospital (The Third Affiliated Hospital of Sun Yat-sen University) from January 2015 to March 2021 were analyzed retrospectively and selected based on inclusion and exclusion criteria. pGGOs were divided into an atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS) group and an MIA group. Radiomic features were extracted from the original and preprocessed images of the region of interest. ANOVA and least absolute shrinkage and selection operator feature selection algorithm were used for feature selection. Logistic regression algorithm was used to construct radiomics prediction model. Receiver operating characteristic curves were used to evaluate the classification efficiency. RESULTS 129 pGGOs were included. 2107 radiomic features were extracted from each region of interest. 18 radiomic features were eventually selected for model construction. The area under the curve of the radiomics model was 0.884 [95% confidence interval (CI), 0.818-0.949] in the training set and 0.872 (95% CI, 0.756-0.988) in the test set, with a sensitivity of 72.73%, specificity of 88.24% and accuracy of 79.47%. The decision curve indicated that the model had a high net benefit rate. CONCLUSION The prediction model for pathological subtypes of MIA and precursor lesions in pGGOs demonstrated a high diagnostic accuracy. ADVANCES IN KNOWLEDGE We focused on lesions appearing as pGGOs on CT and revealed the differences in radiomic features between MIA and precursor lesions. We constructed a radiomics prediction model and improved the diagnostic accuracy for the pathology of MIA and precursor lesions.
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Affiliation(s)
- Yan-qiu Zhu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Chaohui Liu
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Yan Mo
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Hao Dong
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Chencui Huang
- Department of Research Collaboration, R&D Center, Beijing
Deepwise & League of PHD Technology Co. Ltd,
Beijing, China
| | - Ya-ni Duan
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Lei-lei Tang
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Yuan-yuan Chu
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
| | - Jie Qin
- Department of Radiology, The Third Affiliated Hospital of Sun
Yat-sen University, No. 600 Tianhe Road, Tianhe District,
Guangzhou, China
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Anagnostopoulos AK, Gaitanis A, Gkiozos I, Athanasiadis EI, Chatziioannou SN, Syrigos KN, Thanos D, Chatziioannou AN, Papanikolaou N. Radiomics/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results. Cancers (Basel) 2022; 14:cancers14071657. [PMID: 35406429 PMCID: PMC8997041 DOI: 10.3390/cancers14071657] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 03/14/2022] [Accepted: 03/16/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Radiogenomics is a promising new approach in cancer assessment, providing an evaluation of the molecular basis of imaging phenotypes after establishing associations between radiological features and molecular features at the genomic–transcriptomic–proteomic level. This review focuses on describing key aspects of radiogenomics while discussing limitations of translatability to the clinic and possible solutions to these challenges, providing the clinician with an up-to-date handbook on how to use this new tool. Abstract Lung cancer is the leading cause of cancer-related deaths worldwide, and elucidation of its complicated pathobiology has been traditionally targeted by studies incorporating genomic as well other high-throughput approaches. Recently, a collection of methods used for cancer imaging, supplemented by quantitative aspects leading towards imaging biomarker assessment termed “radiomics”, has introduced a novel dimension in cancer research. Integration of genomics and radiomics approaches, where identifying the biological basis of imaging phenotypes is feasible due to the establishment of associations between molecular features at the genomic–transcriptomic–proteomic level and radiological features, has recently emerged termed radiogenomics. This review article aims to briefly describe the main aspects of radiogenomics, while discussing its basic limitations related to lung cancer clinical applications for clinicians, researchers and patients.
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Affiliation(s)
- Athanasios K. Anagnostopoulos
- Division of Biotechnology, Center of Systems Biology, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11525 Athens, Greece
- Correspondence:
| | - Anastasios Gaitanis
- Clinical and Translational Research, Center of Experimental Surgery, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece;
| | - Ioannis Gkiozos
- Third Department of Internal Medicine, “Sotiria” Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (K.N.S.)
| | - Emmanouil I. Athanasiadis
- Greek Genome Centre, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece; (E.I.A.); (D.T.)
| | - Sofia N. Chatziioannou
- Nuclear Medicine Division, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece;
| | - Konstantinos N. Syrigos
- Third Department of Internal Medicine, “Sotiria” Hospital, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (K.N.S.)
| | - Dimitris Thanos
- Greek Genome Centre, Biomedical Research Foundation of the Academy of Athens (BRFAA), 11527 Athens, Greece; (E.I.A.); (D.T.)
| | - Achilles N. Chatziioannou
- First Department of Radiology, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Nikolaos Papanikolaou
- Computational Clinical Imaging Group, Centre for the Unknown, Champalimaud Foundation, 1400-038 Lisbon, Portugal;
- Machine Learning Group, The Royal Marsden, London SM2 5MG, UK
- The Institute of Cancer Research, London SM2 5MG, UK
- Karolinska Institutet, 14186 Stockholm, Sweden
- Institute of Computer Science, FORTH, 70013 Heraklion, Greece
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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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Zhang G, Yang H, Zhu X, Luo J, Zheng J, Xu Y, Zheng Y, Wei Y, Mei Z, Shao G. A CT-Based Radiomics Nomogram to Predict Complete Ablation of Pulmonary Malignancy: A Multicenter Study. Front Oncol 2022; 12:841678. [PMID: 35223526 PMCID: PMC8866938 DOI: 10.3389/fonc.2022.841678] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 01/20/2022] [Indexed: 11/24/2022] Open
Abstract
Objective Thermal ablation is a minimally invasive procedure for the treatment of pulmonary malignancy, but the intraoperative measure of complete ablation of the tumor is mainly based on the subjective judgment of clinicians without quantitative criteria. This study aimed to develop and validate an intraoperative computed tomography (CT)-based radiomic nomogram to predict complete ablation of pulmonary malignancy. Methods This study enrolled 104 individual lesions from 92 patients with primary or metastatic pulmonary malignancies, which were randomly divided into training cohort (n=74) and verification cohort (n=30). Radiomics features were extracted from the original CT images when the study clinicians determined the completion of the ablation surgery. Minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) were adopted for the dimensionality reduction of high-dimensional data and feature selection. The prediction model was developed based on the radiomics signature combined with the independent clinical predictors by multiple logistic regression analysis. The area under the curve (AUC), accuracy, sensitivity, and specificity were calculated. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the predictive performance of the model. Decision curve analysis (DCA) was applied to estimate the clinical usefulness and net benefit of the nomogram for decision making. Results Thirteen CT features were selected to construct radiomics prediction model, which exhibits good predictive performance for determination of complete ablation of pulmonary malignancy. The AUCs of a CT-based radiomics nomogram that integrated the radiomics signature and the clinical predictors were 0.88 (95% CI 0.80-0.96) in the training cohort and 0.87 (95% CI: 0.71–1.00) in the validation cohort, respectively. The radiomics nomogram was well calibrated in both the training and validation cohorts, and it was highly consistent with complete tumor ablation. DCA indicated that the nomogram was clinically useful. Conclusion A CT-based radiomics nomogram has good predictive value for determination of complete ablation of pulmonary malignancy intraoperatively, which can assist in decision-making.
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Affiliation(s)
- Guozheng Zhang
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University (Quzhou People's Hospital), Quzhou, China
| | - Hong Yang
- Department of Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Xisong Zhu
- Department of Radiology, The Quzhou Affiliated Hospital of Wenzhou Medical University (Quzhou People's Hospital), Quzhou, China
| | - Jun Luo
- Department of Interventional Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jiaping Zheng
- Department of Interventional Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Yining Xu
- Department of Radiology, Huzhou Central Hospital, Huzhou, China
| | - Yifeng Zheng
- Department of Radiology, Huzhou Central Hospital, Huzhou, China
| | - Yuguo Wei
- Precision Health Institution, General Electric (GE) Healthcare, Hangzhou, China
| | - Zubing Mei
- Department of Anorectal Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.,Anorectal Disease Institute of Shuguang Hospital, Shanghai, China
| | - Guoliang Shao
- Department of Interventional Radiology, Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
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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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Cho HH, Lee HY, Kim E, Lee G, Kim J, Kwon J, Park H. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol 2021; 4:1286. [PMID: 34773070 PMCID: PMC8590002 DOI: 10.1038/s42003-021-02814-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 10/27/2021] [Indexed: 02/07/2023] Open
Abstract
Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller sample size requirements and enhanced interpretability. Baseline radiomics and DL models for the prognosis of lung adenocarcinomas were developed and tested using local (n = 617) cohort. The DL models were further tested in an external validation (n = 70) cohort. The local cohort was divided into training and test cohorts. A radiomics risk score (RRS) was developed using Cox-LASSO. Three pretrained DL networks derived from natural images were used to extract the DL features. The features were further guided using radiomics by retaining those DL features whose correlations with the radiomics features were high and Bonferroni-corrected p-values were low. The retained DL features were subject to a Cox-LASSO when constructing DL risk scores (DRS). The risk groups stratified by the RRS and DRS showed a significant difference in training, testing, and validation cohorts. The DL features were interpreted using existing radiomics features, and the texture features explained the DL features well.
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Affiliation(s)
- Hwan-Ho Cho
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
- Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.
| | - Eunjin Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Geewon Lee
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan, Republic of Korea
| | - Jonghoon Kim
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Junmo Kwon
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea.
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Republic of Korea.
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Xu Y, Li Y, Yin H, Tang W, Fan G. Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules. Front Oncol 2021; 11:725599. [PMID: 34568054 PMCID: PMC8461974 DOI: 10.3389/fonc.2021.725599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 08/19/2021] [Indexed: 01/31/2023] Open
Abstract
Introduction Tumors are continuously evolving biological systems which can be monitored by medical imaging. Previous studies only focus on single timepoint images, whether the performance could be further improved by using serial noncontrast CT imaging obtained during nodule follow-up management remains unclear. In this study, we evaluated DL model for predicting tumor invasiveness of GGNs through analyzing time series CT images. Methods A total of 168 pathologically confirmed GGN cases (48 noninvasive lesions and 120 invasive lesions) were retrospectively collected and randomly assigned to the development dataset (n = 123) and independent testing dataset (n = 45). All patients underwent consecutive noncontrast CT examinations, and the baseline CT and 3-month follow-up CT images were collected. The gross region of interest (ROI) patches containing only tumor region and the full ROI patches including both tumor and peritumor regions were cropped from CT images. A baseline model was built on the image features and demographic features. Four DL models were proposed: two single-DL model using gross ROI (model 1) or full ROI patches (model 3) from baseline CT images, and two serial-DL models using gross ROI (model 2) or full ROI patches (model 4) from consecutive CT images (baseline scan and 3-month follow-up scan). In addition, a combined model integrating serial full ROI patches and clinical information was also constructed. The performance of these predictive models was assessed with respect to discrimination and clinical usefulness. Results The area under the curve (AUC) of the baseline model, models 1, 2, 3, and 4 were 0.562 [(95% confidence interval (C)], 0.406~0.710), 0.693 (95% CI, 0.538-0.822), 0.787 (95% CI, 0.639-0.895), 0.727 (95% CI, 0.573-0.849), and 0.811 (95% CI, 0.667-0.912) in the independent testing dataset, respectively. The results indicated that the peritumor region had potential to contribute to tumor invasiveness prediction, and the model performance was further improved by integrating imaging scans at multiple timepoints. Furthermore, the combined model showed best discrimination ability, with AUC, sensitivity, specificity, and accuracy achieving 0.831 (95% CI, 0.690-0.926), 86.7%, 73.3%, and 82.2%, respectively. Conclusion The DL model integrating full ROIs from serial CT images shows improved predictive performance in differentiating noninvasive from invasive GGNs than the model using only baseline CT images, which could benefit the clinical management of GGNs.
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Affiliation(s)
- Yao Xu
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yu Li
- Department of Radiology, Dushuhu Public Hospital Affiliated of Soochow University, Suzhou, China
| | - Hongkun Yin
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Wen Tang
- Department of Advanced Research, Infervision Medical Technology Co. Ltd, Beijing, China
| | - Guohua Fan
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
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Xu Y, Ji W, Hou L, Lin S, Shi Y, Zhou C, Meng Y, Wang W, Chen X, Wang M, Yang H. Enhanced CT-Based Radiomics to Predict Micropapillary Pattern Within Lung Invasive Adenocarcinoma. Front Oncol 2021; 11:704994. [PMID: 34513686 PMCID: PMC8429899 DOI: 10.3389/fonc.2021.704994] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 08/09/2021] [Indexed: 12/25/2022] Open
Abstract
Objective We aimed to investigate whether enhanced CT-based radiomics can predict micropapillary pattern (MPP) of lung invasive adenocarcinoma (IAC) in the pre-op phase and to develop an individual diagnostic predictive model for MPP in IAC. Methods 170 patients who underwent complete resection for pathologically confirmed lung IAC were included in our study. Of these 121 were used as a training cohort and the other 49 as a test cohort. Clinical features and enhanced CT images were collected and assessed. Quantitative CT analysis was performed based on feature types including first order, shape, gray-level co-occurrence matrix-based, gray-level size zone matrix-based, gray-level run length matrix-based, gray-level dependence matrix-based, neighboring gray tone difference matrix-based features and transform types including Log, wavelet and local binary pattern. Receiver operating characteristic (ROC) and area under the curve (AUC) were used to value the ability to identify the lung IAC with MPP using these characteristics. Results Using quantitative CT analysis, one thousand three hundred and seventeen radiomics features were deciphered from R (https://www.r-project.org/). Then these radiomic features were decreased to 14 features after dimension reduction using the least absolute shrinkage and selection operator (LASSO) method in R. After correlation analysis, 5 key features were obtained and used as signatures for predicting MPP within IAC. The individualized prediction model which included age, smoking, family tumor history and radiomics signature had better identification (AUC=0.739) in comparison with the model consisting only of radiomics features (AUC=0.722). DeLong test showed that the difference in AUC between the two models was statistically significant (P<0.01). Compared with the simple radiomics model, the more comprehensive individual prediction model has better prediction performance. Conclusion The use of radiomics approach is of great value in the diagnosis of tumors by non-invasive means. The individualized prediction model in the study, when incorporated with age, smoking and radiomics signature, had effective predictive performance of lung IAC with MPP lesions. The combination of imaging features and clinical features can provide additional diagnostic value to identify the micropapillary pattern in IAC and can affect clinical diagnosis and treatment.
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Affiliation(s)
- Yunyu Xu
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China.,Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Wenbin Ji
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Liqiao Hou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Shuangxiang Lin
- Department of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, China
| | - Yangyang Shi
- Department of Radiation Oncology, University of Arizona, Tucson, AZ, United States
| | - Chao Zhou
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Yinnan Meng
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Wei Wang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
| | - Xiaofeng Chen
- Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IU, United States
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Haihua Yang
- Key Laboratory of Radiation Oncology of Taizhou, Radiation Oncology Institute of Enze Medical Health Academy, Department of Radiation Oncology, Taizhou Hospital Affiliated to Wenzhou Medical University, Taizhou, China
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Choi Y, Aum J, Lee SH, Kim HK, Kim J, Shin S, Jeong JY, Ock CY, Lee HY. Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma. Cancers (Basel) 2021; 13:4077. [PMID: 34439230 PMCID: PMC8391458 DOI: 10.3390/cancers13164077] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/02/2021] [Accepted: 08/09/2021] [Indexed: 01/18/2023] Open
Abstract
We aimed to develop a deep learning (DL) model for predicting high-grade patterns in lung adenocarcinomas (ADC) and to assess the prognostic performance of model in advanced lung cancer patients who underwent neoadjuvant or definitive concurrent chemoradiation therapy (CCRT). We included 275 patients with 290 early lung ADCs from an ongoing prospective clinical trial in the training dataset, which we split into internal-training and internal-validation datasets. We constructed a diagnostic DL model of high-grade patterns of lung ADC considering both morphologic view of the tumor and context view of the area surrounding the tumor (MC3DN; morphologic-view context-view 3D network). Validation was performed on an independent dataset of 417 patients with advanced non-small cell lung cancer who underwent neoadjuvant or definitive CCRT. The area under the curve value of the DL model was 0.8 for the prediction of high-grade histologic patterns such as micropapillary and solid patterns (MPSol). When our model was applied to the validation set, a high probability of MPSol was associated with worse overall survival (probability of MPSol >0.5 vs. <0.5; 5-year OS rate 56.1% vs. 70.7%), indicating that our model could predict the clinical outcomes of advanced lung cancer patients. The subgroup with a high probability of MPSol estimated by the DL model showed a 1.76-fold higher risk of death (HR 1.76, 95% CI 1.16-2.68). Our DL model can be useful in estimating high-grade histologic patterns in lung ADCs and predicting clinical outcomes of patients with advanced lung cancer who underwent neoadjuvant or definitive CCRT.
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Affiliation(s)
- Yeonu Choi
- Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
| | - Jaehong Aum
- Lunit Inc., Seoul 06241, Korea; (J.A.); (S.S.)
| | - Se-Hoon Lee
- Division of Hemato-Oncology, Department of Medicine, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
| | - Hong-Kwan Kim
- Department of Thoracic Surgery, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea; (H.-K.K.); (J.K.)
| | - Jhingook Kim
- Department of Thoracic Surgery, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea; (H.-K.K.); (J.K.)
| | | | - Ji Yun Jeong
- Department of Pathology, Kyungpook National University School of Medicine, Kyungpook National University Chilgok Hospital, Daegu 41404, Korea;
| | | | - Ho Yun Lee
- Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul 06351, Korea;
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Kuang Y, Li R, Jia P, Ye W, Zhou R, Zhu R, Wang J, Lin S, Pang P, Ji W. MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm. Abdom Radiol (NY) 2021; 46:3772-3789. [PMID: 33713159 DOI: 10.1007/s00261-021-02992-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 02/05/2021] [Accepted: 02/11/2021] [Indexed: 02/06/2023]
Abstract
PURPOSE To construct MRI radiomics nomograms that can predict short-term response after TACE in HCC patients with diameter less than 5 cm. METHODS MRI images and clinical data of 153 cases with tumor diameter less than 5 cm before TACE from 3 hospitals were collected retrospectively and divided into 1 internal training set and 1 external validation set. The T2-weighted imaging (T2WI) and dynamic contrast-enhanced MRI arterial phase (DCE-MR AP) images were studied. Multivariable logistic regression was used to construct Radiomics models, Clinics models, and Nomograms based on T2WI and DCE-MR AP, respectively. The receiver characteristic curve (ROC) was used to evaluate the predictive performance of each model. RESULTS In this study, 113 eligible cases in Hospital 1 were collected as the training set, and 40 eligible cases in other hospitals were used as the verification set. 11 T2WI features and 11 DCE-MRI AP features with the most predictive value were finally screened. 3 models based on T2WI and 3 models based on DCE-MRI AP were established, respectively. The area under curve (AUC) value of Nomogram based on T2WI of training set and validation set was 0.83 and 0.81, respectively. The AUC value of the models based on T2WI and models based on AP was almost equal, and Nomograms were the most effective models among all three types of models. CONCLUSION MRI-based Nomogram has greater predictive efficacy to predict the response after TACE than Radiomics and Clinics models alone, and the efficacy of T2WI-based models and DCE-MRI AP-based models was almost equal.
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Affiliation(s)
- Yani Kuang
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Renzhan Li
- Sanmen People's Hospital, Taizhou, China
| | - Peng Jia
- First People's Hospital of Taizhou city, Zhejiang, China
| | - Wenhai Ye
- Sanmen People's Hospital, Taizhou, China
| | - Rongzhen Zhou
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Rui Zhu
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Jian Wang
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | - Shuangxiang Lin
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China
| | | | - Wenbin Ji
- Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No. 150 Ximen Street, Linhai, Zhejiang, China.
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Wang C, Shao J, Lv J, Cao Y, Zhu C, Li J, Shen W, Shi L, Liu D, Li W. Deep learning for predicting subtype classification and survival of lung adenocarcinoma on computed tomography. Transl Oncol 2021; 14:101141. [PMID: 34087705 PMCID: PMC8184655 DOI: 10.1016/j.tranon.2021.101141] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 02/08/2023] Open
Abstract
OBJECTIVES The subtype classification of lung adenocarcinoma is important for treatment decision. This study aimed to investigate the deep learning and radiomics networks for predicting histologic subtype classification and survival of lung adenocarcinoma diagnosed through computed tomography (CT) images. METHODS A dataset of 1222 patients with lung adenocarcinoma were retrospectively enrolled from three medical institutions. The anonymised preoperative CT images and pathological labels of atypical adenomatous hyperplasia, adenocarcinoma in situ, minimally invasive adenocarcinoma, invasive adenocarcinoma (IAC) with five predominant components were obtained. These pathological labels were divided into 2-category classification (IAC; non-IAC), 3-category and 8-category. We modeled the classification task of histological subtypes based on modified ResNet-34 deep learning network, radiomics strategies and deep radiomics combined algorithm. Then we established the prognostic models in lung adenocarcinoma patients with survival outcomes. The accuracy (ACC), area under ROC curves (AUCs) and C-index were primarily performed to evaluate the algorithms. RESULTS This study included a training set (n = 802) and two validation cohorts (internal, n = 196; external, n = 224). The ACC of deep radiomics algorithm in internal validation achieved 0.8776, 0.8061 in the 2-category, 3-category classification, respectively. Even in 8 classifications, the AUC ranged from 0.739 to 0.940 in internal set. Further, we constructed a prognosis model that C-index was 0.892(95% CI: 0.846-0.937) in internal validation set. CONCLUSIONS The automated deep radiomics based triage system has achieved the great performance in the subtype classification and survival predictability in patients with CT-detected lung adenocarcinoma nodules, providing the clinical guide for treatment strategies.
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Affiliation(s)
- Chengdi Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, No. 37 Guo Xue Alley, Chengdu 610041, China
| | - Jun Shao
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, No. 37 Guo Xue Alley, Chengdu 610041, China
| | - Junwei Lv
- Hangzhou YITU Healthcare Technology Co., Ltd. Hangzhou, China; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Yidi Cao
- Hangzhou YITU Healthcare Technology Co., Ltd. Hangzhou, China; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Chaonan Zhu
- Hangzhou YITU Healthcare Technology Co., Ltd. Hangzhou, China; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Jingwei Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, No. 37 Guo Xue Alley, Chengdu 610041, China
| | - Wei Shen
- Hangzhou YITU Healthcare Technology Co., Ltd. Hangzhou, China; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Lei Shi
- Hangzhou YITU Healthcare Technology Co., Ltd. Hangzhou, China; Shanghai Key Laboratory of Artificial Intelligence for Medical Image and Knowledge Graph, Shanghai, China
| | - Dan Liu
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, No. 37 Guo Xue Alley, Chengdu 610041, China.
| | - Weimin Li
- Department of Respiratory and Critical Care Medicine, West China Hospital, West China Medical School, Sichuan University, No. 37 Guo Xue Alley, Chengdu 610041, China.
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Chen D, She Y, Wang T, Xie H, Li J, Jiang G, Chen Y, Zhang L, Xie D, Chen C. Radiomics-based prediction for tumour spread through air spaces in stage I lung adenocarcinoma using machine learning. Eur J Cardiothorac Surg 2021; 58:51-58. [PMID: 32011674 DOI: 10.1093/ejcts/ezaa011] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 12/18/2019] [Accepted: 12/25/2019] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES As evidence has proven that sublobar resection is oncologically contraindicated by tumour spread through air spaces (STAS), its preoperative recognition is vital in customizing surgical strategies. We aimed to assess the value of radiomics in predicting STAS in stage I lung adenocarcinoma. METHODS We retrospectively reviewed the patients with stage I lung adenocarcinoma, who accepted curative resection in our institution between January 2011 and December 2013. Using 'PyRadiomics' package, 88 radiomics features were extracted from computed tomography (CT) images and a prediction model was consequently constructed using Naïve Bayes machine-learning approach. The accuracy of the model was assessed through receiver operating curve analysis, and the performance of the model was validated both internally and externally. RESULTS A total of 233 patients were included as the training cohort with 69 (29.6%) patients being STAS (+). Patients with STAS had worse recurrence-free survival and overall survival (P < 0.001). After feature extraction, 5 most contributing radiomics features were selected out to develop a Naïve Bayes model. In the internal validation, the model exhibited good performance with an area under the curve value of 0.63 (0.55-0.71). External validation was conducted on a test cohort with 112 patients and produced an area under the curve value of 0.69. CONCLUSIONS CT-based radiomics is valuable in preoperatively predicting STAS in stage I lung adenocarcinoma, which may aid surgeons in determining the optimal surgical approach.
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Affiliation(s)
- Donglai Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Tingting Wang
- Department of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Huikang Xie
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jian Li
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Zunyi Medical College, Zunyi, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yongbing Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Soochow University, Medical College of Soochow University, Suzhou, China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
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Lee SH, Cho HH, Kwon J, Lee HY, Park H. Are radiomics features universally applicable to different organs? Cancer Imaging 2021; 21:31. [PMID: 33827699 PMCID: PMC8028225 DOI: 10.1186/s40644-021-00400-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 03/26/2021] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target organs in vastly different environments. METHODS Four datasets of three organs were analyzed. One radiomics model was constructed from the training set (lungs, n = 401), and was further evaluated in three independent test sets spanning three organs (lungs, n = 59; kidneys, n = 48; and brains, n = 43). Intensity histograms derived from the whole organ were compared to establish organ-level differences. We constructed a radiomics score based on selected features using training lung data over the tumor region. A total of 143 features were computed for each tumor. We adopted a feature selection approach that favored stable features, which can also capture survival. The radiomics score was applied to three independent test data from lung, kidney, and brain tumors, and whether the score could be used to separate high- and low-risk groups, was evaluated. RESULTS Each organ showed a distinct pattern in the histogram and the derived parameters (mean and median) at the organ-level. The radiomics score trained from the lung data of the tumor region included seven features, and the score was only effective in stratifying survival for other lung data, not in other organs such as the kidney and brain. Eliminating the lung-specific feature (2.5 percentile) from the radiomics score led to similar results. There were no common features between training and test sets, but a common category of features (texture category) was identified. CONCLUSION Although the possibility of a generally applicable model cannot be excluded, we suggest that radiomics score models for survival were mostly specific for a given organ; applying them to other organs would require careful consideration of organ-specific properties.
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Affiliation(s)
- Seung-Hak Lee
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
- Core Research & Development Center, Korea University Ansan Hospital, Ansan, 15355, South Korea
| | - Hwan-Ho Cho
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
| | - Junmo Kwon
- Departement of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon, 16419, South Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, South Korea.
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, 06351, South Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, 16419, South Korea.
- School of Electronic and Electrical Engineering, Center for Neuroscience Imaging Research, Sungkyunkwan University, Suwon, 16419, South Korea.
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Lee G, Park H, Lee HY, Ahn JH, Sohn I, Lee SH, Kim J. Tumor Margin Contains Prognostic Information: Radiomic Margin Characteristics Analysis in Lung Adenocarcinoma Patients. Cancers (Basel) 2021; 13:cancers13071676. [PMID: 33918164 PMCID: PMC8037340 DOI: 10.3390/cancers13071676] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/28/2021] [Accepted: 03/29/2021] [Indexed: 01/15/2023] Open
Abstract
Simple Summary The tumor microenvironment is a dynamic area, with continuous interaction between tumor cells and their surrounding environment. We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, Laplace of Gaussian (LoG) kurtosis 3, and roundness factor were independent predictive factors of overall survival. Our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information. Abstract We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin characteristics were correlated with overall survival. The selected clinical variables and radiomic margin characteristics were used to calculate a prognostic model with subsequent internal and external validation. Nearly all of the radiomic margin characteristics showed excellent reproducibility. The least absolute shrinkage and selection operator (LASSO) method was used to select eight radiomic margin characteristics. When compared to the model with clinical variables only (C-index = 0.738), the model incorporating clinical variables and radiomic margin characteristics (C-index = 0.753) demonstrated a higher C-index for predicting overall survival. In the model integrating both clinical variables and radiomic margin characteristics, convexity, a Laplace of Gaussian (LoG) kurtosis of 3, and the roundness factor were each independently predictive of overall survival. In addition, radiomic margin characteristics were also correlated with the micropapillary subtype, and the sphericity value was able to predict the presence of the micropapillary subtype. In conclusion, our study showed that radiomic margin characteristics helped predict overall survival in patients with lung adenocarcinomas, thus implying that the tumor margin contains prognostic information.
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Affiliation(s)
- Geewon Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Department of Radiology and Medical Research Institute, Pusan National University Hospital, Pusan National University School of Medicine, Busan 49241, Korea
| | - Hyunjin Park
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, Korea;
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon 16419, Korea
| | - Ho Yun Lee
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06355, Korea
- Correspondence:
| | - Joong Hyun Ahn
- Biostatistics and Clinical Epidemiology Center, Samsung Biomedical Research Institute, Seoul 06351, Korea; (J.H.A.); (I.S.)
| | - Insuk Sohn
- Biostatistics and Clinical Epidemiology Center, Samsung Biomedical Research Institute, Seoul 06351, Korea; (J.H.A.); (I.S.)
| | - Seung-Hak Lee
- Department of Electronic Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Korea;
- Core Research and Development Center, Korean University Ansan Hospital, Ansan 15355, Korea
| | - Jhingook Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea;
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Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, Mattonen SA, El Naqa I. Machine and deep learning methods for radiomics. Med Phys 2021; 47:e185-e202. [PMID: 32418336 DOI: 10.1002/mp.13678] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/22/2019] [Accepted: 06/13/2019] [Indexed: 12/12/2022] Open
Abstract
Radiomics is an emerging area in quantitative image analysis that aims to relate large-scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three-dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open-source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics-based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.
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Affiliation(s)
- Michele Avanzo
- Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
| | | | - Martin Vallières
- Medical Physics Unit, McGill University, Montreal, QC, Canada.,Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Arvind Rao
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.,Department of Computational Medicine & Bioinformatics, University of Michigan, Ann Arbor, MI, 48103, USA
| | - Olivier Morin
- Department of Radiation Oncology, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Sarah A Mattonen
- Department of Radiology, Stanford University, Stanford, CA, 94305, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA
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Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
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Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Tumor cell proliferation (Ki-67) expression and its prognostic significance in histological subtypes of lung adenocarcinoma. Lung Cancer 2021; 154:69-75. [PMID: 33626488 DOI: 10.1016/j.lungcan.2021.02.009] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/22/2021] [Accepted: 02/10/2021] [Indexed: 02/08/2023]
Abstract
OBJECTIVES Ki-67 is a key molecular marker to indicate the proliferative activity of tumor cells in lung cancer. However, Ki-67 expression and its prognostic significance in histological subtypes of lung adenocarcinoma (LUAD) remain unclear. MATERIALS AND METHODS We retrospectively analyzed 1028 invasive LUAD patients who underwent surgery treatment between January 2012 and April 2020 in our department. Associations between Ki-67 expression and histological subtypes of LUAD, as well as other clinicopathological characteristics, were evaluated. The prognostic role of Ki-67 in LUAD subtypes was further assessed using log-rank test and univariate/multivariate Cox proportional hazards regression analyses. RESULTS Ki-67 expression differed across LUAD histological subtypes. The solid-predominant adenocarcinoma (SPA, 46.31 ± 24.72) had the highest expression level of Ki-67, followed by micropapillary (MPA, 31.71 ± 18.14), papillary (PPA, 22.09 ± 19.61), acinar (APA, 19.73 ± 18.71) and lepidic-predominant adenocarcinoma (LPA, 9.86 ± 8.10, P < 0.001). Tumors with solid or micropapillary components also had a higher Ki-67 expression than those without solid or micropapillary components. Besides, males, smokers, larger tumor size, lymph node metastasis and EGFR wild type were correlated with elevated Ki-67 expression. Univariate analysis indicated that increased Ki-67 expression and MPA/SPA subtypes were significantly associated with a poorer prognosis. Notably, the survival differences between LUAD subtypes vanished after adjusting for tumor size and Ki-67 expression in multivariate analysis, while Ki-67 was an independent prognostic factor of LUAD. Patients with MPA/SPA had non-inferior overall and disease-free survival than LPA/APA/PPA patients with a Ki-67 expression comparable to MPA/SPA subjects. CONCLUSION Ki-67 expression varied considerably according to the predominant histological subtypes of LUAD. Ki-67 expression level and tumor size contributed to the survival differences between LUAD histological subtypes.
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He B, Song Y, Wang L, Wang T, She Y, Hou L, Zhang L, Wu C, Babu BA, Bagci U, Waseem T, Yang M, Xie D, Chen C. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res 2021; 10:955-964. [PMID: 33718035 PMCID: PMC7947386 DOI: 10.21037/tlcr-21-44] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 02/24/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Micropapillary/solid (MP/S) growth patterns of lung adenocarcinoma are vital for making clinical decisions regarding surgical intervention. This study aimed to predict the presence of a MP/S component in lung adenocarcinoma using radiomics analysis. METHODS Between January 2011 and December 2013, patients undergoing curative invasive lung adenocarcinoma resection were included. Using the "PyRadiomics" package, we extracted 90 radiomics features from the preoperative computed tomography (CT) images. Subsequently, four prediction models were built by utilizing conventional machine learning approaches fitting into radiomics analysis: a generalized linear model (GLM), Naïve Bayes, support vector machine (SVM), and random forest classifiers. The models' accuracy was assessed using a receiver operating curve (ROC) analysis, and the models' stability was validated both internally and externally. RESULTS A total of 268 patients were included as a primary cohort, and 36.6% (98/268) of them had lung adenocarcinoma with an MP/S component. Patients with an MP/S component had a higher rate of lymph node metastasis (18.4% versus 5.3%) and worse recurrence-free and overall survival. Five radiomics features were selected for model building, and in the internal validation, the four models achieved comparable performance of MP/S prediction in terms of area under the curve (AUC): GLM, 0.74 [95% confidence interval (CI): 0.65-0.83]; Naïve Bayes, 0.75 (95% CI: 0.65-0.85); SVM, 0.73 (95% CI: 0.61-0.83); and random forest, 0.72 (95% CI: 0.63-0.81). External validation was performed using a test cohort with 193 patients, and the AUC values were 0.70, 0.72, 0.73, and 0.69 for Naïve Bayes, SVM, random forest, and GLM, respectively. CONCLUSIONS Radiomics-based machine learning approach is a very strong tool for preoperatively predicting the presence of MP/S growth patterns in lung adenocarcinoma, and can help customize treatment and surveillance strategies.
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Affiliation(s)
- Bingxi He
- School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yongxiang Song
- Department of Thoracic Surgery, Affiliated Hospital of Zunyi Medical College, Guizhou, China
| | - Lili Wang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tingting Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Benson A. Babu
- Department of Internal Medicine, Lenox Hill Northwell Health, New York, NY, USA
| | - Ulas Bagci
- Department of Radiology, Northwestern University, Chicago, IL, USA
| | - Tayab Waseem
- Department of Molecular Biology and Cell Biology, Eastern Virginia Medical School Norfolk, VA, USA
| | - Minglei Yang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
- Department of Thoracic Surgery, Ningbo No. 2 Hospital, Chinese Academy of Sciences, Ningbo, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
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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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Wang L, Li T, Hong J, Zhang M, Ouyang M, Zheng X, Tang K. 18F-FDG PET-based radiomics model for predicting occult lymph node metastasis in clinical N0 solid lung adenocarcinoma. Quant Imaging Med Surg 2021; 11:215-225. [PMID: 33392023 DOI: 10.21037/qims-20-337] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background This study aimed to develop a preoperative positron emission tomography (PET)-based radiomics model for predicting occult lymph node metastasis (OLM) in clinical N0 (cN0) solid lung adenocarcinoma. Methods The preoperative fluorine-18-fludeoxyglucose (18F-FDG) PET images of 370 patients with cN0 lung adenocarcinoma confirmed by histopathological examination were retrospectively reviewed. Patients were divided into training and validation sets. Radiomics features and relevant data were extracted from PET images. A nomogram was developed in a training set via univariate and multivariate logistic analyses, and its performance was assessed by concordance-index (C-index), calibration curves, and decision curve analysis (DCA) in the training and validation sets. Results The multivariate logistic regression analysis showed that only carcinoembryonic antigen (CEA), metabolic tumor volume (MTV), and the radiomics signature had statistically significant differences between patients with and without OLM (P<0.05). A nomogram was developed based on the logistic analyses, and its C-index was 0.769 in the training set and 0.768 in the validation set. The calibration curve demonstrated good consistency between the nomogram-predicted probability of OLM and the actual rate. The DCA also confirmed the clinical utility of the nomogram. Conclusions A PET/computed tomography (CT)-based radiomics model including CEA, MTV, and the radiomics signature was developed and demonstrated adequate predictive accuracy and clinical net benefit in the present study, and was conveniently used to facilitate the individualized preoperative prediction of OLM.
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Affiliation(s)
- Lili Wang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Tiancheng Li
- PET Center, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Junjie Hong
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingyue Zhang
- Department of Radiology, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Mingli Ouyang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiangwu Zheng
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Kun Tang
- Department of PET/CT, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
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Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020; 4:24. [PMID: 34191197 PMCID: PMC8218106 DOI: 10.1186/s41824-020-00094-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/26/2020] [Indexed: 12/20/2022] Open
Abstract
Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.
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Affiliation(s)
- Martina Sollini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy
- Humanitas Clinical and Research Center, Rozzano (Milan), Italy
| | - Francesco Bartoli
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Andrea Marciano
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Roberta Zanca
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Riemer H J A Slart
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands
- Faculty of Science and Technology, Biomedical Photonic Imaging, University of Twente, Enschede, The Netherlands
| | - Paola A Erba
- Regional Center of Nuclear Medicine, Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
- University Medical Center Groningen, Medical Imaging Center, University of Groningen, Groningen, The Netherlands.
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