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Hanaoka T, Matoba H, Nakayama J, Ono S, Ikegawa K, Okada M. A spatio-temporal image analysis for growth of indeterminate pulmonary nodules detected by CT scan. Radiol Phys Technol 2024; 17:71-82. [PMID: 37889460 DOI: 10.1007/s12194-023-00750-1] [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: 06/07/2023] [Revised: 09/29/2023] [Accepted: 10/05/2023] [Indexed: 10/28/2023]
Abstract
The objective is to evaluate the performance of computational image classification for indeterminate pulmonary nodules (IPN) chronologically detected by CT scan. Total 483 patients with 670 abnormal pulmonary nodules, who were taken chest thin-section CT (TSCT) images at least twice and resected as suspicious nodules in our hospital, were enrolled in this study. Nodular regions from the initial and the latest TSCT images were cut manually for each case, and approached by Python development environment, using the open-source cv2 library, to measure the nodular change rate (NCR). These NCRs were statistically compared with clinico-pathological factors, and then, this discriminator was evaluated for clinical performance. NCR showed significant differences among the nodular consistencies. In terms of histological subtypes, NCR of invasive adenocarcinoma (ADC) were significantly distinguishable from other lesions, but not from minimally invasive ADC. Only for cancers, NCR was significantly associated with loco-regional invasivity, p53-immunoreactivity, and Ki67-immunoreactivity. Regarding Epidermal Growth Factor Receptor gene mutation of ADC-related nodules, NCR showed a significant negative correlation. On staging of lung cancer cases, NCR was significantly increased with progression from pTis-stage 0 up to pT1b-stage IA2. For clinical shared decision-making (SDM) whether urgent resection or watchful-waiting, receiver operating characteristic (ROC) analysis showed that area under the ROC curve was 0.686. For small-sized IPN detected by CT scan, this approach shows promise as a potential navigator to improve work-up for life-threatening cancer screening and assist SDM before surgery.
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Affiliation(s)
- Takaomi Hanaoka
- Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan.
| | - Hisanori Matoba
- Department of Molecular Pathology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Jun Nakayama
- Department of Molecular Pathology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
- Department of Pathology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-Machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Shotaro Ono
- Department of Thoracic Surgery, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Kayoko Ikegawa
- Department of Respirology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
| | - Mitsuyo Okada
- Department of Respirology, JA Nagano North Alps Medical Center Azumi Hospital, 3207-1 Ikeda, Ikeda-machi, Kita-azumi-gun, Nagano, 399-8605, Japan
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de Margerie-Mellon C, Chassagnon G. Artificial intelligence: A critical review of applications for lung nodule and lung cancer. Diagn Interv Imaging 2023; 104:11-17. [PMID: 36513593 DOI: 10.1016/j.diii.2022.11.007] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/14/2022]
Abstract
Artificial intelligence (AI) is a broad concept that usually refers to computer programs that can learn from data and perform certain specific tasks. In the recent years, the growth of deep learning, a successful technique for computer vision tasks that does not require explicit programming, coupled with the availability of large imaging databases fostered the development of multiple applications in the medical imaging field, especially for lung nodules and lung cancer, mostly through convolutional neural networks (CNN). Some of the first applications of AI is this field were dedicated to automated detection of lung nodules on X-ray and computed tomography (CT) examinations, with performances now reaching or exceeding those of radiologists. For lung nodule segmentation, CNN-based algorithms applied to CT images show excellent spatial overlap index with manual segmentation, even for irregular and ground glass nodules. A third application of AI is the classification of lung nodules between malignant and benign, which could limit the number of follow-up CT examinations for less suspicious lesions. Several algorithms have demonstrated excellent capabilities for the prediction of the malignancy risk when a nodule is discovered. These different applications of AI for lung nodules are particularly appealing in the context of lung cancer screening. In the field of lung cancer, AI tools applied to lung imaging have been investigated for distinct aims. First, they could play a role for the non-invasive characterization of tumors, especially for histological subtype and somatic mutation predictions, with a potential therapeutic impact. Additionally, they could help predict the patient prognosis, in combination to clinical data. Despite these encouraging perspectives, clinical implementation of AI tools is only beginning because of the lack of generalizability of published studies, of an inner obscure working and because of limited data about the impact of such tools on the radiologists' decision and on the patient outcome. Radiologists must be active participants in the process of evaluating AI tools, as such tools could support their daily work and offer them more time for high added value tasks.
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Affiliation(s)
- Constance de Margerie-Mellon
- Université Paris Cité, Laboratory of Imaging Biomarkers, Center for Research on Inflammation, UMR 1149, INSERM, 75018 Paris, France; Department of Radiology, Hôpital Saint-Louis APHP, 75010 Paris, France
| | - Guillaume Chassagnon
- Université Paris Cité, Faculté de Médecine, 75006 Paris, France; Department of Radiology, Hôpital Cochin APHP, 75014 Paris, France
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Zhao W, Sun Y, Kuang K, Yang J, Li G, Ni B, Jiang Y, Jiang B, Liu J, Li M. ViSTA: A Novel Network Improving Lung Adenocarcinoma Invasiveness Prediction from Follow-Up CT Series. Cancers (Basel) 2022; 14:cancers14153675. [PMID: 35954342 PMCID: PMC9367560 DOI: 10.3390/cancers14153675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 11/21/2022] Open
Abstract
Simple Summary Assessing follow-up computed tomography(CT) series is of great importance in clinical practice for lung nodule diagnosis. Deep learning is a thriving data mining method in medical imaging and has obtained surprising results. However, previous studies mostly focused on the analysis of single static time points instead of the entire follow-up series and required regular intervals between CT examinations. In the current study, we propose a new deep learning framework, named ViSTA, that can better evaluate tumor invasiveness using irregularly serial follow-up CT images to avoid aggressive procedures or delay diagnosis in clinical practice. ViSTA provides a new solution for irregularly sampled data. ViSTA delivers superior performance compared with other static or serial deep learning models. The proposed ViSTA framework is capable of improving performance close to the human level in the prediction of invasiveness of lung adenocarcinoma while being transferrable to other tasks analyzing serial medical data. Abstract To investigate the value of the deep learning method in predicting the invasiveness of early lung adenocarcinoma based on irregularly sampled follow-up computed tomography (CT) scans. In total, 351 nodules were enrolled in the study. A new deep learning network based on temporal attention, named Visual Simple Temporal Attention (ViSTA), was proposed to process irregularly sampled follow-up CT scans. We conducted substantial experiments to investigate the supplemental value in predicting the invasiveness using serial CTs. A test set composed of 69 lung nodules was reviewed by three radiologists. The performance of the model and radiologists were compared and analyzed. We also performed a visual investigation to explore the inherent growth pattern of the early adenocarcinomas. Among counterpart models, ViSTA showed the best performance (AUC: 86.4% vs. 60.6%, 75.9%, 66.9%, 73.9%, 76.5%, 78.3%). ViSTA also outperformed the model based on Volume Doubling Time (AUC: 60.6%). ViSTA scored higher than two junior radiologists (accuracy of 81.2% vs. 75.4% and 71.0%) and came close to the senior radiologist (85.5%). Our proposed model using irregularly sampled follow-up CT scans achieved promising accuracy in evaluating the invasiveness of the early stage lung adenocarcinoma. Its performance is comparable with senior experts and better than junior experts and traditional deep learning models. With further validation, it can potentially be applied in clinical practice.
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Affiliation(s)
- Wei Zhao
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (W.Z.); (Y.J.); (B.J.)
| | - Yingli Sun
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai 200040, China;
| | - Kaiming Kuang
- Dianei Technology, Shanghai 200051, China; (K.K.); (J.Y.)
| | - Jiancheng Yang
- Dianei Technology, Shanghai 200051, China; (K.K.); (J.Y.)
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Ge Li
- Department of Radiology, The Xiangya Hospital, Central South University, Changsha 410008, China;
| | - Bingbing Ni
- Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
| | - Yingjia Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (W.Z.); (Y.J.); (B.J.)
| | - Bo Jiang
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (W.Z.); (Y.J.); (B.J.)
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha 410011, China; (W.Z.); (Y.J.); (B.J.)
- Radiology Quality Control Center, Changsha 410011, China
- Correspondence: (J.L.); (M.L.); Tel.: +86-137-8708-5002 (J.L.); +86-138-1662-0371 (M.L.); Fax: +86-0731-85292116 (J.L.); +86-21-57643271 (M.L.)
| | - Ming Li
- Department of Radiology, Huadong Hospital, Fudan University, Shanghai 200040, China;
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200437, China
- Correspondence: (J.L.); (M.L.); Tel.: +86-137-8708-5002 (J.L.); +86-138-1662-0371 (M.L.); Fax: +86-0731-85292116 (J.L.); +86-21-57643271 (M.L.)
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Shalmon T, Salazar P, Horie M, Hanneman K, Pakkal M, Anwari V, Fratesi J. Predefined and data driven CT densitometric features predict critical illness and hospital length of stay in COVID-19 patients. Sci Rep 2022; 12:8143. [PMID: 35581369 PMCID: PMC9114017 DOI: 10.1038/s41598-022-12311-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 05/09/2022] [Indexed: 11/09/2022] Open
Abstract
The aim of this study was to compare whole lung CT density histograms to predict critical illness outcome and hospital length of stay in a cohort of 80 COVID-19 patients. CT chest images on segmented lungs were retrospectively analyzed. Functional Principal Component Analysis (FPCA) was used to find the main modes of variations on CT density histograms. CT density features, the CT severity score, the COVID-GRAM score and the patient clinical data were assessed for predicting the patient outcome using logistic regression models and survival analysis. ROC analysis predictors of critically ill status: 87.5th percentile CT density (Q875)—AUC 0.88 95% CI (0.79 0.94), F1-CT—AUC 0.87 (0.77 0.93) Standard Deviation (SD-CT)—AUC 0.86 (0.73, 0.93). Multivariate models combining CT-density predictors and Neutrophil–Lymphocyte Ratio showed the highest accuracy. SD-CT, Q875 and F1 score were significant predictors of hospital length of stay (LOS) while controlling for hospital death using competing risks models. Moreover, two multivariate Fine-Gray regression models combining the clinical variables: age, NLR, Contrast CT factor with either Q875 or F1 CT-density predictors revealed significant effects for the prediction of LOS incidence in presence of a competing risk (death) and acceptable predictive performances (Bootstrapped C-index 0.74 [0.70 0.78]).
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Affiliation(s)
- Tamar Shalmon
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | | | - Miho Horie
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Kate Hanneman
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Mini Pakkal
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Vahid Anwari
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada.,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada
| | - Jennifer Fratesi
- Joint Department of Medical Imaging, University of Toronto, Toronto, ON, Canada. .,University Health Network, Toronto General Hospital, 200 Elizabeth St, Toronto, ON, M5G 2C4, Canada.
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Gul NH, Ripley RT. Commentary: Radiomics: Can We Demystify the Subsolid Nodules? Semin Thorac Cardiovasc Surg 2021; 34:711. [PMID: 34089827 DOI: 10.1053/j.semtcvs.2021.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 05/25/2021] [Indexed: 11/11/2022]
Affiliation(s)
- Nabeel H Gul
- Division of General Thoracic Surgery, The Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas
| | - R Taylor Ripley
- Division of General Thoracic Surgery, The Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, Texas.
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