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Iwano S, Kamiya S, Ito R, Kudo A, Kitamura Y, Nakamura K, Naganawa S. Measurement of solid size in early-stage lung adenocarcinoma by virtual 3D thin-section CT applied artificial intelligence. Sci Rep 2023; 13:21709. [PMID: 38066174 PMCID: PMC10709591 DOI: 10.1038/s41598-023-48755-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
An artificial intelligence (AI) system that reconstructs virtual 3D thin-section CT (TSCT) images from conventional CT images by applying deep learning was developed. The aim of this study was to investigate whether virtual and real TSCT could measure the solid size of early-stage lung adenocarcinoma. The pair of original thin-CT and simulated thick-CT from the training data with TSCT images (thickness, 0.5-1.0 mm) of 2700 pulmonary nodules were used to train the thin-CT generator in the generative adversarial network (GAN) framework and develop a virtual TSCT AI system. For validation, CT images of 93 stage 0-I lung adenocarcinomas were collected, and virtual TSCTs were reconstructed from conventional 5-mm thick-CT images using the AI system. Two radiologists measured and compared the solid size of tumors on conventional CT and virtual and real TSCT. The agreement between the two observers showed an almost perfect agreement on the virtual TSCT for solid size measurements (intraclass correlation coefficient = 0.967, P < 0.001, respectively). The virtual TSCT had a significantly stronger correlation than that of conventional CT (P = 0.003 and P = 0.001, respectively). The degree of agreement between the clinical T stage determined by virtual TSCT and the clinical T stage determined by real TSCT was excellent in both observers (k = 0.882 and k = 0.881, respectively). The AI system developed in this study was able to measure the solid size of early-stage lung adenocarcinoma on virtual TSCT as well as on real TSCT.
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Affiliation(s)
- Shingo Iwano
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan.
| | - Shinichiro Kamiya
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
| | - Akira Kudo
- Imaging Technology Center, Fujifilm Corporation, 2-26-30, Nishiazabu, Minato-ku, Tokyo, 106-8620, Japan
| | - Yoshiro Kitamura
- Imaging Technology Center, Fujifilm Corporation, 2-26-30, Nishiazabu, Minato-ku, Tokyo, 106-8620, Japan
| | - Keigo Nakamura
- Imaging Technology Center, Fujifilm Corporation, 2-26-30, Nishiazabu, Minato-ku, Tokyo, 106-8620, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, 466-8550, Japan
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Zhu Y, Chen LL, Luo YW, Zhang L, Ma HY, Yang HS, Liu BC, Li LJ, Zhang WB, Li XM, Xie CM, Yang JC, Wang DL, Li Q. Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma. Eur Radiol 2023; 33:8542-8553. [PMID: 37436506 DOI: 10.1007/s00330-023-09845-0] [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: 11/18/2022] [Revised: 03/24/2023] [Accepted: 04/21/2023] [Indexed: 07/13/2023]
Abstract
OBJECTIVES To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.
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Affiliation(s)
- Ying Zhu
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Li-Li Chen
- Department of Pathology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Ying-Wei Luo
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China
| | - Li Zhang
- Dianei Technology, Shanghai, 200000, People's Republic of China
| | - Hui-Yun Ma
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China
| | - Hao-Shuai Yang
- Department of Thoracic Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Bao-Cong Liu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China
| | - Lu-Jie Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Wen-Biao Zhang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China
| | - Xiang-Min Li
- Department of Radiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080, Province Guangdong, People's Republic of China
| | - Chuan-Miao Xie
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China
| | - Jian-Cheng Yang
- Dianei Technology, Shanghai, 200000, People's Republic of China.
- Shanghai Jiao Tong University, Shanghai, China.
- EPFL, Lausanne, Switzerland.
| | - De-Ling Wang
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, Province Guangdong, People's Republic of China.
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Koike S, Shimizu K, Ide S, Mishima S, Matsuoka S, Takeda T, Miura K, Eguchi T, Hamanaka K, Araki T, Sonehara K, Todoroki K, Ichinohe F, Kawakami S, Koinuma M. Is using a consolidation tumor ratio 0.5 as criterion feasible in daily practice? Evaluation of interobserver measurement variability of consolidation tumor ratio of lung cancer less than 3 cm in size. Thorac Cancer 2022; 13:3018-3024. [PMID: 36193574 PMCID: PMC9626346 DOI: 10.1111/1759-7714.14653] [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: 06/27/2022] [Revised: 08/28/2022] [Accepted: 08/30/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Consolidation tumor ratio (CTR) calculated as the ratio of the tumor consolidation diameter to the tumor maximum diameter on thin-section computed tomography (CT) of lung cancer has been reported as an important prognostic factor. It has also been used for treatment decision-making. This study aimed to investigate the interobserver variability of CTR measurements on preoperative CT and propose a clinically useful CTR-based classification criterion. METHODS We enrolled 119 patients who underwent surgery for suspected or diagnosed small-sized lung cancer (≤3.0 cm in diameter). Nine doctors reviewed preoperative CT scans to measure CTR. Interobserver variability of CTR measurements was evaluated using the coefficient of variation (CV) and Fleiss' κ. The prognostic effect of the CTR-based classification was assessed using the Kaplan-Meier method. RESULTS Interobserver variability of CTR measurement was the highest for tumors with the lowest CTR (CTR = 0); it decreased as CTR increased and reached a plateaued level of low variability (CV <0.5) at CTR of 0.5. We proposed a three-group classification based on the findings of CTR interobserver variability (CTR < 0.5, 0.5 ≤ CTR < 1, and CTR = 1). Interobserver agreement of the judgment of the CTR-based classification was excellent (Fleiss' κ = 0.81). The classification significantly stratified patient prognosis (p < 0.001, 5-year overall survival rates with CTR < 0.5, 0.5 ≤ CTR < 1, and CTR = 1 were 100, 88, and 73.8%, respectively). CONCLUSIONS CTR 0.5 is a clinically relevant and helpful cutoff for treatment decision-making in patients with early-stage lung cancer based on high interobserver agreement and good prognostic stratification.
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Affiliation(s)
- Sachie Koike
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Kimihiro Shimizu
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Shogo Ide
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Shuji Mishima
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Shunichiro Matsuoka
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Tetsu Takeda
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Kentaro Miura
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Takashi Eguchi
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Kazutoshi Hamanaka
- Division of General Thoracic Surgery, Department of SurgeryShinshu University School of MedicineNaganoJapan
| | - Taisuke Araki
- First Department of Internal MedicineShinshu University School of MedicineNaganoJapan
| | - Kei Sonehara
- First Department of Internal MedicineShinshu University School of MedicineNaganoJapan
| | - Keisuke Todoroki
- Department of RadiologyShinshu University School of MedicineNaganoJapan
| | - Fumihito Ichinohe
- Department of RadiologyShinshu University School of MedicineNaganoJapan
| | - Satoshi Kawakami
- Department of RadiologyShinshu University School of MedicineNaganoJapan
| | - Masayoshi Koinuma
- Faculty of Pharmaceutical SciencesTeikyo Heisei UniversityTokyoJapan,Center for Clinical ResearchShinshu University HospitalNaganoJapan
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Li M, Xi J, Sui Q, Kuroda H, Hamanaka K, Bongiolatti S, Hong G, Zhan C, Feng M, Wang Q, Tan L. Impact of a Ground-glass Opacity Component on c-Stage IA Lung Adenocarcinoma. Semin Thorac Cardiovasc Surg 2022; 35:783-795. [PMID: 35907612 DOI: 10.1053/j.semtcvs.2022.07.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 07/21/2022] [Indexed: 11/11/2022]
Abstract
Studies have shown that tumors with ground-glass opacity (GGO) components are associated with favorable outcomes. However, this view should be confirmed in an international cohort. We aimed to verify the impact of a GGO component on clinical (c)-stage IA lung adenocarcinoma and to describe the biological discrepancies between the part-solid and pure-solid groups. We evaluated 1333 cases of surgically resected c-stage IA lung adenocarcinomas, including 484 part-solid and 849 pure-solid tumors. Furthermore, we matched the solid size between the 2 groups and examined 470 patients. We compared the prognoses between the 2 groups before and after matching. The prognostic and biological differences were described before and after matching. Compared with the pure-solid group, the part-solid group was associated with favorable outcomes [5-year overall survival (OS) 99.4% vs 87.6%, P < 0.001; 5-year recurrence-free survival (RFS) 96.9% vs 82.2%, P < 0.001]. Similar results were obtained after matching (5-year OS 98.9% vs 92.2%, P = 0.012; 5-year RFS 95.0% vs 88.5%, P = 0.007). Multivariable analyses revealed that GGO component appearance was a factor of better OS and RFS. The part-solid tumor, regardless of the size of the solid component, had a similar outcome to the pure-solid tumor of c-stage T1a classification. Also, more epidermal growth factor receptor, human epidermal growth factor receptor-2 mutations, and receptor tyrosine kinase ROS-1-positive were observed in the part-solid group. In comparison, more wild types and Kirsten-Ras were observed in the pure-solid group. Adenocarcinomas with a GGO component were associated with superior outcomes. The GGO component should be considereda new clinical T descriptor. Early-stage lung adenocarcinomas with and without a GGO component may be 2 distinct tumor types.
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Affiliation(s)
- Ming Li
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Junjie Xi
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Qihai Sui
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Hiroaki Kuroda
- Department of Thoracic Surgery, Aichi Cancer Center Hospital, Nagoya, Japan
| | - Kazutoshi Hamanaka
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University School of Medicine, Nagano, Japan
| | | | - Goohyeon Hong
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea
| | - Cheng Zhan
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Mingxiang Feng
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
| | - Qun Wang
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China.
| | - Lijie Tan
- Department of Thoracic Surgery, People's Republic of China and Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China
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Hamanaka K, Eguchi T, Shimizu K. Eighth edition T category is prognostic: the size of the solid component matters, not the ratio. J Thorac Dis 2020; 12:3426-3428. [PMID: 32642270 PMCID: PMC7330786 DOI: 10.21037/jtd.2020.01.50] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Kazutoshi Hamanaka
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University School of Medicine, Nagano, Japan
| | - Takashi Eguchi
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University School of Medicine, Nagano, Japan
| | - Kimihiro Shimizu
- Division of General Thoracic Surgery, Department of Surgery, Shinshu University School of Medicine, Nagano, Japan
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Jokerst C. Case of the Season: Management of the Subsolid Pulmonary Nodule. Semin Roentgenol 2019; 55:5-13. [PMID: 31964480 DOI: 10.1053/j.ro.2019.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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