1
|
Sourlos N, Pelgrim G, Wisselink HJ, Yang X, de Jonge G, Rook M, Prokop M, Sidorenkov G, van Tuinen M, Vliegenthart R, van Ooijen PMA. Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT. Eur Radiol Exp 2024; 8:63. [PMID: 38764066 PMCID: PMC11102890 DOI: 10.1186/s41747-024-00459-9] [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: 12/01/2023] [Accepted: 03/18/2024] [Indexed: 05/21/2024] Open
Abstract
BACKGROUND Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR). METHODS Individuals were selected from the "Lifelines" cohort who had undergone low-dose chest CT. Nodules in individuals without emphysema were matched to similar-sized nodules in individuals with at least moderate emphysema. AI results for nodular findings of 30-100 mm3 and 101-300 mm3 were compared to those of HR; two expert radiologists blindly reviewed discrepancies. Sensitivity and false positives (FPs)/scan were compared for emphysema and non-emphysema groups. RESULTS Thirty-nine participants with and 82 without emphysema were included (n = 121, aged 61 ± 8 years (mean ± standard deviation), 58/121 males (47.9%)). AI and HR detected 196 and 206 nodular findings, respectively, yielding 109 concordant nodules and 184 discrepancies, including 118 true nodules. For AI, sensitivity was 0.68 (95% confidence interval 0.57-0.77) in emphysema versus 0.71 (0.62-0.78) in non-emphysema, with FPs/scan 0.51 and 0.22, respectively (p = 0.028). For HR, sensitivity was 0.76 (0.65-0.84) and 0.80 (0.72-0.86), with FPs/scan of 0.15 and 0.27 (p = 0.230). Overall sensitivity was slightly higher for HR than for AI, but this difference disappeared after the exclusion of benign lymph nodes. FPs/scan were higher for AI in emphysema than in non-emphysema (p = 0.028), while FPs/scan for HR were higher than AI for 30-100 mm3 nodules in non-emphysema (p = 0.009). CONCLUSIONS AI resulted in more FPs/scan in emphysema compared to non-emphysema, a difference not observed for HR. RELEVANCE STATEMENT In the creation of a benchmark dataset to validate AI software for lung nodule detection, the inclusion of emphysema cases is important due to the additional number of FPs. KEY POINTS • The sensitivity of nodule detection by AI was similar in emphysema and non-emphysema. • AI had more FPs/scan in emphysema compared to non-emphysema. • Sensitivity and FPs/scan by the human reader were comparable for emphysema and non-emphysema. • Emphysema and non-emphysema representation in benchmark dataset is important for validating AI.
Collapse
Affiliation(s)
- Nikos Sourlos
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - GertJan Pelgrim
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- Department of Oral Surgery of the Medical Spectrum Twente (MST), Enschede, 7500KA, The Netherlands
| | - Hendrik Joost Wisselink
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Xiaofei Yang
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Gonda de Jonge
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Mieneke Rook
- Department of Radiology, Martini Hospital, Groningen, 9728NT, The Netherlands
| | - Mathias Prokop
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Grigory Sidorenkov
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- Department of Epidemiology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Marcel van Tuinen
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center of Groningen, Groningen, 9713GZ, The Netherlands
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands
| | - Peter M A van Ooijen
- DataScience Center in Health (DASH), University Medical Center Groningen, Groningen, 9713GZ, The Netherlands.
- Department of Radiation Oncology, University Medical Center Groningen, Groningen, 9713GZ, The Netherlands.
| |
Collapse
|
2
|
Nomura Y, Hanaoka S, Hayashi N, Yoshikawa T, Koshino S, Sato C, Tatsuta M, Tanaka Y, Kano S, Nakaya M, Inui S, Kusakabe M, Nakao T, Miki S, Watadani T, Nakaoka R, Shimizu A, Abe O. Performance changes due to differences among annotating radiologists for training data in computerized lesion detection. Int J Comput Assist Radiol Surg 2024:10.1007/s11548-024-03136-9. [PMID: 38625446 DOI: 10.1007/s11548-024-03136-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/28/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE The quality and bias of annotations by annotators (e.g., radiologists) affect the performance changes in computer-aided detection (CAD) software using machine learning. We hypothesized that the difference in the years of experience in image interpretation among radiologists contributes to annotation variability. In this study, we focused on how the performance of CAD software changes with retraining by incorporating cases annotated by radiologists with varying experience. METHODS We used two types of CAD software for lung nodule detection in chest computed tomography images and cerebral aneurysm detection in magnetic resonance angiography images. Twelve radiologists with different years of experience independently annotated the lesions, and the performance changes were investigated by repeating the retraining of the CAD software twice, with the addition of cases annotated by each radiologist. Additionally, we investigated the effects of retraining using integrated annotations from multiple radiologists. RESULTS The performance of the CAD software after retraining differed among annotating radiologists. In some cases, the performance was degraded compared to that of the initial software. Retraining using integrated annotations showed different performance trends depending on the target CAD software, notably in cerebral aneurysm detection, where the performance decreased compared to using annotations from a single radiologist. CONCLUSIONS Although the performance of the CAD software after retraining varied among the annotating radiologists, no direct correlation with their experience was found. The performance trends differed according to the type of CAD software used when integrated annotations from multiple radiologists were used.
Collapse
Affiliation(s)
- Yukihiro Nomura
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, 263-8522, Japan.
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan.
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Saori Koshino
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Chiaki Sato
- Department of Radiology, Tokyo Metropolitan Bokutoh Hospital, Tokyo, Japan
| | - Momoko Tatsuta
- Department of Diagnostic Radiology, Kitasato University Hospital, Sagamihara, Kanagawa, Japan
| | - Yuya Tanaka
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shintaro Kano
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Moto Nakaya
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shohei Inui
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | | | - Takahiro Nakao
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Takeyuki Watadani
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryusuke Nakaoka
- Division of Medical Devices, National Institute of Health Sciences, Kawasaki, Kanagawa, Japan
| | - Akinobu Shimizu
- Institute of Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| |
Collapse
|
3
|
Milanese G, Silva M, Ledda RE, Iezzi E, Bortolotto C, Mauro LA, Valentini A, Reali L, Bottinelli OM, Ilardi A, Basile A, Palmucci S, Preda L, Sverzellati N. Study rationale and design of the PEOPLHE trial. LA RADIOLOGIA MEDICA 2024; 129:411-419. [PMID: 38319494 PMCID: PMC10943160 DOI: 10.1007/s11547-024-01764-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Accepted: 01/03/2024] [Indexed: 02/07/2024]
Abstract
PURPOSE Lung cancer screening (LCS) by low-dose computed tomography (LDCT) demonstrated a 20-40% reduction in lung cancer mortality. National stakeholders and international scientific societies are increasingly endorsing LCS programs, but translating their benefits into practice is rather challenging. The "Model for Optimized Implementation of Early Lung Cancer Detection: Prospective Evaluation Of Preventive Lung HEalth" (PEOPLHE) is an Italian multicentric LCS program aiming at testing LCS feasibility and implementation within the national healthcare system. PEOPLHE is intended to assess (i) strategies to optimize LCS workflow, (ii) radiological quality assurance, and (iii) the need for dedicated resources, including smoking cessation facilities. METHODS PEOPLHE aims to recruit 1.500 high-risk individuals across three tertiary general hospitals in three different Italian regions that provide comprehensive services to large populations to explore geographic, demographic, and socioeconomic diversities. Screening by LDCT will target current or former (quitting < 10 years) smokers (> 15 cigarettes/day for > 25 years, or > 10 cigarettes/day for > 30 years) aged 50-75 years. Lung nodules will be volumetric measured and classified by a modified PEOPLHE Lung-RADS 1.1 system. Current smokers will be offered smoking cessation support. CONCLUSION The PEOPLHE program will provide information on strategies for screening enrollment and smoking cessation interventions; administrative, organizational, and radiological needs for performing a state-of-the-art LCS; collateral and incidental findings (both pulmonary and extrapulmonary), contributing to the LCS implementation within national healthcare systems.
Collapse
Affiliation(s)
- Gianluca Milanese
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Mario Silva
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy
| | | | - Chandra Bortolotto
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Letizia Antonella Mauro
- Radiology Unit 1, University Hospital Policlinico G. Rodolico-San Marco, Catania, Catania, Italy
| | - Adele Valentini
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Linda Reali
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Olivia Maria Bottinelli
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
| | - Adriana Ilardi
- Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Antonio Basile
- Radiology Unit 1-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Stefano Palmucci
- UOSD I.P.T.R.A.-Department of Medical Surgical Sciences and Advanced Technologies "GF Ingrassia", University of Catania, University Hospital Policlinico G. Rodolico-San Marco, Catania, Italy
| | - Lorenzo Preda
- Diagnostic Imaging Unit, Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
- Radiology Unit-Diagnostic Imaging I, Department of Diagnostic Medicine, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Nicola Sverzellati
- Unit of Radiological Sciences, University Hospital of Parma, University of Parma, Parma, Italy.
| |
Collapse
|
4
|
Hendrix W, Hendrix N, Scholten ET, Mourits M, Trap-de Jong J, Schalekamp S, Korst M, van Leuken M, van Ginneken B, Prokop M, Rutten M, Jacobs C. Deep learning for the detection of benign and malignant pulmonary nodules in non-screening chest CT scans. COMMUNICATIONS MEDICINE 2023; 3:156. [PMID: 37891360 PMCID: PMC10611755 DOI: 10.1038/s43856-023-00388-5] [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: 05/31/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND Outside a screening program, early-stage lung cancer is generally diagnosed after the detection of incidental nodules in clinically ordered chest CT scans. Despite the advances in artificial intelligence (AI) systems for lung cancer detection, clinical validation of these systems is lacking in a non-screening setting. METHOD We developed a deep learning-based AI system and assessed its performance for the detection of actionable benign nodules (requiring follow-up), small lung cancers, and pulmonary metastases in CT scans acquired in two Dutch hospitals (internal and external validation). A panel of five thoracic radiologists labeled all nodules, and two additional radiologists verified the nodule malignancy status and searched for any missed cancers using data from the national Netherlands Cancer Registry. The detection performance was evaluated by measuring the sensitivity at predefined false positive rates on a free receiver operating characteristic curve and was compared with the panel of radiologists. RESULTS On the external test set (100 scans from 100 patients), the sensitivity of the AI system for detecting benign nodules, primary lung cancers, and metastases is respectively 94.3% (82/87, 95% CI: 88.1-98.8%), 96.9% (31/32, 95% CI: 91.7-100%), and 92.0% (104/113, 95% CI: 88.5-95.5%) at a clinically acceptable operating point of 1 false positive per scan (FP/s). These sensitivities are comparable to or higher than the radiologists, albeit with a slightly higher FP/s (average difference of 0.6). CONCLUSIONS The AI system reliably detects benign and malignant pulmonary nodules in clinically indicated CT scans and can potentially assist radiologists in this setting.
Collapse
Affiliation(s)
- Ward Hendrix
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Nils Hendrix
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
- Jheronimus Academy of Data Science, Sint Janssingel 92, 5211 DA, 's-Hertogenbosch, The Netherlands
| | - Ernst T Scholten
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mariëlle Mourits
- Radiology Department, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, The Netherlands
| | - Joline Trap-de Jong
- Radiology Department, St. Antonius Hospital, Koekoekslaan 1, 3435 CM, Nieuwegein, The Netherlands
| | - Steven Schalekamp
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mike Korst
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Maarten van Leuken
- Radiology Department, Canisius Wilhelmina Hospital, Weg door Jonkerbos 100, 6532 SZ, Nijmegen, The Netherlands
| | - Bram van Ginneken
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
| | - Mathias Prokop
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, University Medical Center Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Matthieu Rutten
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands
- Radiology Department, Jeroen Bosch Hospital, Henri Dunantstraat 1, 5223 GZ, 's-Hertogenbosch, The Netherlands
| | - Colin Jacobs
- Diagnostic Imaging Analysis Group, Radiology and Nuclear Medicine Department, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA, Nijmegen, The Netherlands.
| |
Collapse
|
5
|
Min Y, Hu L, Wei L, Nie S. Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac568e] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 02/18/2022] [Indexed: 02/08/2023]
Abstract
Abstract
Computer-aided detection (CADe) technology has been proven to increase the detection rate of pulmonary nodules that has important clinical significance for the early diagnosis of lung cancer. In this study, we systematically review the latest techniques in pulmonary nodule CADe based on deep learning models with convolutional neural networks in computed tomography images. First, the brief descriptions and popular architecture of convolutional neural networks are introduced. Second, several common public databases and evaluation metrics are briefly described. Third, state-of-the-art approaches with excellent performances are selected. Subsequently, we combine the clinical diagnostic process and the traditional four steps of pulmonary nodule CADe into two stages, namely, data preprocessing and image analysis. Further, the major optimizations of deep learning models and algorithms are highlighted according to the progressive evaluation effect of each method, and some clinical evidence is added. Finally, various methods are summarized and compared. The innovative or valuable contributions of each method are expected to guide future research directions. The analyzed results show that deep learning-based methods significantly transformed the detection of pulmonary nodules, and the design of these methods can be inspired by clinical imaging diagnostic procedures. Moreover, focusing on the image analysis stage will result in improved returns. In particular, optimal results can be achieved by optimizing the steps of candidate nodule generation and false positive reduction. End-to-end methods, with greater operating speeds and lower computational consumptions, are superior to other methods in CADe of pulmonary nodules.
Collapse
|
6
|
Wada S, Kamei M, Uehara N, Tsuzaki K, Hamano T. Paraneoplastic Cerebellar Degeneration and Lambert-Eaton Myasthenic Syndrome with SOX-1 Antibodies. Intern Med 2021; 60:1607-1610. [PMID: 33328403 PMCID: PMC8188018 DOI: 10.2169/internalmedicine.5934-20] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
A 69-year-old man was admitted to our hospital for progressive muscle weakness in both lower limbs and limb ataxia (day 0). Nerve conduction studies showed low compound muscle action potential amplitudes at rest and increased amplitudes after maximum voluntary contraction. Blood testing revealed SOX-1 antibodies. He was diagnosed with paraneoplastic cerebellar degeneration and Lambert-Eaton myasthenic syndrome (PCD-LEMS). He died from aspiration pneumonia on day 9. Small-cell lung carcinoma (SCLC), which had not been obvious on computed tomography, was found during the autopsy. Patients with PCD-LEMS who test positive for SOX-1 antibodies should be carefully evaluated for SCLC.
Collapse
Affiliation(s)
- Shinichi Wada
- Department of Neurology, Kansai Electric Power Hospital, Japan
| | - Mayu Kamei
- Department of Neurology, Kansai Electric Power Hospital, Japan
| | - Naoko Uehara
- Department of Neurology, Kansai Electric Power Hospital, Japan
- Division of Clinical Neurology, Kansai Electric Power Medical Research Institute, Japan
| | - Koji Tsuzaki
- Department of Neurology, Kansai Electric Power Hospital, Japan
- Division of Clinical Neurology, Kansai Electric Power Medical Research Institute, Japan
| | - Toshiaki Hamano
- Department of Neurology, Kansai Electric Power Hospital, Japan
- Division of Clinical Neurology, Kansai Electric Power Medical Research Institute, Japan
| |
Collapse
|