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Liu M, Li M, Feng H, Jiang X, Zheng R, Zhang X, Li J, Liang X, Zhang L. Risk assessment of persistent incidental pulmonary subsolid nodules to guide appropriate surveillance interval and endpoints. Pulmonology 2025; 31:2423541. [PMID: 39883492 DOI: 10.1080/25310429.2024.2423541] [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: 06/09/2024] [Accepted: 10/22/2024] [Indexed: 01/31/2025] Open
Abstract
Guidelines for the follow-up of pulmonary subsolid nodule (SSN) vary in terms of frequency and criteria for discontinuation. We aimed to evaluate the growth risk of SSNs and define appropriate follow-up intervals and endpoints. The immediate risk (IR) and cumulative risk (CR) of SSN growth were assessed using the Kaplan-Meier method according to nodule consistency and size. Follow-up plans were proposed based on optimal growth risk threshold of 5%. 892 SSNs, comprising 833 pure ground-glass nodules (pGGNs) and 59 part-solid nodules (PSNs) were included. For pGGNs ≤ 6.6 mm, the CR exceeded 5% at every 3-year interval in the first 9 years. For pGGNs measuring 6.6-8.8 mm and >8.8 mm, the IR remained above 5% for the first 2-7 years, and the 2-year CR for pGGNs measuring 6.6-8.8 mm in the 8th and 9th years achieved 6.66%. For PSNs, the IR peaked in the 4th year (44%) and then declined. Therefore, triennial follow-up for 9 years is recommended for pGGNs ≤ 6.6 mm, annual follow-up for 7 years followed by biennial follow-up for 2 years for pGGNs measuring 6.6-8.8 mm, annual follow-up for 7 years for pGGNs > 8.8 mm, and continuous annual follow-up until nodule growth for PSNs.
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Affiliation(s)
- Mengwen Liu
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Meng Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Hao Feng
- Department of Cardiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Xu Jiang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Rongshou Zheng
- National Central Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xue Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jianwei Li
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xin Liang
- Medical Statistics Office, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Li Zhang
- Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Raad RA, Garrana S, Moreira AL, Moore WH, Ko JP. Imaging and Management of Subsolid Lung Nodules. Radiol Clin North Am 2025; 63:517-535. [PMID: 40409933 DOI: 10.1016/j.rcl.2024.12.002] [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] [Indexed: 05/25/2025]
Abstract
Subsolid nodules (SSNs) are increasingly encountered in chest computed tomography (CT) imaging and clinical practice, as awareness of their significance and CT utilization grows. Either part-solid or solely ground-glass in attenuation, SSNs are shown to correlate with lung adenocarcinomas and their precursors, although a differential diagnosis is to be considered that includes additional neoplastic and inflammatory etiologies. This review discusses the differential diagnosis for SSNs, imaging and clinical features, and pathology that are helpful when making management decisions that may include PET/CT, biopsy, or surgery. Potential pitfalls in nodule characterization and management will be highlighted, to aid in managing SSNs appropriately.
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Affiliation(s)
- Roy A Raad
- Department of Radiology, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY 10016, USA.
| | - Sherief Garrana
- Department of Radiology, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY 10016, USA
| | - Andre L Moreira
- Department of Pathology, NYU Langone Health, 560 First Avenue, TH4 15J, New York, NY 10016, USA
| | - William H Moore
- Department of Radiology, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY 10016, USA
| | - Jane P Ko
- Department of Radiology, NYU Langone Health, 660 First Avenue, 3rd Floor, New York, NY 10016, USA
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Li H, Salehjahromi M, Godoy MCB, Qin K, Plummer CM, Zhang Z, Hong L, Heeke S, Le X, Vokes N, Zhang B, Araujo HA, Altan M, Wu CC, Antonoff MB, Ostrin EJ, Gibbons DL, Heymach JV, Lee JJ, Gerber DE, Wu J, Zhang J. Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model. Cancers (Basel) 2025; 17:1499. [PMID: 40361426 PMCID: PMC12070823 DOI: 10.3390/cancers17091499] [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: 03/17/2025] [Revised: 04/26/2025] [Accepted: 04/28/2025] [Indexed: 05/15/2025] Open
Abstract
BACKGROUND/OBJECTIVES Persistent pulmonary nodules are at higher risk of developing into lung cancers. Assessing their future cancer risk is essential for successful interception. We evaluated the performance of two risk prediction models for persistent nodules in hospital-based cohorts: the Brock model, based on clinical and radiological characteristics, and the Sybil model, a novel deep learning model for lung cancer risk prediction. METHODS Patients with persistent pulmonary nodules-defined as nodules detected on at least two computed tomography (CT) scans, three months apart, without evidence of shrinkage-were included in the retrospective (n = 130) and prospective (n = 301) cohorts. We analyzed the correlations between demographic factors, nodule characteristics, and Brock scores and assessed the performance of both models. We also built machine learning models to refine the risk assessment for our cohort. RESULTS In the retrospective cohort, Brock scores ranged from 0% to 85.82%. In the prospective cohort, 62 of 301 patients were diagnosed with lung cancer, displaying higher median Brock scores than those without lung cancer diagnosis (18.65% vs. 4.95%, p < 0.001). Family history, nodule size ≥10 mm, part-solid nodule types, and spiculation were associated with the risks of lung cancer. The Brock model had an AUC of 0.679, and Sybil's AUC was 0.678. We tested five machine learning models, and the logistic regression model achieved the highest AUC at 0.729. CONCLUSIONS For patients with persistent pulmonary nodules in real-world cancer hospital-based cohorts, both the Brock and Sybil models had values and limitations for lung cancer risk prediction. Optimizing predictive models in this population is crucial for improving early lung cancer detection and interception.
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Affiliation(s)
- Hui Li
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Morteza Salehjahromi
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Myrna C. B. Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Kang Qin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Courtney M. Plummer
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Zheng Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Lingzhi Hong
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Simon Heeke
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Natalie Vokes
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Bingnan Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Haniel A. Araujo
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Mehmet Altan
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - Carol C. Wu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Mara B. Antonoff
- Department of Thoracic and Cardiovascular Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Edwin J. Ostrin
- Department of General Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
- Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L. Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Molecular and Cellular Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - John V. Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
| | - J. Jack Lee
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - David E. Gerber
- Harold C. Simmons Comprehensive Cancer Center, UT Southwestern Medical Center, Dallas, TX 75390, USA;
| | - Jia Wu
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Jianjun Zhang
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; (H.L.); (K.Q.); (C.M.P.); (Z.Z.); (L.H.); (S.H.); (X.L.); (N.V.); (B.Z.); (H.A.A.); (M.A.); (D.L.G.); (J.V.H.)
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA;
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Otabe M, Mimae T, Miyata Y, Tsubokawa N, Kudo Y, Nagashima T, Ito H, Ikeda N, Okada M. Role of 18F-fluorodeoxyglucose accumulation in radiological ground-glass opacity of non-small cell lung cancer. Jpn J Clin Oncol 2025; 55:391-398. [PMID: 39909863 DOI: 10.1093/jjco/hyae185] [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: 09/04/2024] [Accepted: 02/04/2025] [Indexed: 02/07/2025] Open
Abstract
BACKGROUND This study aimed to elucidate the significance of the maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) by radiological ground glass opacity (GGO) tumors of non-small cell lung cancer (NSCLC), particularly in tumors assumed to be pathologically non-invasive. METHODS Overall, 709 consecutive patients with GGO-dominant NSCLC who underwent complete resections at three institutions between 2017 and 2022 were included. GGO-dominant tumors and pure GGO tumors were evaluated based on the SUVmax. The adenocarcinoma subtypes were categorized into low, medium, and high grade. The correlation between the SUVmax, pathological malignant grade, and pathological invasive diameter was examined. RESULTS In GGO-dominant lung adenocarcinoma, the SUVmax correlated positively with the pathological malignant grade and the pathological invasive diameters (respectively, (P = .0001), (P < .0001)). Similarly, in pure GGO lung adenocarcinoma, the SUVmax correlated positively with the pathological malignant grade. The median pathological invasive diameter was higher in patients with SUVmax ≥ 1.0 compared to those with SUVmax < 1.0 [10 mm vs 0 mm, respectively, (P = .017)]. CONCLUSIONS A higher accumulation of FDG than in the background lung reflects invasive components even in pure GGO areas where only non-invasive components are expected. An FDG-PET/CT can complement the qualitative diagnosis in predicting invasive components with limitations in high-resolution computed tomography alone.
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Affiliation(s)
- Masaya Otabe
- Department of Surgical Oncology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takahiro Mimae
- Department of Surgical Oncology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yoshihiro Miyata
- Department of Surgical Oncology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Norifumi Tsubokawa
- Department of Surgical Oncology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Yujin Kudo
- Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Takuya Nagashima
- Department of Thoracic Surgery, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi, Yokohama, Kanagawa, 241-8515, Japan
| | - Hiroyuki Ito
- Department of Thoracic Surgery, Kanagawa Cancer Center, 2-3-2 Nakao, Asahi, Yokohama, Kanagawa, 241-8515, Japan
| | - Norihiko Ikeda
- Department of Surgery, Tokyo Medical University, 6-7-1 Nishishinjuku, Shinjuku-ku, Tokyo, 160-0023, Japan
| | - Morihito Okada
- Department of Surgical Oncology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
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Zou PL, Ma CH, Li X, Luo TY, Lv FJ, Li Q. Early Lung Adenocarcinoma Manifesting as Irregular Subsolid Nodules: Clinical and CT Characteristics. Acad Radiol 2025; 32:2320-2329. [PMID: 39732616 DOI: 10.1016/j.acra.2024.12.010] [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: 11/09/2024] [Revised: 12/06/2024] [Accepted: 12/07/2024] [Indexed: 12/30/2024]
Abstract
RATIONALE AND OBJECTIVES To explore the clinical and computed tomography (CT) characteristics of early-stage lung adenocarcinoma (LADC) that presents with an irregular shape. MATERIALS AND METHODS The CT data of 575 patients with stage IA LADC and 295 with persistent inflammatory lesion (PIL) manifesting as subsolid nodules (SSNs) were analyzed retrospectively. Among these patients, we selected 233 patients with LADC and 140 patients with PIL, who showed irregular SSNs, hereinafter referred to as irregular LADC (I-LADC) and irregular PIL (I-PIL), respectively. The incidence rates, clinical characteristics, and CT features of I-LADC and I-PIL were compared. Additionally, binary logistic regression analysis was performed to determine the independent factors for diagnosing I-LADC. RESULTS The incidence rates of I-LADC and I-PIL were 40.5% (233/575) and 47.5% (140/295), respectively, with no statistically significant difference observed between the two groups (P > 0.05). Univariate analysis revealed significant differences in three clinical characteristics and 13 radiological features between I-LADC and I-PIL (all P < 0.05). Binary logistic regression indicated that the alignment of the long axis of SSN with the bronchial vascular bundle, a well-defined boundary of ground-glass opacity, lobulation, arc concave sign, and absence of knife-like change were the independent predictors of I-LADC, yielding an area under the curve and accuracy of 0.979% and 93.5%, respectively. CONCLUSION Early LADC presenting as SSNs is associated with a high incidence of irregular shape. I-LADC and I-PIL exhibited different clinical and imaging characteristics. A good understanding of these differences may be helpful for the accurate diagnosis of I-LADC.
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Affiliation(s)
- Pei-Ling Zou
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China (P.-l.Z., T.-y.L., F.-j.L., Q.L.); Department of Radiology, Shapingba Hospital affiliated to Chongqing University, Chongqing, China (P.-l.Z.).
| | - Chao-Hao Ma
- Department of Ultrasound, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China (C.-h.M.).
| | - Xian Li
- Department of Pathology, Chongqing Medical University, Chongqing, China (X.L.).
| | - Tian-You Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China (P.-l.Z., T.-y.L., F.-j.L., Q.L.).
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China (P.-l.Z., T.-y.L., F.-j.L., Q.L.).
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing 400016, China (P.-l.Z., T.-y.L., F.-j.L., Q.L.).
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Singh A, Roshkovan L, Horng H, Chen A, Katz SI, Thompson JC, Kontos D. Radiomics Analysis for the Identification of Invasive Pulmonary Subsolid Nodules From Longitudinal Presurgical CT Scans. J Thorac Imaging 2025; 40:00005382-990000000-00146. [PMID: 39172061 PMCID: PMC11654445 DOI: 10.1097/rti.0000000000000800] [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] [Indexed: 08/23/2024]
Abstract
PURPOSE Effective identification of malignant part-solid lung nodules is crucial to eliminate risks due to therapeutic intervention or lack thereof. We aimed to develop delta radiomics and volumetric signatures, characterize changes in nodule properties over three presurgical time points, and assess the accuracy of nodule invasiveness identification when combined with immediate presurgical time point radiomics signature and clinical biomarkers. MATERIALS AND METHODS Cohort included 156 part-solid lung nodules with immediate presurgical CT scans and a subset of 122 nodules with scans at 3 presurgical time points. Region of interest segmentation was performed using ITK-SNAP, and feature extraction using CaPTk. Image parameter heterogeneity was mitigated at each time point using nested ComBat harmonization. For 122 nodules, delta radiomics features (ΔR AB = (R B -R A )/R A ) and delta volumes (ΔV AB = (V B -V A )/V A ) were computed between the time points. Principal Component Analysis was performed to construct immediate presurgical radiomics (Rs 1 ) and delta radiomics signatures (ΔRs 31 + ΔRs 21 + ΔRs 32 ). Identification of nodule pathology was performed using logistic regression on delta radiomics and immediate presurgical time point signatures, delta volumes (ΔV 31 + ΔV 21 + ΔV 32 ), and clinical variable (smoking status, BMI) models (train test split (2:1)). RESULTS In delta radiomics analysis (n= 122 nodules), the best-performing model combined immediate pre-surgical time point and delta radiomics signatures, delta volumes, and clinical factors (classification accuracy [AUC]): (77.5% [0.73]) (train); (71.6% [0.69]) (test). CONCLUSIONS Delta radiomics and volumes can detect changes in nodule properties over time, which are predictive of nodule invasiveness. These tools could improve conventional radiologic assessment, allow for earlier intervention for aggressive nodules, and decrease unnecessary intervention-related morbidity.
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Affiliation(s)
| | | | | | - Andrew Chen
- Departments of Radiology
- Department of Radiology, Columbia University, New York, NY
| | | | - Jeffrey C. Thompson
- Department of Medicine, Pulmonary, Allergy and Critical Care Medicine, Thoracic Oncology Group, University of Pennsylvania, Philadelphia, PA
| | - Despina Kontos
- Departments of Radiology
- Department of Radiology, Columbia University, New York, NY
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Faber DL, Agbarya A, Lee A, Tsenter Y, Schneer S, Robitsky Gelis Y, Galili R. Clinical Versus Pathological Staging in Patients with Resected Ground Glass Pulmonary Lesions. Diagnostics (Basel) 2024; 14:2874. [PMID: 39767235 PMCID: PMC11675473 DOI: 10.3390/diagnostics14242874] [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: 11/07/2024] [Revised: 12/16/2024] [Accepted: 12/19/2024] [Indexed: 01/11/2025] Open
Abstract
BACKGROUND A ground glass nodule (GGN) is a radiologically descriptive term for a lung parenchymal area with increased attenuation and preserved bronchial and vascular structures. GGNs are further divided into pure versus subsolid lesions. The differential diagnosis for GGNs is wide and contains a malignant possibility for a lung adenocarcinoma precursor or tumor. Clinical and pathological staging of GGNs is based on the lesions' solid component and falls into a specific classification including T0 for TIS, T1mi for minimally invasive adenocarcinoma (MIA) and T1abc for lepidic predominant adenocarcinoma (LPA) according to the eighth edition of the TNM classification of lung cancer. Correlation between solid parts seen on a CT scan and the tumor pathological invasive component is not absolute. METHODS This retrospective study collected the data of 68 GGNs that were operated upon in Carmel Medical Center. A comparison between preoperative clinical staging and post-surgery pathological staging was conducted. RESULTS Over a third of the lesions, twenty-four (35.3%), were upstaged while only four (5.9%) lesions were downstaged. Another third of the lesions, twenty-three (33.8%), kept their stage. In three (4.4%) cases, premalignant lesion atypical adenomatous hyperplasia (AAH) was diagnosed. Ten (14.7%) cases were diagnosed as non-malignant on final pathology. These findings show an overall low agreement between the clinical and pathological stages of GGNs. CONCLUSIONS The relatively high percentage of upstaging tumors detected in this study and the overall safe and short surgical procedure advocate for surgical resection even in the presence of a significant number of non-malignant lesions that retrospectively do not mandate intervention at all.
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Affiliation(s)
- Dan Levy Faber
- Department of Cardiothoracic Surgery, Lady Davis Carmel Medical Center, 7 Michal St., Haifa 3436212, Israel; (S.S.); (R.G.)
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3109601, Israel
| | - Abed Agbarya
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3109601, Israel
- Oncology Institute, Bnai-Zion Medical Center, Haifa 3339419, Israel
| | - Andrew Lee
- Department of Anesthesia, Lady Davis Carmel Medical Center, 7 Michal St., Haifa 3436212, Israel;
| | - Yael Tsenter
- Pathology Institute, Lady Davis Carmel Medical Center, Haifa 3436212, Israel;
| | - Sonia Schneer
- Department of Cardiothoracic Surgery, Lady Davis Carmel Medical Center, 7 Michal St., Haifa 3436212, Israel; (S.S.); (R.G.)
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa 3109601, Israel
- Pulmonary Division, Lady Davis Carmel Medical Center, Haifa 3436212, Israel
| | - Yulia Robitsky Gelis
- Oncology Institute, Lin Medical Center and Carmel Medical Center, Haifa 3515210, Israel;
| | - Ronen Galili
- Department of Cardiothoracic Surgery, Lady Davis Carmel Medical Center, 7 Michal St., Haifa 3436212, Israel; (S.S.); (R.G.)
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Chen J, Zeng X, Li F, Peng J. The value of non-enhanced CT 3D visualization in differentiating stage Ⅰ invasive lung adenocarcinoma between LPA and non-LPA. Eur J Radiol Open 2024; 13:100600. [PMID: 39351522 PMCID: PMC11440297 DOI: 10.1016/j.ejro.2024.100600] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Revised: 09/04/2024] [Accepted: 09/10/2024] [Indexed: 10/04/2024] Open
Abstract
Objective This study aims to analyze the quantitative parameters and morphological indices of three-dimensional (3D) visualization to differentiate lepidic predominant adenocarcinoma (LPA) from non-LPA subtypes, which include acinar predominant adenocarcinoma (APA), papillary predominant adenocarcinoma (PPA), micropapillary predominant adenocarcinoma (MPA), and solid predominant adenocarcinoma (SPA). Methods A group of 178 individuals diagnosed with lung adenocarcinoma were chosen and categorized into two groups: the LPA group and the non-LPA group, according to their pathological results. Quantitative parameters and morphological indexes such as 3D volume, solid proportion, and vascular cluster sign were obtained using 3D visualization and reconstruction techniques. Results Significant differences were observed in the vascular cluster sign, spiculation, shape, air bronchogram, bubble-like lucency, margin, pleural indentation, lobulation, maximum tumor diameter, 3D mean CT value, 3D volume, 3D mass, 3D density, and solid proportion between two groups (P<0.05). The optimal cut-off values for diagnosing non-LPA were a 3D mean CT value of -445.45 HU, a 3D density of 0.56 mg·mm-3, and a solid proportion reaching 27.95 %. Multivariate logistic regression analysis revealed that 3D mean CT value, lobulation, and margin characteristics independently predicted stageⅠinvasive lung adenocarcinoma. The combination of three indicators significantly improved prediction accuracy (AUC=0.881). Conclusion The utilization of 3D visualization technology in a systematic approach enables the acquisition of 3D quantitative parameters and morphological indicators of thin-slice CT lesions. These efforts significantly contribute to the identification of histopathological subtypes for stageⅠinvasive lung adenocarcinoma. When integrated with pertinent clinical data, this offers essential guidance for developing various surgical techniques and treatment plans.
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Affiliation(s)
- Jinxin Chen
- Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China
| | - Xinyi Zeng
- Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China
| | - Feng Li
- Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China
| | - Jidong Peng
- Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, 16th Meiguan Avenue, Ganzhou 341000, PR China
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Woo JH, Kim JH, Jeong DY, Park SG, Jung M, Kim CH, Lee J, Kim HK, Han J, Kim TJ, Chung MJ, Cha YK. Differentiation Between Invasive Adenocarcinoma and Focal Interstitial Fibrosis among Persistent Pulmonary Part-solid Nodules: With Emphasis on the CT Morphologic Analysis. J Thorac Imaging 2024; 39:335-341. [PMID: 38665005 DOI: 10.1097/rti.0000000000000786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2024]
Abstract
PURPOSE Focal interstitial fibrosis (FIF) manifesting as a persistent part-solid nodule (PSN) has been mistakenly treated surgically due to similar imaging features to invasive adenocarcinoma (ADC). The purpose of this study was to observe predictive imaging features correlated with FIF through CT morphologic analysis. MATERIALS AND METHODS From January 2009 to December 2020, 44 patients with surgically proven FIF in a single institution were enrolled and compared with 88 ADC patients through propensity score matching. Patient characteristics and CT morphologic analysis of persistent PSNs were used to identify predictive imaging features of FIF. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of imaging features. RESULTS A total of 132 patients with 132 PSNs (44 FIF, 88 ADC; mean age, 67.7±7.58; 75 females) were involved in our analysis. Multivariable analysis demonstrated that preserved peritumoral vascular margin (preserved vascular margin), preserved secondary pulmonary lobule margin (preserved lobular margin), and lower coronal to axial ratio (C/A ratio; cutoff: 1.005) were significant independent predictors of FIF ( P< 0.05). ROC curve analysis to evaluate the predictive value of the logistic model based on the imaging features of FIF, and the AUC value was 0.881. CONCLUSION CT imaging features of preserved vascular margin, preserved lobular margin, and lower C/A ratio (cutoff, <1.005) might be helpful imaging features in discriminating FIF over ADC among persistent PSN in clinical practice.
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Affiliation(s)
- Jung Han Woo
- Departments of Radiology and Center for Imaging Science
| | - Jong Hee Kim
- Departments of Radiology and Center for Imaging Science
| | | | - Sung Goo Park
- Departments of Radiology and Center for Imaging Science
| | | | - Chu Hyun Kim
- Departments of Radiology and Center for Imaging Science
| | - Junghee Lee
- Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine
| | - Hong Kwan Kim
- Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine
| | - Joungho Han
- Department of Internal Medicine, Division of Cardiology, Severance Cardiovascular Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Tae Jung Kim
- Departments of Radiology and Center for Imaging Science
| | | | - Yoon Ki Cha
- Departments of Radiology and Center for Imaging Science
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10
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Robles Gómez A, Oliva Lozano J, Rodríguez Fernández P, Ruiz González E, Tilve Gómez A, Arenas-Jiménez J. Lung adenocarcinoma: characteristic radiological presentations. RADIOLOGIA 2024; 66:542-554. [PMID: 39674619 DOI: 10.1016/j.rxeng.2024.11.003] [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: 06/06/2023] [Accepted: 07/10/2023] [Indexed: 12/16/2024]
Abstract
Radiology, mainly computed tomography, has a fundamental role in diagnosis, staging and follow-up of lung adenocarcinoma, the most common type of pulmonary cancer. Within its broad spectrum of presentation, the pathological, clinical and morphological characteristics of this neoplasm allow, in an appropriate clinical context, to suggest certain histological subtypes among which are mucinous adenocarcinoma, lepidic growth adenocarcinoma or associated with cystic lung lesions. The objective of this review is to describe the pathologic, clinical and radiological features of those characteristic forms of lung carcinoma that can be diagnosed radiologically with fair accuracy.
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Affiliation(s)
- A Robles Gómez
- Servicio de Radiodiagnóstico, Hospital Álvaro Cunqueiro, Vigo, Spain; Instituto de Investigación Sanitaria Galicia Sur (IISGS), Vigo, Spain
| | - J Oliva Lozano
- Servicio de Radiodiagnóstico, Hospital General Universitario Dr. Balmis, Alicante, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - P Rodríguez Fernández
- Servicio de Radiodiagnóstico, Hospital Álvaro Cunqueiro, Vigo, Spain; Instituto de Investigación Sanitaria Galicia Sur (IISGS), Vigo, Spain.
| | - E Ruiz González
- Servicio de Radiodiagnóstico, Hospital General Universitario Dr. Balmis, Alicante, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain
| | - A Tilve Gómez
- Servicio de Radiodiagnóstico, Hospital Álvaro Cunqueiro, Vigo, Spain; Instituto de Investigación Sanitaria Galicia Sur (IISGS), Vigo, Spain
| | - J Arenas-Jiménez
- Servicio de Radiodiagnóstico, Hospital General Universitario Dr. Balmis, Alicante, Spain; Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, Spain; Departamento de Patología y Cirugía, Universidad Miguel Hernández, Alicante, Spain
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11
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Daher A. [The pulmonary nodule: from incidental finding to pathological confirmation]. Dtsch Med Wochenschr 2024; 149:1238-1248. [PMID: 39312965 DOI: 10.1055/a-2188-8913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
As the number of CT examinations of the lungs increases, so does the prevalence of incidentally discovered pulmonary nodules. While most lung nodules are benign, the risk of malignancy significantly rises with the presence of risk factors and specific imaging features. Upon encountering an incidental nodule, efforts should focus on achieving an accurate pathological diagnosis, particularly to ascertain malignancy while minimizing the risks associated with unnecessary diagnostic procedures. A comprehensive understanding of the typical characteristics and behavior of malignant lung nodules, along with a detailed patient history and standardized clinical and imaging risk assessment, is crucial for determining the optimal diagnostic approach. Additionally, the decision regarding histologic confirmation should consider the patient's comorbidities, preferences, and the examiner's expertise. Emerging sampling technologies provide methods for addressing peripheral lung nodules with minimal risk of complications.
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12
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Wang L, Maolan A, Luo Y, Li Y, Liu R. Knowledge mapping analysis of ground glass nodules: a bibliometric analysis from 2013 to 2023. Front Oncol 2024; 14:1469354. [PMID: 39381043 PMCID: PMC11458373 DOI: 10.3389/fonc.2024.1469354] [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: 07/23/2024] [Accepted: 09/03/2024] [Indexed: 10/10/2024] Open
Abstract
Background In recent years, the widespread use of computed tomography (CT) in early lung cancer screening has led to an increase in the detection rate of lung ground glass nodules (GGNs). The persistence of GGNs, which may indicate early lung adenocarcinoma, has been a focus of attention for scholars in the field of lung cancer prevention and treatment in recent years. Despite the rapid development of research into GGNs, there is a lack of intuitive content and trend analyses in this field, as well as a lack of detailed elaboration on possible research hotspots. The objective of this study was to conduct a comprehensive analysis of the knowledge structure and research hotspots of lung ground glass nodules over the past decade, employing bibliometric methods. Method The Web of Science Core Collection (WoSCC) database was searched for relevant ground-glass lung nodule literature published from 2013-2023. Bibliometric analyses were performed using VOSviewer, CiteSpace, and the R package "bibliometrix". Results A total of 2,218 articles from 75 countries and 2,274 institutions were included in this study. The number of publications related to GGNs has been high in recent years. The United States has led in GGNs-related research. Radiology has one of the highest visibilities as a selected journal and co-cited journal. Jin Mo Goo has published the most articles. Travis WD has been cited the most frequently. The main topics of research in this field are Lung Cancer, CT, and Deep Learning, which have been identified as long-term research hotspots. The GGNs-related marker is a major research trend in this field. Conclusion This study represents the inaugural bibliometric analysis of applied research on ground-glass lung nodules utilizing three established bibliometric software. The bibliometric analysis of this study elucidates the prevailing research themes and trends in the field of GGNs over the past decade. It also furnishes pertinent recommendations for researchers to provide objective descriptions and comprehensive guidance for future related research.
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Affiliation(s)
| | | | | | | | - Rui Liu
- Department of Oncology, Guang’anmen Hospital, China Academy of Chinese Medical
Sciences, Beijing, China
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13
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Cheng M, Ding R, Wang S. Diagnosis and treatment of high-risk bilateral lung ground-glass opacity nodules. Asian J Surg 2024; 47:2969-2974. [PMID: 38246790 DOI: 10.1016/j.asjsur.2024.01.072] [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: 11/01/2023] [Revised: 12/30/2023] [Accepted: 01/11/2024] [Indexed: 01/23/2024] Open
Abstract
In recent years, there has been a significant increase in the detection rate of Ground Glass Opacity (GGO) nodules through high-resolution computed tomography (HRCT). GGO is an imaging finding that encompasses various pathological types, some of which exhibit indolent growth, while others may represent early lung cancer or remain relatively stable, not significantly impacting the surgical treatment outcome. In clinical practice, patients often experience psychological anxiety when multiple pulmonary GGO nodules are present, and they may request simultaneous resection. However, there is currently no standardized criterion for determining when multiple GGO nodules should be resected. As personalized medicine continues to advance, the treatment approach for multiple pulmonary GGO nodules needs to prioritize accuracy. High-risk factors associated with multiple pulmonary GGO nodules may necessitate surgical intervention along with mediastinal lymph node dissection or sampling. This article provides a review of the characteristics, treatment methods, and clinical experiences related to multiple pulmonary GGO nodules, offering practical insights and guidance for healthcare professionals.
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Affiliation(s)
- Ming Cheng
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110016, China
| | - Renquan Ding
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110016, China
| | - Shumin Wang
- Department of Thoracic Surgery, General Hospital of Northern Theater Command, Shenyang, 110016, China.
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14
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Yang Y, Gao Y, Lu F, Wang E, Liu H. Correlation of CT features of lung adenocarcinoma with sex and age. Sci Rep 2024; 14:13414. [PMID: 38862598 PMCID: PMC11167049 DOI: 10.1038/s41598-024-64335-7] [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: 03/18/2024] [Accepted: 06/07/2024] [Indexed: 06/13/2024] Open
Abstract
This study aimed to retrospectively examine the computed tomography (CT) features of lung adenocarcinoma across different demographic groups. Preoperative chest CT findings from 1266 surgically resected lung adenocarcinoma cases were retrospectively analyzed. Lung adenocarcinomas were categorized based on CT characteristics into pure ground glass (pGGO), nodule-containing ground glass opacity (mGGO), and pure solid without containing ground glass opacity (pSD). These categories were correlated with sex, age, EGFR status, and five histopathological subtypes. The diameters of pGGO, mGGO, and pSD significantly increased across all patient groups (P < 0.05). Males exhibited a significantly higher proportion of pSD than females (P = 0.002). The mean diameters of pGGO and pSD were significantly larger in males than in females (P = 0.0017 and P = 0.043, respectively). The frequency of pGGO was higher in the younger age group (≤ 60 years) compared to the older group (> 60 years) for both males (P = 0.002) and females (P = 0.027). The frequency of pSD was higher in the older age group for both sexes. A linear correlation between age and diameter was observed in the entire cohort as well as in the male and female groups (P < 0.0001 for all groups). EGFR mutations were less frequent in pSD compared to pGGO (P = 0.0002) and mGGO (P < 0.0001). The frequency of lesions containing micropapillary components increased from pGGO to mGGO and pSD (P < 0.0001 for all). The frequency of lesions containing solid components also increased from pGGO to mGGO and pSD (P = 0.045, P < 0.0001, and P < 0.0001, respectively). The CT features of lung adenocarcinoma exhibit differences across genders and age groups. Male gender and older age are risk factors for lung adenocarcinoma growth.
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Affiliation(s)
- Yanli Yang
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Yiyi Gao
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Fang Lu
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ernuo Wang
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Haiquan Liu
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
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15
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Lu F, Wang E, Liu H. Factors correlating the expression of PD-L1. BMC Cancer 2024; 24:642. [PMID: 38796458 PMCID: PMC11127358 DOI: 10.1186/s12885-024-12400-9] [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: 03/03/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
OBJECTIVE PD-L1 was an important biomarker in lung adenocarcinoma. The study was to confirm the most important factor affecting the expression of PD-L1 remains undetermined. METHODS The clinical records of 1045 lung adenocarcinoma patients were retrospectively reviewed. The High-Resolution Computed Tomography (HRCT) scanning images of all the participants were analyzed, and based on the CT characteristics, the adenocarcinomas were categorized according to CT textures. Furthermore, PD-L1 expression and Ki67 index were detected by immunohistochemistry. All patients underwent EGFR mutation detection. RESULTS Multivariate logistic regression analysis revealed that smoking (OR: 1.73, 95% CI: 1.04-2.89, p = 0.004), EGFR wild (OR: 1.52, 95% CI: 1.11-2.07, p = 0.009), micropapillary subtypes (OR: 2.05, 95% CI: 1.46-2.89, p < 0.0001), and high expression of Ki67 (OR: 2.02, 95% CI: 1.44-2.82, p < 0.0001) were independent factors which influence PD-L1 expression. In univariate analysis, tumor size > 3 cm and CT textures of pSD showed a correlation with high expression of PD-L1. Further analysis revealed that smoking, micropapillary subtype, and EGFR wild type were also associated with high Ki67 expression. Moreover, high Ki67 expression was observed more frequently in tumors of size > 3 cm than in tumors with ≤ 3 cm size as well as in CT texture of pSD than lesions with GGO components. In addition, multivariate logistic regression analysis revealed that only lesions with micropapillary components correlated with pSD (OR: 3.96, 95% CI: 2.52-5.37, p < 0.0001). CONCLUSION This study revealed that in lung adenocarcinoma high Ki67 expression significantly influenced PD-L1 expression, an important biomarker for immune checkpoint treatment.
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Affiliation(s)
- Fang Lu
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Ernuo Wang
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China
| | - Haiquan Liu
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai, 200040, China.
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Liu SZ, Yang SH, Ye M, Fu BJ, Lv FJ, Chu ZG. Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions. Cancer Imaging 2024; 24:47. [PMID: 38566150 PMCID: PMC10985942 DOI: 10.1186/s40644-024-00694-8] [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: 11/29/2023] [Accepted: 03/29/2024] [Indexed: 04/04/2024] Open
Abstract
PURPOSE To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis. MATERIALS AND METHODS From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them. RESULTS Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness. CONCLUSION The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.
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Affiliation(s)
- Si-Zhu Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Shi-Hai Yang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
- Department of Radiology, People's Hospital of Nanchuan district, 16# South street, Nanchuan district, 408400, Chongqing, China
| | - Min Ye
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
- Department of Radiology, The First People's Hospital of Neijiang, No.31 Tuozhong Road, Shizhong District, 641099, Neijiang, Sichuang Province, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong district, 400016, Chongqing, China.
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Ye Y, Sun Y, Hu J, Ren Z, Chen X, Chen C. A clinical-radiological predictive model for solitary pulmonary nodules and the relationship between radiological features and pathological subtype. Clin Radiol 2024; 79:e432-e439. [PMID: 38097460 DOI: 10.1016/j.crad.2023.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 02/15/2024]
Abstract
AIM To develop a clinical-radiological model to predict the malignancy of solitary pulmonary nodules (SPNs) and to evaluate the accuracy of chest computed tomography imaging characteristics of SPN in diagnosing pathological type. MATERIALS AND METHODS The predictive model was developed using a retrospective cohort of 601 SPN patients (Group A) between July 2015 and July 2020. The established model was tested using a second retrospective cohort of 124 patients between August 2020 and August 2021 (Group B). The radiological characteristics of all adenocarcinomas in two groups were analysed to determine the correlation between radiological and pathological characteristics. RESULTS Malignant nodules were found in 78.87% of cases and benign in 21.13%. Two clinical characteristics (age and gender) and four radiological characteristics (calcification, vascular convergence, pleural retraction sign, and density) were identified as independent predictors of malignancy in patients with SPN using logistic regression analysis. The area under the receiver operating characteristic curve (0.748) of the present model was greater than the other two reported models. Diameter, spiculation, lobulation, vascular convergence, and pleural retraction signs differed significantly among pre-invasive lesions, minimally invasive adenocarcinoma, and invasive adenocarcinoma. Only diameter and density were significantly different among invasive adenocarcinoma subtypes. CONCLUSIONS Older age, male gender, no calcification, vascular convergence, pleural contraction sign, and lower density were independent malignancy predictors of SPNs. Furthermore, the pathological classification can be clarified based on the radiological characteristics of SPN, providing a new option for the prevention and treatment of early lung cancer.
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Affiliation(s)
- Y Ye
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Y Sun
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - J Hu
- General Surgery, Cancer Center, Department of Gastrointestinal and Pancreatic Surgery, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - Z Ren
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - X Chen
- Cancer Center, Department of Medical Oncology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China
| | - C Chen
- Cancer Center, Department of Pulmonary and Critical Care Medicine, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, 310014, China.
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Xue M, Li R, Wang K, Liu W, Liu J, Li Z, Chen G, Zhang H, Tian H. Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter. BMC Surg 2024; 24:56. [PMID: 38355554 PMCID: PMC10868041 DOI: 10.1186/s12893-024-02341-2] [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: 11/07/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024] Open
Abstract
OBJECTIVES In this study, we aimed to develop a multiparameter prediction model to improve the diagnostic accuracy of invasive adenocarcinoma in pulmonary pure glass nodules. METHOD We included patients with pulmonary pure glass nodules who underwent lung resection and had a clear pathology between January 2020 and January 2022 at the Qilu Hospital of Shandong University. We collected data on the clinical characteristics of the patients as well as their preoperative biomarker results and computed tomography features. Thereafter, we performed univariate and multivariate logistic regression analyses to identify independent risk factors, which were then used to develop a prediction model and nomogram. We then evaluated the recognition ability of the model via receiver operating characteristic (ROC) curve analysis and assessed its calibration ability using the Hosmer-Lemeshow test and calibration curves. Further, to assess the clinical utility of the nomogram, we performed decision curve analysis. RESULT We included 563 patients, comprising 174 and 389 cases of invasive and non-invasive adenocarcinoma, respectively, and identified seven independent risk factors, namely, maximum tumor diameter, age, serum amyloid level, pleural effusion sign, bronchial sign, tumor location, and lobulation. The area under the ROC curve was 0.839 (95% CI: 0.798-0.879) for the training cohort and 0.782 (95% CI: 0.706-0.858) for the validation cohort, indicating a relatively high predictive accuracy for the nomogram. Calibration curves for the prediction model also showed good calibration for both cohorts, and decision curve analysis showed that the clinical prediction model has clinical utility. CONCLUSION The novel nomogram thus constructed for identifying invasive adenocarcinoma in patients with isolated pulmonary pure glass nodules exhibited excellent discriminatory power, calibration capacity, and clinical utility.
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Affiliation(s)
- Mengchao Xue
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Rongyang Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Kun Wang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Wen Liu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Junjie Liu
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Zhenyi Li
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Guanqing Chen
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Huiying Zhang
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China
| | - Hui Tian
- Department of Thoracic Surgery, Qilu Hospital of Shandong University, Lixia District, Jinan, Shandong Province, China.
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Fu BJ, Zhang XC, Lv FJ, Chu ZG. Potential Role of Intrapulmonary Concomitant Lesions in Differentiating Non-Neoplastic and Neoplastic Ground Glass Nodules. J Inflamm Res 2023; 16:6155-6166. [PMID: 38107382 PMCID: PMC10725751 DOI: 10.2147/jir.s437419] [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: 08/27/2023] [Accepted: 12/06/2023] [Indexed: 12/19/2023] Open
Abstract
Purpose To determine the value of intrapulmonary concomitant lesions in differentiating non-neoplastic and neoplastic ground-glass nodules (GGNs). Patients and Methods From January 2014 to March 2022, 395 and 583 patients with confirmed non-neoplastic and neoplastic GGNs were retrospectively enrolled. Their clinical and chest CT data were evaluated. The CT features of target GGNs and intrapulmonary concomitant lesions in these two groups were analyzed and compared, and the role of intrapulmonary concomitant lesions in improving differentiation was evaluated. Results The intrapulmonary concomitant lesions were more common in patients with non-neoplastic GGNs than in those with neoplastic ones (87.88% vs 82.18%, P = 0.015). Specifically, patients with non-neoplastic GGNs had a higher incidence of multiple solid nodules (SNs), patchy ground-glass opacity/consolidation, and fibrosis/calcification in any lung fields (each P < 0.05). Logistic regression analysis indicated that patients < 44 years old, diameter < 7.35 mm, irregular shape, and coarse margin or ill-defined boundary for target GGN, pleural thickening, and concomitant SNs in the same lobe and fibrosis or calcification in any lung field were independent indicators for predicting non-neoplastic GGNs. The AUC of the model for predicting non-neoplastic GGNs increased from 0.894 to 0.926 (sensitivity, 83.10%; specificity, 87.10%) after including the concomitant lesions in the patients' clinical characteristics and CT features of target GGNs (P < 0.0001). Conclusion Besides the patients' clinical characteristics and CT features of target GGNs, the concomitant multiple SNs in the same lobe and fibrosis/calcification in any lung field should be considered in further differentiating non-neoplastic and neoplastic GGNs.
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Affiliation(s)
- Bin-Jie Fu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xiao-Chuan Zhang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Department of Radiology, Chonggang General Hospital, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
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20
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Huang W, Deng H, Li Z, Xiong Z, Zhou T, Ge Y, Zhang J, Jing W, Geng Y, Wang X, Tu W, Dong P, Liu S, Fan L. Baseline whole-lung CT features deriving from deep learning and radiomics: prediction of benign and malignant pulmonary ground-glass nodules. Front Oncol 2023; 13:1255007. [PMID: 37664069 PMCID: PMC10470826 DOI: 10.3389/fonc.2023.1255007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 07/28/2023] [Indexed: 09/05/2023] Open
Abstract
Objective To develop and validate the model for predicting benign and malignant ground-glass nodules (GGNs) based on the whole-lung baseline CT features deriving from deep learning and radiomics. Methods This retrospective study included 385 GGNs from 3 hospitals, confirmed by pathology. We used 239 GGNs from Hospital 1 as the training and internal validation set; 115 and 31 GGNs from Hospital 2 and Hospital 3 as the external test sets 1 and 2, respectively. An additional 32 stable GGNs from Hospital 3 with more than five years of follow-up were used as the external test set 3. We evaluated clinical and morphological features of GGNs at baseline chest CT and extracted the whole-lung radiomics features simultaneously. Besides, baseline whole-lung CT image features are further assisted and extracted using the convolutional neural network. We used the back-propagation neural network to construct five prediction models based on different collocations of the features used for training. The area under the receiver operator characteristic curve (AUC) was used to compare the prediction performance among the five models. The Delong test was used to compare the differences in AUC between models pairwise. Results The model integrated clinical-morphological features, whole-lung radiomic features, and whole-lung image features (CMRI) performed best among the five models, and achieved the highest AUC in the internal validation set, external test set 1, and external test set 2, which were 0.886 (95% CI: 0.841-0.921), 0.830 (95%CI: 0.749-0.893) and 0.879 (95%CI: 0.712-0.968), respectively. In the above three sets, the differences in AUC between the CMRI model and other models were significant (all P < 0.05). Moreover, the accuracy of the CMRI model in the external test set 3 was 96.88%. Conclusion The baseline whole-lung CT features were feasible to predict the benign and malignant of GGNs, which is helpful for more refined management of GGNs.
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Affiliation(s)
- Wenjun Huang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Heng Deng
- School of Medicine, Shanghai University, Shanghai, China
| | - Zhaobin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, Shanghai, China
| | - Zhanda Xiong
- Department of Artificial Intelligence Medical Imaging, Tron Technology, Shanghai, China
| | - Taohu Zhou
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Yanming Ge
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, Shandong, China
| | - Jing Zhang
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Wenbin Jing
- Department of Radiology, The Second People’s hospital of Deyang, Deyang, Sichuan, China
| | - Yayuan Geng
- Clinical Research Institute, Shukun (Beijing) Technology Co., Ltd., Beijing, China
| | - Xiang Wang
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, Shandong, China
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Naval Medical University, Shanghai, China
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21
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Zheng Y, Han X, Jia X, Ding C, Zhang K, Li H, Cao X, Zhang X, Zhang X, Shi H. Dual-energy CT-based radiomics for predicting invasiveness of lung adenocarcinoma appearing as ground-glass nodules. Front Oncol 2023; 13:1208758. [PMID: 37637058 PMCID: PMC10449576 DOI: 10.3389/fonc.2023.1208758] [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: 04/19/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Objectives To explore the value of radiomics based on Dual-energy CT (DECT) for discriminating preinvasive or MIA from IA appearing as GGNs before surgery. Methods The retrospective study included 92 patients with lung adenocarcinoma comprising 30 IA and 62 preinvasive-MIA, which were further divided into a training (n=64) and a test set (n=28). Clinical and radiographic features along with quantitative parameters were recorded. Radiomics features were derived from virtual monoenergetic images (VMI), including 50kev and 150kev images. Intraclass correlation coefficients (ICCs), Pearson's correlation analysis and least absolute shrinkage and selection operator (LASSO) penalized logistic regression were conducted to eliminate unstable and redundant features. The performance of the models was evaluated by area under the curve (AUC) and the clinical utility was assessed using decision curve analysis (DCA). Results The DECT-based radiomics model performed well with an AUC of 0.957 and 0.865 in the training and test set. The clinical-DECT model, comprising sex, age, tumor size, density, smoking, alcohol, effective atomic number, and normalized iodine concentration, had an AUC of 0.929 in the training and 0.719 in the test set. In addition, the radiomics model revealed a higher AUC value and a greater net benefit to patients than the clinical-DECT model. Conclusion DECT-based radiomics features were valuable in predicting the invasiveness of GGNs, yielding a better predictive performance than the clinical-DECT model.
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Affiliation(s)
- Yuting Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiaoyu Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chengyu Ding
- ShuKun (BeiJing) Technology Co., Ltd., Beijing, China
| | - Kailu Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Hanting Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xuexiang Cao
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Xiaohui Zhang
- Clinical Solution, Philips Healthcare, Shanghai, China
| | - Xin Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
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22
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Powell CL, Saddoughi SA, Wigle DA. Progress in genome-inspired treatment decisions for multifocal lung adenocarcinoma. Expert Rev Respir Med 2023; 17:1009-1021. [PMID: 37982734 DOI: 10.1080/17476348.2023.2286277] [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: 07/05/2023] [Accepted: 11/17/2023] [Indexed: 11/21/2023]
Abstract
INTRODUCTION Multifocal lung adenocarcinoma (MFLA) is becoming increasingly recognized as a distinct subset of lung cancer, with unique biology, disease course, and treatment outcomes. While definitions remain controversial, MFLA is characterized by the development and concurrent presence of multiple independent (non-metastatic) lesions on the lung adenocarcinoma spectrum. Disease progression typically follows an indolent course measured in years, with a lower propensity for nodal and distant metastases than other more common forms of non-small cell lung cancer. AREAS COVERED Traditional imaging and histopathological analyses of tumor biopsies are frequently unable to fully characterize the disease, prompting interest in molecular diagnosis. We highlight some of the key questions in the field, including accurate definitions to identify and stage MLFA, molecular tests to stratify patients and treatment decisions, and the lack of clinical trial data to delineate best management for this poorly understood subset of lung cancer patients. We review the existing literature and progress toward a genomic diagnosis for this unique disease entity. EXPERT OPINION Multifocal lung adenocarcinoma behaves differently than other forms of non-small cell lung cancer. Progress in molecular diagnosis may enhance potential for accurate definition, diagnosis, and optimizing treatment approach.
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Affiliation(s)
- Chelsea L Powell
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Sahar A Saddoughi
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
| | - Dennis A Wigle
- Division of Thoracic Surgery, Department of Surgery, Mayo Clinic, Rochester, MN, USA
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23
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Lee K, Liu Z, Chandran U, Kalsekar I, Laxmanan B, Higashi MK, Jun T, Ma M, Li M, Mai Y, Gilman C, Wang T, Ai L, Aggarwal P, Pan Q, Oh W, Stolovitzky G, Schadt E, Wang X. Detecting Ground Glass Opacity Features in Patients With Lung Cancer: Automated Extraction and Longitudinal Analysis via Deep Learning-Based Natural Language Processing. JMIR AI 2023; 2:e44537. [PMID: 38875565 PMCID: PMC11041451 DOI: 10.2196/44537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 01/30/2023] [Accepted: 03/31/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Ground-glass opacities (GGOs) appearing in computed tomography (CT) scans may indicate potential lung malignancy. Proper management of GGOs based on their features can prevent the development of lung cancer. Electronic health records are rich sources of information on GGO nodules and their granular features, but most of the valuable information is embedded in unstructured clinical notes. OBJECTIVE We aimed to develop, test, and validate a deep learning-based natural language processing (NLP) tool that automatically extracts GGO features to inform the longitudinal trajectory of GGO status from large-scale radiology notes. METHODS We developed a bidirectional long short-term memory with a conditional random field-based deep-learning NLP pipeline to extract GGO and granular features of GGO retrospectively from radiology notes of 13,216 lung cancer patients. We evaluated the pipeline with quality assessments and analyzed cohort characterization of the distribution of nodule features longitudinally to assess changes in size and solidity over time. RESULTS Our NLP pipeline built on the GGO ontology we developed achieved between 95% and 100% precision, 89% and 100% recall, and 92% and 100% F1-scores on different GGO features. We deployed this GGO NLP model to extract and structure comprehensive characteristics of GGOs from 29,496 radiology notes of 4521 lung cancer patients. Longitudinal analysis revealed that size increased in 16.8% (240/1424) of patients, decreased in 14.6% (208/1424), and remained unchanged in 68.5% (976/1424) in their last note compared to the first note. Among 1127 patients who had longitudinal radiology notes of GGO status, 815 (72.3%) were reported to have stable status, and 259 (23%) had increased/progressed status in the subsequent notes. CONCLUSIONS Our deep learning-based NLP pipeline can automatically extract granular GGO features at scale from electronic health records when this information is documented in radiology notes and help inform the natural history of GGO. This will open the way for a new paradigm in lung cancer prevention and early detection.
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Affiliation(s)
| | | | - Urmila Chandran
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Iftekhar Kalsekar
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | - Balaji Laxmanan
- Lung Cancer Initiative, Johnson & Johnson, New Brunswick, NJ, United States
| | | | - Tomi Jun
- Sema4, Stamford, CT, United States
| | - Meng Ma
- Sema4, Stamford, CT, United States
| | | | - Yun Mai
- Sema4, Stamford, CT, United States
| | | | | | - Lei Ai
- Sema4, Stamford, CT, United States
| | | | - Qi Pan
- Sema4, Stamford, CT, United States
| | - William Oh
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | | | - Eric Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, United States
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24
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Li M, Zhu L, Lv Y, Shen L, Han Y, Ye B. Thin-slice computed tomography enables to classify pulmonary subsolid nodules into pre-invasive lesion/minimally invasive adenocarcinoma and invasive adenocarcinoma: a retrospective study. Sci Rep 2023; 13:6999. [PMID: 37117233 PMCID: PMC10147622 DOI: 10.1038/s41598-023-33803-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 04/19/2023] [Indexed: 04/30/2023] Open
Abstract
The aim was to investigate the ability of thin-slice computed tomography (TSCT) to differentiate invasive pulmonary adenocarcinomas (IACs) from pre-invasive/minimally invasive adenocarcinoma (AAH-MIAs), manifesting as subsolid nodules (SSNs) of diameter less than 30 mm. The CT findings of 810 patients with single subsolid nodules diagnosed by pathology of resection specimens were analyzed (atypical adenomatous hyperplasia, n = 13; adenocarcinoma in situ, n = 175; minimally invasive adenocarcinoma, n = 285; and invasive adenocarcinoma, n = 337). According to the classification of lung adenocarcinoma published by WHO classification of thoracic tumors in 2015, TSCT features of 368 pure ground-glass nodules (pGGN) and 442 part-solid nodules (PSNs) were compared AAH-MIAs with IACs. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed. In pGGNs, multivariate analysis of factors found to be significant by univariate analysis revealed that higher mean-CT values (p = 0.006, OR 1.006, 95% CI 1.002-1.010), larger tumor size (p < 0.001, OR 1.483, 95% CI 1.304-1.688) with air bronchogram and non-smooth margins were significantly associated with IACs. The optimal cut-off tumor diameter for AAH-MIAs lesions was less than 10.75 mm (sensitivity, 82.8%; specificity, 80.6%) and optimal cut-off mean-CT value - 629HU (sensitivity, 78.1%; specificity, 50.7%). In PSNs, multivariate analysis of factors found to be significant by univariate analysis revealed that smaller tumor diameter (p < 0.001, OR 0.647, 95% CI 0.481-0.871), smaller size of solid component (p = 0.001, OR 83.175, 95% CI 16.748-413.079),and lower mean-CT value of solid component (p < 0.001, OR 1.009, 95% CI 1.004-1.014) were significantly associated with AAH-MIAs (p < 0.05). The optimal cut-off tumor diameter, size of solid component, and mean-CT value of solid component for AAH-MIAs lesions were less than 14.595 mm (sensitivity, 71.1%; specificity, 83.4%), 4.995 mm (sensitivity, 97.8%; specificity, 92.3%) and - 227HU (sensitivity, 65.6%; specificity, 76.3%), respectively. In subsolid nodules, whether pGGN or PSNs, the characteristics of TSCT can help in distinguishing IACs from AAH-MIAs.
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Affiliation(s)
- Min Li
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Lei Zhu
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Yilv Lv
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China
| | - Leilei Shen
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Yuchen Han
- Department of Pathology, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
| | - Bo Ye
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiaotong University, 241 Huaihai West Road, Xuhui District, Shanghai, 200030, China.
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25
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He W, Guo G, Du X, Guo S, Zhuang X. CT imaging indications correlate with the degree of lung adenocarcinoma infiltration. Front Oncol 2023; 13:1108758. [PMID: 36969028 PMCID: PMC10036829 DOI: 10.3389/fonc.2023.1108758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
BackgroundGround glass nodules (GGN) of the lung may be a precursor of lung cancer and have received increasing attention in recent years with the popularity of low-dose high-resolution computed tomography (CT). Many studies have discussed imaging features that suggest the benignity or malignancy of GGN, but the extent of its postoperative pathological infiltration is poorly understood. In this study, we identified CT imaging features that indicate the extent of GGN pathological infiltration.MethodsA retrospective analysis of 189 patients with pulmonary GGN from January 2020 to December 2021 at Shanxi Cancer Hospital was performed. Patients were classified according to their pathological type into non-invasive adenocarcinoma [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) in a total of 34 cases], micro-invasive adenocarcinoma (MIA) in 80 cases, and invasive adenocarcinoma (IAC) in a total of 75 cases. The general demographic data, nodule size, nodule area, solid component, CT indications and pathological findings of the three groups of patients were analyzed to predict the correlation between GGN and the degree of lung adenocarcinoma infiltration.ResultsNo statistically significant differences were found among the three groups in general information, vascular signs, and vacuolar signs (P > 0.05). Statistically significant differences among the three groups were found in nodule size, nodule area, lobar signs, pleural traction, burr signs, bronchial signs, and solid components (P < 0.05). Logistic regression equation tests based on the statistically significant indicators showed that nodal area, lobar sign, pleural pull, burr sign, bronchial sign, and solid component were independent predictors of lung adenocarcinoma infiltration. The subject operating characteristic (ROC) curve analysis showed that nodal area is valuable in predicting GGN infiltration.ConclusionCT-based imaging indications are useful predictors of infiltrative adenocarcinoma manifested as pulmonary ground glass nodules.
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Affiliation(s)
- Wenchen He
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Gang Guo
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Xiaoxiang Du
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
| | - Shiping Guo
- Cancer Hospital Affiliated to Shanxi Medical University/Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Taiyuan, Shanxi, China
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
- *Correspondence: Shiping Guo, ; Xiaofei Zhuang,
| | - Xiaofei Zhuang
- Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Cardiothoracic Surgery, Lvliang People's Hospital, Lvliang, Shanxi, China
- *Correspondence: Shiping Guo, ; Xiaofei Zhuang,
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Su Y, Zhou H, Huang W, Li L, Wang J. The value of preoperative positron emission tomography/computed tomography in differentiating the invasive degree of hypometabolic lung adenocarcinoma. BMC Med Imaging 2023; 23:31. [PMID: 36765284 PMCID: PMC9912592 DOI: 10.1186/s12880-023-00986-8] [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: 11/04/2022] [Accepted: 02/03/2023] [Indexed: 02/12/2023] Open
Abstract
OBJECTIVES To investigate the value of preoperative positron emission tomography/computed tomography (PET/CT) in differentiating the invasive degree of hypometabolic lung adenocarcinoma. METHODS We retrospectively analyzed the data of patients who underwent PET/CT examination, high-resolution computed tomography, and surgical resection for low-metabolism lung adenocarcinoma in our hospital between June 2016 and December 2021. We also investigated the relationship between the preoperative PET/CT findings and the pathological subtype of hypometabolic lung adenocarcinoma. RESULTS A total of 128 lesions were found in 113 patients who underwent resection for lung adenocarcinoma, including 20 minimally invasive adenocarcinomas (MIA) and 108 invasive adenocarcinomas (IAC), whose preoperative PET/CT showed low metabolism. There were significant differences in the largest diameter (Dmax), lesion type, maximum standard uptake value (SUVmax), SUVindex (the ratio of SUVmax of lesion to SUVmax of contralateral normal lung paranchyma), fasting blood glucose, lobulation, spiculation, and pleura indentation between the MIA and IAC groups (p < 0.05). Multivariate logistic regression analysis showed that the Dmax (odds ratio (OR) = 1.413, 95% confidence interval (CI: 1.155-1.729, p = 0.001)) and SUVmax (OR = 12.137, 95% CI: 1.068-137.900, p = 0.044) were independent risk factors for predicting the hypometabolic IAC (p < 0.05). Receiver operating characteristic (ROC) curve analysis showed that the Dmax ≥ 10.5 mm and SUVmax ≥ 0.85 were the cut-off values for differentiating MIA from IAC, with high sensitivity (84.3% and 75.9%, respectively) and specificity (84.5% and 85.0%, respectively), the Combined Diagnosis showed higher sensitivity (91.7%) and specificity (85.0%). CONCLUSIONS The PET/CT findings correlated with the subtype of hypometabolic lung adenocarcinoma. The parameters Dmax and SUVmax were independent risk factors for predicting IAC, and the sensitivity of Combined Diagnosis prediction is better.
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Affiliation(s)
- Yuling Su
- Department of Nuclear Medicine, Zhuhai People's Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China.
| | - Hui Zhou
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Wenshan Huang
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Lei Li
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
| | - Jinyu Wang
- grid.452930.90000 0004 1757 8087Department of Nuclear Medicine, Zhuhai People’s Hospital (Zhuhai Hospital Affiliated with Jinan University), Zhuhai, China
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27
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Analysis of the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types. TURK GOGUS KALP DAMAR CERRAHISI DERGISI 2023; 31:95-104. [PMID: 36926148 PMCID: PMC10012978 DOI: 10.5606/tgkdc.dergisi.2023.22239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 11/22/2021] [Indexed: 03/18/2023]
Abstract
Background In this study, we aimed to analyze the relevance between computed tomography characterization and pathology of pulmonary ground-glass nodules with different pathology types. Methods Between January 2017 and December 2018, a total of 657 patients (191 males, 466 females; mean age: 60.9±8.1 years; range, 34 to 80 years) with pathologically diagnosed ground-glass nodules were retrospectively analyzed. The clinicopathological characteristics and computed tomography characterizations of patients with ground-glass nodules who received surgical resection were analyzed. The clinical data including age, sex, smoking status and medical history were recorded. Computed tomography characterizations included the location and size of the tumor, the size of the consolidation components, density uniformity, shape, margin, tumor-lung interface, internal signs and surrounding signs. Results Based on the computed tomography imaging characteristics, a mean computed tomography value of ≥444.5 HU was more likely to indicate malignant lesions, while ≤444.5 HU indicated benign lesions. A malignant ground-glass nodules" maximum diameter of <6.78 mm, a diameter of the consolidation component of <3.88 mm, and a mean computed tomography value of <-536.5 HU were more likely to indicate atypical adenomatous hyperplasia and adenocarcinoma in situ. A maximum diameter of malignant ground-glass nodules of >11.52 mm, a diameter of the consolidation component of >6.20 mm, and a mean computed tomography value of ≥493.5 HU were more likely to indicate invasive adenocarcinomas. The focus between these parameters indicated minimally invasive adenocarcinomas. Conclusion Ill-defined tumor-lung interface, irregular in shape, and smooth nodule margins suggest benign lesions while round or oval, clear tumor-lung interface, spiculation signs, lobulation signs, bubble signs, air bronchograms, pleural indentations, and vessel convergences are helpful in the diagnosis of malignant lesions. A clear tumor-lung interface, the spiculation signs, lobulation signs, and bubble signs indicate the invasion of the lesions.
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Liu XL, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Correlations Between Inflammatory Cell Infiltration and Relative Density and the Boundary Manifestation of Pulmonary Non-Neoplastic Ground Glass Nodules. J Inflamm Res 2023; 16:1147-1155. [PMID: 36945317 PMCID: PMC10024903 DOI: 10.2147/jir.s399953] [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: 12/06/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Purpose To investigate the influence factors for the various boundary manifestations of pulmonary non-neoplastic ground glass nodules (GGNs) on computed tomography (CT). Materials and Methods From January 2015 to March 2022, a total of 280 patients with 318 non-neoplastic GGNs were enrolled. The correlations between degree of inflammatory cell infiltration and relative density (ΔCT) and the boundary manifestations of lesions were evaluated, respectively. Results Nongranulomatous nodules (283, 89.0%) with fibrous tissue proliferation and/or inflammatory cells as the predominant pathological findings were the most common non-neoplastic GGNs, followed by granulomatous nodules (28, 8.8%). Among nongranulomatous GGNs, cases with more and less/no inflammatory cells were 15 (10.9%) and 122 (89.1%) in 137 well-defined ones with smooth margin, 16 (24.6%) and 49 (75.4%) in 65 well-defined ones with coarse margin, 43 (91.5%) and 4 (8.5%) in 47 ill-defined ones with higher ΔCT (>151HU), and 4 (11.8%) and 30 (88.2%) in 34 ill-defined ones with lower ΔCT (< 151HU). The proportion of cases with more inflammatory cells in well-defined nodules was similar to that in ill-defined ones with lower ΔCT (P = 0.587) but significantly lower than that in ill-defined ones with higher ΔCT (P < 0.001). Among the granulomatous nodules, ill-defined cases with higher ΔCT (16, 57.1%) were the most common, and they (7/8, 87.5%) frequently had changes during short-term follow-up. Conclusion Nongranulomatous nodules are the most common non-neoplastic GGNs, their diverse boundary manifestations closely correlate with degree of inflammatory cell infiltration and density difference.
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Affiliation(s)
- Xiang-Ling Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Rui-Yu Lin
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Correspondence: Zhi-Gang Chu, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, 1# Youyi Road, Yuanjiagang, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86 18723032809, Fax +86 23 68811487, Email
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Cheng J, Xie Y, Zhou S, Lu A, Peng X, Liu W. Improved Weighted Non-Local Mean Filtering Algorithm for Laser Image Speckle Suppression. MICROMACHINES 2022; 14:98. [PMID: 36677160 PMCID: PMC9865138 DOI: 10.3390/mi14010098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 06/17/2023]
Abstract
Laser speckle noise caused by coherence between lasers greatly influences the produced image. In order to suppress the effect of laser speckles on images, in this paper we set up a combination of a laser-structured light module and an infrared camera to acquire laser images, and propose an improved weighted non-local mean (IW-NLM) filtering method that adopts an SSI-based adaptive h-solving method to select the optimal h in the weight function. The analysis shows that the algorithm not only denoises the laser image but also smooths pixel jumps in the image, while preserving the image details. The experimental results show that compared with the original laser image, the equivalent number of looks (ENL) index of the IW-NLM filtered image improved by 0.80%. The speckle suppression index (SSI) of local images dropped from 4.69 to 2.55%. Compared with non-local mean filtering algorithms, the algorithm proposed in this paper is an improvement and provides more accurate data support for subsequent image processing analysis.
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Affiliation(s)
- Jin Cheng
- School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710000, China
| | - Yibo Xie
- School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710000, China
| | - Shun Zhou
- School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710000, China
| | - Anjiang Lu
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550000, China
| | - Xishun Peng
- College of Big Data and Information Engineering, Guizhou University, Guiyang 550000, China
| | - Weiguo Liu
- School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710000, China
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Koo CW, Kline TL, Yoon JH, Vercnocke AJ, Johnson MP, Suman G, Lu A, Larson NB. Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics. Br J Radiol 2022; 95:20220230. [PMID: 36367095 PMCID: PMC9733623 DOI: 10.1259/bjr.20220230] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 10/05/2022] [Accepted: 10/13/2022] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. METHODS Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. RESULTS Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73-0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). CONCLUSION Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. ADVANCES IN KNOWLEDGE Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.
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Affiliation(s)
- Chi Wan Koo
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Joo Hee Yoon
- Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA
| | | | - Mathew P Johnson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Garima Suman
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Aiming Lu
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Nicholas B Larson
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
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Liang ZR, Ye M, Lv FJ, Fu BJ, Lin RY, Li WJ, Chu ZG. Differential diagnosis of benign and malignant patchy ground-glass opacity by thin-section computed tomography. BMC Cancer 2022; 22:1206. [DOI: 10.1186/s12885-022-10338-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 11/18/2022] [Indexed: 11/25/2022] Open
Abstract
Abstract
Background
Previous studies confirmed that ground-glass nodules (GGNs) with certain CT manifestations had a higher probability of malignancy. However, differentiating patchy ground-glass opacities (GGOs) and GGNs has not been discussed solely. This study aimed to investigate the differences between the CT features of benign and malignant patchy GGOs to improve the differential diagnosis.
Methods
From January 2016 to September 2021, 226 patients with 247 patchy GGOs (103 benign and 144 malignant) confirmed by postoperative pathological examination or follow-up were retrospectively enrolled. Their clinical and CT data were reviewed, and their CT features were compared. A binary logistic regression analysis was performed to reveal the predictors of malignancy.
Results
Compared to patients with benign patchy GGOs, malignant cases were older (P < 0.001), had a lower incidence of malignant tumor history (P = 0.003), and more commonly occurred in females (P = 0.012). Based on CT images, there were significant differences in the location, distribution, density pattern, internal bronchial changes, and boundary between malignant and benign GGOs (P < 0.05). The binary logistic regression analysis revealed that the independent predictors of malignant GGOs were the following: patient age ≥ 58 years [odds ratio (OR), 2.175; 95% confidence interval (CI), 1.135–6.496; P = 0.025], locating in the upper lobe (OR, 5.481; 95%CI, 2.027–14.818; P = 0.001), distributing along the bronchovascular bundles (OR, 12.770; 95%CI, 4.062–40.145; P < 0.001), centrally distributed solid component (OR, 3.024; 95%CI, 1.124–8.133; P = 0.028), and well-defined boundary (OR, 5.094; 95%CI, 2.079–12.482; P < 0.001).
Conclusions
In older patients (≥58 years), well-defined patchy GGOs with centric solid component, locating in the upper lobe, and distributing along the bronchovascular bundles should be highly suspected as malignancy.
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A comparative study for the evaluation of CT-based conventional, radiomic, combined conventional and radiomic, and delta-radiomic features, and the prediction of the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules. Clin Radiol 2022; 77:e741-e748. [PMID: 35840455 DOI: 10.1016/j.crad.2022.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 05/10/2022] [Accepted: 06/01/2022] [Indexed: 11/20/2022]
Abstract
AIM To investigate and compare the performance of conventional, radiomic, combined, and delta-radiomic features to predict the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGNs). MATERIALS AND METHODS The present retrospective study included 216 GGNs confirmed surgically as pulmonary adenocarcinomas. All the thin-section computed tomography (CT) images were imported into the software of the United Imaging Intelligence research portal, and radiomic features were extracted with three-dimensional (3D) regions of interest. Least Absolute Shrinkage and Selection Operator was used to select the optimal radiomic features. Four models were constructed, including conventional, radiomic, combined conventional and radiomic, and delta-radiomic models. The receiver operating characteristic curves were built to evaluate the validity of these. RESULTS The type, long diameter, shape, margin, vacuole, air bronchus, vascular convergence, and pleural traction exhibited significant differences between pre-invasive lesions (PILs)/minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) groups were selected for conventional model building. Nine radiomic features were selected to build the radiomic model. The four models indicated optimal performance (AUC > 0.7). The radiomic and combined models exhibited the highest diagnostic efficiency, and their AUC were 0.89 and 0.88 in the training set, and 0.87 and 0.88 in the validation set, respectively. The delta-radiomic model indicated that the AUC was 0.83 in the training set, and 0.76 in the validation set. Finally, the conventional model exhibited an AUC in the training and validation sets of 0.78 and 0.76. CONCLUSIONS The radiomic model and combined model, in particular, and the delta-radiomic model all demonstrated improved diagnostic efficiency in differentiating IA from PIL/MIA than that of the conventional model.
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Li Y, Yang CF, Peng J, Li B, Zhang C, Yu JH. Small (≤ 20 mm) ground-glass opacity pulmonary lesions: which factors influence the diagnostic accuracy of CT-guided percutaneous core needle biopsy? BMC Pulm Med 2022; 22:265. [PMID: 35799223 PMCID: PMC9264544 DOI: 10.1186/s12890-022-02058-z] [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: 03/31/2022] [Accepted: 06/30/2022] [Indexed: 11/17/2022] Open
Abstract
Background The diagnostic accuracy of computed tomography (CT)-guided percutaneous core needle biopsy (CNB) for small (≤ 20 mm) ground-glass opacity (GGO) lesions has not been reported in detail. Objectives To evaluate factors that affect the diagnostic accuracy of CT-guided percutaneous CNB for small (≤ 20 mm) GGO pulmonary lesions. Methods From January 2014 to February 2018, 156 patients with a small (≤ 20 mm) GGO pulmonary lesion who underwent CT-guided CNB were enrolled in this study. Factors affecting diagnostic accuracy were evaluated by analyzing patient and lesion characteristics and technical factors. Significant factors were identified by multivariate logistic regression. Results The diagnostic accuracy of CT-guided percutaneous CNB was 90.4% for small (≤ 20 mm) GGO pulmonary lesions. The diagnostic accuracy was higher for larger lesions (72.5% for lesions ≤ 10 mm, 96.6% for lesions between 11 and 20 mm [P < 0.001]). The diagnostic accuracy of CT-guided percutaneous CNB was 74.5% for lesions with > 90% GGO components and 97.2% for lesions with 50–90% GGO components (P < 0.001). In multivariate analysis, the significant factors influencing diagnostic accuracy were lesion size (P = 0.022; odds ratio [OR] for a lesion between 11 and 20 mm in size was approximately 5 times higher than that for a lesion ≤ 10 mm; 95% confidence interval [CI], 1.3 to 18.5), and GGO component (P = 0.015; OR for a lesion with 50–90% GGO components was approximately 6 times higher than that for a lesion with > 90% GGO components; 95% CI: 1.4 to 25.7). Conclusions Lesion size and GGO component are factors affecting diagnostic accuracy. The diagnostic accuracy was higher for larger lesions and lesions with 50–90% GGO components.
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Affiliation(s)
- Yang Li
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong City, 637000, Sichuan Province, China.,Department of Radiology, The People's Hospital of Yuechi County, 22 East Jianshe Road, Yuechi County, 638350, Sichuan Province, China
| | - Chao Feng Yang
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong City, 637000, Sichuan Province, China
| | - Jun Peng
- Department of Radiology, The People's Hospital of Yuechi County, 22 East Jianshe Road, Yuechi County, 638350, Sichuan Province, China
| | - Bing Li
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong City, 637000, Sichuan Province, China
| | - Chuan Zhang
- Sichuan Key Laboratory of Medical Imaging, Department of Radiology, The Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong City, 637000, Sichuan Province, China
| | - Jin Hong Yu
- Sichuan Key Laboratory of Medical Imaging, Department of Ultrasound, The Affiliated Hospital of North Sichuan Medical College, 63 Wenhua Road, Nanchong City, 637000, Sichuan Province, China. .,Department of Ultrasound, The People's Hospital of Yuechi County, 22 East Jianshe Road, Yuechi County, 638350, Sichuan Province, China.
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Wang C, Wu N, Zhang Z, Zhang LX, Yuan XD. Evaluation of the dual vascular supply patterns in ground-glass nodules with a dynamic volume computed tomography. World J Radiol 2022; 14:155-164. [PMID: 35978977 PMCID: PMC9258305 DOI: 10.4329/wjr.v14.i6.155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 04/20/2022] [Accepted: 06/17/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND In recent years, the detection rate of ground-glass nodules (GGNs) has been improved dramatically due to the popularization of low-dose computed tomography (CT) screening with high-resolution CT technique. This presents challenges for the characterization and management of the GGNs, which depends on a thorough investigation and sufficient diagnostic knowledge of the GGNs. In most diagnostic studies of the GGNs, morphological manifestations are used to differentiate benignancy and malignancy. In contrast, few studies are dedicated to the assessment of the hemodynamics, i.e., perfusion parameters of the GGNs.
AIM To assess the dual vascular supply patterns of GGNs on different histopathology and opacities.
METHODS Forty-seven GGNs from 47 patients were prospectively included and underwent the dynamic volume CT. Histopathologic diagnoses were obtained within two weeks after the CT examination. Blood flow from the bronchial artery [bronchial flow (BF)] and pulmonary artery [pulmonary flow (PF)] as well as the perfusion index (PI) = [PF/(PF + BF)] were obtained using first-pass dual-input CT perfusion analysis and compared respectively between different histopathology and lesion types (pure or mixed GGNs) and correlated with the attenuation values of the lesions using one-way ANOVA, student’s t test and Pearson correlation analysis.
RESULTS Of the 47 GGNs (mean diameter, 8.17 mm; range, 5.3-12.7 mm), 30 (64%) were carcinoma, 6 (13%) were atypical adenomatous hyperplasia and 11 (23%) were organizing pneumonia. All perfusion parameters (BF, PF and PI) demonstrated no significant difference among the three conditions (all P > 0.05). The PFs were higher than the BFs in all the three conditions (all P < 0.001). Of the 30 GGN carcinomas, 14 showed mixed GGNs and 16 pure GGNs with a higher PI in the latter (P < 0.01). Of the 17 benign GGNs, 4 showed mixed GGNs and 13 pure GGNs with no significant difference of the PI between the GGN types (P = 0.21). A negative correlation (r = -0.76, P < 0.001) was demonstrated between the CT attenuation values and the PIs in the 30 GGN carcinomas.
CONCLUSION The GGNs are perfused dominantly by the PF regardless of its histopathology while the weight of the BF in the GGN carcinomas increases gradually during the progress of its opacification.
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Affiliation(s)
- Chao Wang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Ning Wu
- Department of Radiology, The Eighth Medical Center of the People's Liberation Army General Hospital, Beijing 100091, China
| | - Zhuang Zhang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Lai-Xing Zhang
- Department of Graduate, Hebei North University, Zhangjiakou 075000, Hebei Province, China
| | - Xiao-Dong Yuan
- Department of Radiology, The Eighth Medical Center of the People's Liberation Army General Hospital, Beijing 100091, China
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Wang X, Gao M, Xie J, Deng Y, Tu W, Yang H, Liang S, Xu P, Zhang M, Lu Y, Fu C, Li Q, Fan L, Liu S. Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:892890. [PMID: 35747810 PMCID: PMC9209648 DOI: 10.3389/fonc.2022.892890] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
Objective This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention. Methods Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features. The performance of the models was evaluated on an internal test dataset (the “Changzheng Dataset”) and an independent test dataset collected from an external institute (the “Longyan Dataset”). In addition, the performance of automatic diagnostic models was compared with that of manual evaluations by two radiologists on the ‘Longyan dataset’. Results The image-based deep learning model achieved an appealing diagnostic performance, yielding AUC values of 0.75 (95% confidence interval [CI]: 0.62, 0.89) and 0.76 (95% CI: 0.61, 0.90), respectively, on both the Changzheng and Longyan datasets. The clinical feature-based machine learning model performed well on the Changzheng dataset (AUC, 0.80 [95% CI: 0.64, 0.96]), whereas it performed poorly on the Longyan dataset (AUC, 0.62 [95% CI: 0.42, 0.83]). The fusion diagnostic model achieved the best performance on both the Changzheng dataset (AUC, 0.82 [95% CI: 0.71-0.93]) and the Longyan dataset (AUC, 0.83 [95% CI: 0.70-0.96]), and it achieved a better specificity (0.69) than the radiologists (0.33-0.44) on the Longyan dataset. Conclusion The deep learning models, including both the image-based deep learning model and the fusion model, have the ability to assist radiologists in differentiating between benign and malignant nodules for the precise management of patients with GGNs.
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Affiliation(s)
- Xiang Wang
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Man Gao
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Jicai Xie
- Department of Radiology, The Second People’s Hospital of Yuhuan, Yuhuan, China
| | - Yanfang Deng
- Department of Radiology, Longyan First Affiliated Hospital of Fujian Medical University, Fujian, China
| | - Wenting Tu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Hua Yang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Shuang Liang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Panlong Xu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Mingzi Zhang
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Yang Lu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - ChiCheng Fu
- Department of Research, Shanghai Aitrox Technology Corporation Limited, Shanghai, China
| | - Qiong Li
- Department of Radiology, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Li Fan
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
| | - Shiyuan Liu
- Department of Radiology, Changzheng Hospital, Navy Medical University, Shanghai, China
- *Correspondence: Qiong Li, ; Li Fan, ; Shiyuan Liu,
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Ko JP, Bagga B, Gozansky E, Moore WH. Solitary Pulmonary Nodule Evaluation: Pearls and Pitfalls. Semin Ultrasound CT MR 2022; 43:230-245. [PMID: 35688534 DOI: 10.1053/j.sult.2022.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Lung nodules are frequently encountered while interpreting chest CTs and are challenging to detect, characterize, and manage given they can represent both benign or malignant etiologies. An understanding of features associated with malignancy and causes of interpretive pitfalls is helpful to avoid misdiagnoses. This review addresses pertinent topics related to the etiologies for missed lung nodules on radiography and CT. Additionally, CT imaging technical pitfalls and challenges in addition to issues in the evaluation of nodule morphology, attenuation, and size will be discussed. Nodule management guidelines will be addressed as well as recent investigations that further our understanding of lung nodules.
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Affiliation(s)
- Jane P Ko
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY.
| | - Barun Bagga
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - Elliott Gozansky
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
| | - William H Moore
- Department of Radiology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY
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Godoy MCB, Lago EAD, Pria HRFD, Shroff GS, Strange CD, Truong MT. Pearls and Pitfalls in Lung Cancer CT Screening. Semin Ultrasound CT MR 2022; 43:246-256. [PMID: 35688535 DOI: 10.1053/j.sult.2022.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Annual LDCT lung cancer screening is recommended by the United States Preventive Services Task Force (USPSTF) for high-risk population based on the results from the National Lung Cancer Screening Trial (NLST) that showed a significant (20%) reduction in lung cancer-specific mortality rate with the use of annual low-dose computed tomography (LDCT) screening. More recently, the benefits of lung cancer screening were confirmed by the Dutch- Belgian NELSON trial in Europe. With the implementation of lung screening in large scale, knowledge of the limitations related to false positive, false negative and other potential pitfalls is essential to avoid misdiagnosis. This review outlines the most common potential pitfalls in the characterization of screen-detected lung nodules that include artifacts in LDCT, benign nodules that mimic lung cancer, and causes of false negative evaluations of lung cancer with LDCT and PET/CT studies. Awareness of the spectrum of potential pitfalls in pulmonary nodule detection and characterization, including equivocal or atypical presentations, is important for avoiding misinterpretation that can alter patient management.
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Affiliation(s)
- Myrna C B Godoy
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX.
| | - Eduardo A Dal Lago
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - Girish S Shroff
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Chad D Strange
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
| | - Mylene T Truong
- Department of Thoracic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX
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Zhao FH, Fan HJ, Shan KF, Zhou L, Pang ZZ, Fu CL, Yang ZB, Wu MK, Sun JH, Yang XM, Huang ZH. Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules. Front Oncol 2022; 12:872503. [PMID: 35646675 PMCID: PMC9133455 DOI: 10.3389/fonc.2022.872503] [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: 02/09/2022] [Accepted: 04/18/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). Methods We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect. Results There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities. Conclusions The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.
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Affiliation(s)
- Fen-hua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Hong-jie Fan
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Kang-fei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Long Zhou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhen-zhu Pang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chun-long Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Ze-bin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Mei-kang Wu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Ji-hong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao-ming Yang
- Image-Guided Bio-Molecular Intervention Research, Department of Radiology, University of Washington School of Medicine, Seattle, WA, United States
| | - Zhao-hui Huang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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Shi F, Chen B, Cao Q, Wei Y, Zhou Q, Zhang R, Zhou Y, Yang W, Wang X, Fan R, Yang F, Chen Y, Li W, Gao Y, Shen D. Semi-Supervised Deep Transfer Learning for Benign-Malignant Diagnosis of Pulmonary Nodules in Chest CT Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:771-781. [PMID: 34705640 DOI: 10.1109/tmi.2021.3123572] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Lung cancer is the leading cause of cancer deaths worldwide. Accurately diagnosing the malignancy of suspected lung nodules is of paramount clinical importance. However, to date, the pathologically-proven lung nodule dataset is largely limited and is highly imbalanced in benign and malignant distributions. In this study, we proposed a Semi-supervised Deep Transfer Learning (SDTL) framework for benign-malignant pulmonary nodule diagnosis. First, we utilize a transfer learning strategy by adopting a pre-trained classification network that is used to differentiate pulmonary nodules from nodule-like tissues. Second, since the size of samples with pathological-proven is small, an iterated feature-matching-based semi-supervised method is proposed to take advantage of a large available dataset with no pathological results. Specifically, a similarity metric function is adopted in the network semantic representation space for gradually including a small subset of samples with no pathological results to iteratively optimize the classification network. In this study, a total of 3,038 pulmonary nodules (from 2,853 subjects) with pathologically-proven benign or malignant labels and 14,735 unlabeled nodules (from 4,391 subjects) were retrospectively collected. Experimental results demonstrate that our proposed SDTL framework achieves superior diagnosis performance, with accuracy = 88.3%, AUC = 91.0% in the main dataset, and accuracy = 74.5%, AUC = 79.5% in the independent testing dataset. Furthermore, ablation study shows that the use of transfer learning provides 2% accuracy improvement, and the use of semi-supervised learning further contributes 2.9% accuracy improvement. Results implicate that our proposed classification network could provide an effective diagnostic tool for suspected lung nodules, and might have a promising application in clinical practice.
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Jiang Y, Xiong Z, Zhao W, Zhang J, Guo Y, Li G, Li Z. Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs. Gan To Kagaku Ryoho 2022; 70:880-890. [PMID: 35301662 DOI: 10.1007/s11748-022-01801-x] [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: 12/17/2021] [Accepted: 03/03/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.
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Affiliation(s)
- Yining Jiang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Ziqi Xiong
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenjing Zhao
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Jingyu Zhang
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Yan Guo
- GE Healthcare, Beijing, China
| | - Guosheng Li
- Department of Pathology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Zhiyong Li
- Department of Radiology, The First Affiliated Hospital of Dalian Medical University, Dalian, China. .,Dalian Engineering Research Centre for Artificial Intelligence in Medical Imaging, Dalian, China.
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Xie S, Li S, Deng H, Han Y, Liu G, Liu Q. Application Value of PET/CT and MRI in the Diagnosis and Treatment of Patients With Synchronous Multiple Pulmonary Ground-Glass Nodules. Front Oncol 2022; 12:797823. [PMID: 35280735 PMCID: PMC8905144 DOI: 10.3389/fonc.2022.797823] [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: 10/19/2021] [Accepted: 01/21/2022] [Indexed: 11/29/2022] Open
Abstract
Background Synchronous multiple ground-glass nodules (SMGGNs) in synchronous multiple lung cancers are associated with specific imaging findings. It is difficult to distinguish whether multiple nodules are primary tumors or metastatic lesions in the lungs. The need for PET/CT and contrast-enhanced brain MRI for these patients remains unclear. This study investigated the necessity of these two imaging examinations for SMGGN patients by means of retrospective analysis. Methods SMGGN patients who were diagnosed and treated in our hospital from October 2017 to May 2020 and underwent whole-body PET/CT(Cranial excepted) and/or contrast-enhanced brain MRI+DWI were enrolled in this study. We analyzed the imaging and clinical characteristics of these patients to evaluate SMGGN patients’ need to undergo whole-body PET/CT and brain MRI examination. Results A total of 87 SMGGN patients were enrolled. 51 patients underwent whole-body PET/CT examinations and did not show signs of primary tumors in other organs, metastatic foci in other organs, or metastasis to surrounding lymph nodes. 87 patients underwent whole-brain MRI, which did not reveal brain metastases but did detect an old cerebral infarction in 23 patients and a new cerebral infarction in one patient. 87 patients underwent surgical treatment in which 219 nodules were removed. All nodules were diagnosed as adenocarcinoma or atypical adenomatous hyperplasia. No lymph node metastasis was noted. Conclusion For SMGGN patients, PET/CT and enhanced cranial MRI are unnecessary for SMGGNs patients, but from the perspective of perioperative patient safety, preoperative MRI+DWI examination is recommended for SMGGNs patients.
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Affiliation(s)
- Shaonan Xie
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shaoteng Li
- Department of Diagnostic Radiology, The People's Hospital of Xingtai, Xingtai, China
| | - Huiyan Deng
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yaqing Han
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangjie Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Qingyi Liu
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
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He XQ, Li X, Wu Y, Wu S, Luo TY, Lv FJ, Li Q. Differential Diagnosis of Nonabsorbable Inflammatory and Malignant Subsolid Nodules with a Solid Component ≤5 mm. J Inflamm Res 2022; 15:1785-1796. [PMID: 35300212 PMCID: PMC8923683 DOI: 10.2147/jir.s355848] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 03/01/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose To investigate the differential clinical and computed tomography (CT) characteristics of pulmonary nonabsorbable inflammatory and malignant subsolid nodules (SSNs) with a solid component ≤5 mm. Patients and Methods We retrospectively analyzed 576 consecutive patients who underwent surgical resection and had SSNs with a solid component ≤5 mm on CT images. These patients were divided into inflammatory and malignant groups according to pathology. Their clinical and imaging data were analyzed and compared. Multiple logistic regression analysis was used to identify independent prognostic factors differentiating inflammatory from malignant SSNs. Furthermore, 146 consecutive patients were included as internal validation cohort to test the prediction efficiency of this model. Results Significant differences in 11 clinical characteristics and CT features were found between both groups (P < 0.05). Presence of respiratory symptoms, distribution of middle/lower lobe, irregular shape, part-solid nodule (PSNs), CT value of ground-glass opacity (GGO) areas <−657 Hu, presence of abnormal intra-nodular vessel sign, and interlobular septal thickening were the most effective factors for diagnosing nonabsorbable inflammatory SSNs, with an AUC (95% CI), accuracy, sensitivity, and specificity of 0.843 (95% CI: 0.811–0.872), 89.76%, 72.86%, and 81.23%, respectively. The internal validation cohort obtained an AUC (95% CI), accuracy, sensitivity, and specificity of 0.830 (95% CI: 0.759–0.887), 83.56%, 73.91%, and 76.42%, respectively. Conclusion Nonabsorbable inflammatory and malignant SSNs with a solid component ≤5 mm exhibited different clinical and imaging characteristics.
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Affiliation(s)
- Xiao-Qun He
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Xian Li
- Department of Pathology, Chongqing Medical University, Chongqing, People’s Republic of China
| | - Yan Wu
- Nursing School, Chongqing Medical University, Chongqing, People’s Republic of China
| | - Shun Wu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Tian-You Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
- Correspondence: Qi Li, Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu Zhong District, Chongqing, 400016, People’s Republic of China, Tel +86 15823408652, Fax +86 23 68811487, Email
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Computed tomography of ground glass nodule image based on fuzzy C-means clustering algorithm to predict invasion of pulmonary adenocarcinoma. JOURNAL OF RADIATION RESEARCH AND APPLIED SCIENCES 2022. [DOI: 10.1016/j.jrras.2022.01.015] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Yu Y, Fu Y, Chen X, Zhang Y, Zhang F, Li X, Zhao X, Cheng J, Wu H. Dual-layer spectral detector CT: predicting the invasiveness of pure ground-glass adenocarcinoma. Clin Radiol 2022; 77:e458-e465. [DOI: 10.1016/j.crad.2022.02.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 02/02/2022] [Indexed: 12/15/2022]
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Chen X, Wang Z, Qi Q, Zhang K, Sui X, Wang X, Weng W, Wang S, Zhao H, Sun C, Wang D, Zhang H, Liu E, Zou T, Hong N, Yang F. A fully automated noncontrast CT 3-D reconstruction algorithm enabled accurate anatomical demonstration for lung segmentectomy. Thorac Cancer 2022; 13:795-803. [PMID: 35142044 PMCID: PMC8930461 DOI: 10.1111/1759-7714.14322] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 12/30/2021] [Accepted: 01/03/2022] [Indexed: 01/19/2023] Open
Abstract
Background Three‐dimensional reconstruction of chest computerized tomography (CT) excels in intuitively demonstrating anatomical patterns for pulmonary segmentectomy. However, current methods are labor‐intensive and rely on contrast CT. We hereby present a novel fully automated reconstruction algorithm based on noncontrast CT and assess its performance both independently and in combination with surgeons. Methods A retrospective pilot study was performed. Patients between May 2020 to August 2020 who underwent segmentectomy in our single institution were enrolled. Noncontrast CTs were used for reconstruction. In the first part of the study, the accuracy of the demonstration of anatomical variants by either automated or manual reconstruction algorithm were compared to surgical observation, respectively. In the second part of the study, we tested the accuracy of the identification of anatomical variants by four independent attendees who reviewed 3‐D reconstruction in combination with CT scans. Results A total of 20 cases were enrolled in this study. All segments were represented in this study with two left S1‐3, two left S4 + 5, one left S6, five left basal segmentectomies, one right S1, three right S2, 1 right S2b + 3a, one right S3, two right S6 and two right basal segmentectomies. The median time consumption for the automated reconstruction was 280 (205–324) s. Accurate vessel and bronchial detection were achieved in 85% by the AI approach and 80% by Mimics, p = 1.00. The accuracy of vessel classification was 80 and 95% by AI and manual approaches, respectively, p = 0.34. In real‐world application, the accuracy of the identification of anatomical variant by thoracic surgeons was 85% by AI+CT, and the median time consumption was 2 (1–3) min. Conclusions The AI reconstruction algorithm overcame defects of traditional methods and is valuable in surgical planning for segmentectomy. With the AI reconstruction, surgeons may achieve high identification accuracy of anatomical patterns in a short time frame.
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Affiliation(s)
- Xiuyuan Chen
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Zhenfan Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Qingyi Qi
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Kai Zhang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Xizhao Sui
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Xun Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Wenhan Weng
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Shaodong Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Heng Zhao
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Chao Sun
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Dawei Wang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Huajie Zhang
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Enyou Liu
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Tong Zou
- Institute of Advanced Research, Infervision Medical Technology Co., Ltd, Beijing, China
| | - Nan Hong
- Department of Radiology, Peking University People's Hospital, Beijing, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
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Zhong F, Liu Z, An W, Wang B, Zhang H, Liu Y, Liao M. Radiomics Study for Discriminating Second Primary Lung Cancers From Pulmonary Metastases in Pulmonary Solid Lesions. Front Oncol 2022; 11:801213. [PMID: 35047410 PMCID: PMC8761898 DOI: 10.3389/fonc.2021.801213] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 12/06/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The objective of this study was to assess the value of quantitative radiomics features in discriminating second primary lung cancers (SPLCs) from pulmonary metastases (PMs). METHODS This retrospective study enrolled 252 malignant pulmonary nodules with histopathologically confirmed SPLCs or PMs and randomly assigned them to a training or validation cohort. Clinical data were collected from the electronic medical records system. The imaging and radiomics features of each nodule were extracted from CT images. RESULTS A rad-score was generated from the training cohort using the least absolute shrinkage and selection operator regression. A clinical and radiographic model was constructed using the clinical and imaging features selected by univariate and multivariate regression. A nomogram composed of clinical-radiographic factors and a rad-score were developed to validate the discriminative ability. The rad-scores differed significantly between the SPLC and PM groups. Sixteen radiomics features and four clinical-radiographic features were selected to build the final model to differentiate between SPLCs and PMs. The comprehensive clinical radiographic-radiomics model demonstrated good discriminative capacity with an area under the curve of the receiver operating characteristic curve of 0.9421 and 0.9041 in the respective training and validation cohorts. The decision curve analysis demonstrated that the comprehensive model showed a higher clinical value than the model without the rad-score. CONCLUSION The proposed model based on clinical data, imaging features, and radiomics features could accurately discriminate SPLCs from PMs. The model thus has the potential to support clinicians in improving decision-making in a noninvasive manner.
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Affiliation(s)
- Feiyang Zhong
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Zhenxing Liu
- Department of Neurology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Wenting An
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Binchen Wang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Hanfei Zhang
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Yumin Liu
- Department of Neurology, Zhongnan Hospital of Wuhan University, Wuhan, China
| | - Meiyan Liao
- Department of Radiology, Zhongnan Hospital of Wuhan University, Wuhan, China
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Li WJ, Lv FJ, Tan YW, Fu BJ, Chu ZG. Benign and malignant pulmonary part-solid nodules: differentiation via thin-section computed tomography. Quant Imaging Med Surg 2022; 12:699-710. [PMID: 34993112 DOI: 10.21037/qims-21-145] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Accepted: 08/11/2021] [Indexed: 12/18/2022]
Abstract
BACKGROUND Pulmonary part-solid nodules (PSNs) reportedly have a high possibility of malignancy, while benign PSNs are common. This study aimed to reveal the differences between benign and malignant PSNs by comparing their thin-section computed tomography (CT) features. METHODS Patients with PSNs confirmed by postoperative pathological examination or follow-up (at the same period) were retrospectively enrolled from March 2016 to January 2020. The clinical data of patients and CT features of benign and malignant PSNs were reviewed and compared. Binary logistic regression analysis was performed to reveal the predictors of malignant PSNs. RESULTS A total of 119 PSNs in 117 patients [age (mean ± standard deviation), 56±11 years; 70 women] were evaluated. Of the 119 PSNs, 44 (37.0%) were benign, and 75 (63.0%) were malignant (12 adenocarcinomas in situ, 22 minimally invasive adenocarcinomas, and 41 invasive adenocarcinomas). There were significant differences in the patients' age and smoking history between benign and malignant PSNs. In terms of CT characteristics, malignant and benign lesions significantly differed in the following CT features: whole nodule, internal solid component, and peripheral ground-glass opacity. The binary logistic regression analysis revealed that well-defined border [odds ratio (OR), 4.574; 95% confidence interval (CI), 1.186-17.643; P=0.027] and lobulation (OR, 61.739; 95% CI, 5.230-728.860; P=0.001) of the nodule, as well as irregular shape (OR, 9.502; 95% CI, 1.788-50.482; P=0.008) and scattered distribution (OR, 13.238; 95% CI, 1.359-128.924; P=0.026) of the internal solid components were significant independent predictors distinguishing malignant PSNs. However, the lesion shape, density, and margin were similar between malignant and benign lesions. CONCLUSIONS Well-defined and lobulated PSNs with irregular and scattered solid components are highly likely to be malignant.
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Affiliation(s)
- Wang-Jia Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fa-Jin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yi-Wen Tan
- Department of Pathology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Bin-Jie Fu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Zhi-Gang Chu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Zhang T, Li X, Liu J. Prediction of the Invasiveness of Ground-Glass Nodules in Lung Adenocarcinoma by Radiomics Analysis Using High-Resolution Computed Tomography Imaging. Cancer Control 2022; 29:10732748221089408. [PMID: 35848489 PMCID: PMC9297444 DOI: 10.1177/10732748221089408] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Background Pure ground-glass nodules (pGGNs) have been considered inert tumors due to their biological behavior; however, their prognosis is not completely consistent because of differences in internal pathological component. The aim of this study was to explore whether radiomics can be used to identify the invasiveness of pGGNs. Methods The retrospective study received the relevant ethical approval. After postoperative pathological confirmation, sixty-five patients with lung adenocarcinoma pGGNs (≤30 mm) were enrolled in this study from January 2015 to October 2018. All the cases were randomly divided into training and test groups in a 7:3 ratio. In total, 385 radiomics features were obtained from HRCT images, and then least absolute shrinkage and selection operator (LASSO) logistic regression was applied to the training group to obtain optimal features to distinguish the invasion degree of lesions. The diagnostic efficiency of the radiomics model was estimated by the area under the curve (AUC) of the receiver operating curve (ROC), and verified by the test group. Results The optimal features (“GLCMEntropy_angle135_offset1” and “Sphericity”) were selected after applying the LASSO regression to develop the proposed radiomics model. This prediction model exhibited good differentiation between pre-invasive and invasive lesions. The AUC for the test group was 0.824 (95%CI: 0.599-1.000), indicating that the radiomics model has some prediction ability. Conclusion The HRCT radiomics features can discriminate pre-invasive from invasive lung adenocarcinoma pGGNs. This non-invasive method can provide more information for surgeons before operation, and can also predict the prognosis of patients to some extent.
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Affiliation(s)
- Tianqi Zhang
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China.,Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
| | - Xiuling Li
- College of Applied Mathematics, 66445Jilin University of Finance and Economics, Changchun, China
| | - Jianhua Liu
- Department of Radiology, 12510the Second Hospital of Jilin University, Changchun, China
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Huo JW, Huang XT, Li X, Gong JW, Luo TY, Li Q. Pneumonic-type lung adenocarcinoma with different ranges exhibiting different clinical, imaging, and pathological characteristics. Insights Imaging 2021; 12:169. [PMID: 34787725 PMCID: PMC8599601 DOI: 10.1186/s13244-021-01114-2] [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: 09/23/2021] [Accepted: 10/25/2021] [Indexed: 02/08/2023] Open
Abstract
Background Pneumonic-type lung adenocarcinoma (PLADC) with different ranges might exhibit different imaging and clinicopathological features. This study divided PLADC into localized PLADC (L-PLADC) and diffuse PLADC (D-PLADC) based on imaging and aimed to clarify the differences in clinical, imaging, and pathologic characteristics between the two new subtypes. Results The data of 131 patients with L-PLADC and 117 patients with D-PLADC who were pathologically confirmed and underwent chest computed tomography (CT) at our institute from December 2014 to December 2020 were retrospectively collected. Patients with L-PLADC were predominantly female, non-smokers, and without respiratory symptoms and elevated white blood cell count and C-reactive protein level, whereas those with D-PLADC were predominantly male, smokers, and had respiratory symptoms and elevated white blood cell count and C-reactive protein level (all p < 0.05). Pleural retraction was more common in L-PLADC, whereas interlobular fissure bulging, hypodense sign, air space, CT angiogram sign, coexisting nodules, pleural effusion, and lymphadenopathy were more frequent in D-PLADC (all p < 0.001). Among the 129 patients with surgically resected PLADC, the most common histological subtype of L-PLADC was acinar-predominant growth pattern (76.7%, 79/103), whereas that of D-PLADC was invasive mucinous adenocarcinoma (80.8%, 21/26). Among the 136 patients with EGFR mutation status, L-PLADC had a significantly higher EGFR mutation rate than D-PLADC (p < 0.001). Conclusions L-PLADC and D-PLADC have different clinical, imaging, and pathological characteristics. This new imaging-based classification may help improve our understanding of PLADC and develop personalized treatment plans, with concomitant implications for patient outcomes.
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Affiliation(s)
- Ji-Wen Huo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu zhong District, Chongqing, 400016, China
| | - Xing-Tao Huang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu zhong District, Chongqing, 400016, China
| | - Xian Li
- Department of Pathology, Chongqing Medical University, No.1 Youyi Road, Yuzhong District, Chongqing, 400016, China
| | - Jun-Wei Gong
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu zhong District, Chongqing, 400016, China
| | - Tian-You Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu zhong District, Chongqing, 400016, China
| | - Qi Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yu zhong District, Chongqing, 400016, China.
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Zhu P, Xu XJ, Zhang MM, Fan SF. High-resolution computed tomography findings independently predict epidermal growth factor receptor mutation status in ground-glass nodular lung adenocarcinoma. World J Clin Cases 2021; 9:9792-9803. [PMID: 34877318 PMCID: PMC8610895 DOI: 10.12998/wjcc.v9.i32.9792] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/30/2021] [Accepted: 09/23/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND For lung adenocarcinoma with epidermal growth factor receptor (EGFR) gene mutation, small molecule tyrosine kinase inhibitors are more effective. Some patients could not obtain enough histological specimens for EGFR gene mutation detection. Specific imaging features can predict EGFR mutation status to a certain extent.
AIM To assess the associations of EGFR mutations with high-resolution computerized tomography (HRCT) features in ground-glass nodular lung adenocarcinoma.
METHODS This study retrospectively assessed patients with ground-glass nodular lung adenocarcinoma diagnosed between January 2011 and March 2017. EGFR gene mutations in exons 18-21 were detected. The patients were classified into mutant EGFR and wild-type groups, and general data and HRCT image characteristics were assessed.
RESULTS Among 98 patients, 31 (31.6%) and 67 (68.4%) had mutated and wild-type EGFR in exons 18-21, respectively. Gender, age, smoking history, location of lesions, morphology, edges, borders, pleural indentations, and associations of nodules with bronchus and blood vessels were comparable in both groups (all P > 0.05). Patients with mutant EGFR had larger nodules than those with the wild-type (17.19 ± 6.79 and 14.37 ± 6.30 mm, respectively; P = 0.047). Meanwhile, the vacuole/honeycomb sign was more frequent in the mutant EGFR group (P = 0.011). The logistic regression prediction model included the combination of nodule size and vacuole/honeycomb sign (OR = 1.120, 95%CI: 1.023-1.227, P = 0.014) revealed a sensitivity of 83.9%, a specificity of 52.2% and an AUC of 0.698 (95%CI: 0.589-0.806; P = 0.002).
CONCLUSION Nodule size and vacuole/honeycomb features could independently predict EGFR mutation status in ground-glass nodular lung adenocarcinoma.
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Affiliation(s)
- Ping Zhu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Xiao-Jun Xu
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Min-Ming Zhang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
| | - Shu-Feng Fan
- Department of Radiology, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
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