1
|
Ito H, Yoshizawa A, Terada K, Nakakura A, Rokutan-Kurata M, Sugimoto T, Nishimura K, Nakajima N, Sumiyoshi S, Hamaji M, Menju T, Date H, Morita S, Bise R, Haga H. A Deep Learning-Based Assay for Programmed Death Ligand 1 Immunohistochemistry Scoring in Non-Small Cell Lung Carcinoma: Does it Help Pathologists Score? Mod Pathol 2024; 37:100485. [PMID: 38588885 DOI: 10.1016/j.modpat.2024.100485] [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: 08/10/2023] [Revised: 02/08/2024] [Accepted: 04/01/2024] [Indexed: 04/10/2024]
Abstract
Several studies have developed various artificial intelligence (AI) models for immunohistochemical analysis of programmed death ligand 1 (PD-L1) in patients with non-small cell lung carcinoma; however, none have focused on specific ways by which AI-assisted systems could help pathologists determine the tumor proportion score (TPS). In this study, we developed an AI model to calculate the TPS of the PD-L1 22C3 assay and evaluated whether and how this AI-assisted system could help pathologists determine the TPS and analyze how AI-assisted systems could affect pathologists' assessment accuracy. We assessed the 4 methods of the AI-assisted system: (1 and 2) pathologists first assessed and then referred to automated AI scoring results (1, positive tumor cell percentage; 2, positive tumor cell percentage and visualized overlay image) for final confirmation, and (3 and 4) pathologists referred to the automated AI scoring results (3, positive tumor cell percentage; 4, positive tumor cell percentage and visualized overlay image) while determining TPS. Mixed-model analysis was used to calculate the odds ratios (ORs) with 95% CI for AI-assisted TPS methods 1 to 4 compared with pathologists' scoring. For all 584 samples of the tissue microarray, the OR for AI-assisted TPS methods 1 to 4 was 0.94 to 1.07 and not statistically significant. Of them, we found 332 discordant cases, on which the pathologists' judgments were inconsistent; the ORs for AI-assisted TPS methods 1, 2, 3, and 4 were 1.28 (1.06-1.54; P = .012), 1.29 (1.06-1.55; P = .010), 1.28 (1.06-1.54; P = .012), and 1.29 (1.06-1.55; P = .010), respectively, which were statistically significant. For discordant cases, the OR for each AI-assisted TPS method compared with the others was 0.99 to 1.01 and not statistically significant. This study emphasized the usefulness of the AI-assisted system for cases in which pathologists had difficulty determining the PD-L1 TPS.
Collapse
Affiliation(s)
- Hiroaki Ito
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Akihiko Yoshizawa
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Nara Medical University, Nara, Japan.
| | - Kazuhiro Terada
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| | - Akiyoshi Nakakura
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | | | - Tatsuhiko Sugimoto
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Kazuya Nishimura
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Naoki Nakajima
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Toyooka Hospital, Hyogo, Japan
| | - Shinji Sumiyoshi
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan; Department of Diagnostic Pathology, Tenri Hospital, Nara, Japan
| | - Masatsugu Hamaji
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Toshi Menju
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Hiroshi Date
- Department of Thoracic Surgery, Kyoto University Hospital, Kyoto, Japan
| | - Satoshi Morita
- Department of Biomedical Statistics and Bioinformatics, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Ryoma Bise
- Department of Advanced Information Technology, Kyushu University, Fukuoka, Japan
| | - Hironori Haga
- Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan
| |
Collapse
|
2
|
Chao HS, Tsai CY, Chou CW, Shiao TH, Huang HC, Chen KC, Tsai HH, Lin CY, Chen YM. Artificial Intelligence Assisted Computational Tomographic Detection of Lung Nodules for Prognostic Cancer Examination: A Large-Scale Clinical Trial. Biomedicines 2023; 11:biomedicines11010147. [PMID: 36672655 PMCID: PMC9856020 DOI: 10.3390/biomedicines11010147] [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: 11/19/2022] [Revised: 12/23/2022] [Accepted: 12/29/2022] [Indexed: 01/11/2023] Open
Abstract
Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.
Collapse
Affiliation(s)
- Heng-Sheng Chao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Chiao-Yun Tsai
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chung-Wei Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Tsu-Hui Shiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Hsu-Chih Huang
- Division of Thoracic Surgery, Department of Surgery, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Kun-Chieh Chen
- Division of Pulmonary Medicine, Department of Internal Medicine, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Applied Chemistry, National Chi Nan University, Nantou 545301, Taiwan
| | - Hao-Hung Tsai
- Institute of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
- Department of Medical Imaging, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- School of Medicine, College of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan
| | - Chin-Yu Lin
- Institute of New Drug Development, College of Medicine, China Medical University, Taichung 40402, Taiwan
- Tsuzuki Institute for Traditional Medicine, College of Pharmacy, China Medical University, Taichung 40402, Taiwan
- Department for Biomedical Engineering, Collage of Biomedical Engineering, China Medical University, Taichung 40402, Taiwan
| | - Yuh-Min Chen
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Correspondence: ; Tel.: +886-2-28712121 (ext. 7865)
| |
Collapse
|
3
|
Hsu HH, Ko KH, Chou YC, Wu YC, Chiu SH, Chang CK, Chang WC. Performance and reading time of lung nodule identification on multidetector CT with or without an artificial intelligence-powered computer-aided detection system. Clin Radiol 2021; 76:626.e23-626.e32. [PMID: 34023068 DOI: 10.1016/j.crad.2021.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 04/15/2021] [Indexed: 10/21/2022]
Abstract
AIM To compare the performance and reading time of different readers using automatic artificial intelligence (AI)-powered computer-aided detection (CAD) to detect lung nodules in different reading modes. MATERIALS AND METHODS One hundred and fifty multidetector computed tomography (CT) datasets containing 340 nodules ≤10 mm in diameter were collected retrospectively. A CAD with vessel-suppressed function was used to interpret the images. Three junior and three senior readers were assigned to read (1) CT images without CAD, (2) second-read using CAD in which CAD was applied only after initial unassisted assessment, and (3) a concurrent read with CAD in which CAD was applied at the start of assessment. Diagnostic performances and reading times were compared using analysis of variance. RESULTS For all readers, the mean sensitivity improved from 64% (95% confidence interval [CI]: 62%, 66%) for the without-CAD mode to 82% (95% CI: 80%, 84%) for the second-reading mode and to 80% (95% CI: 79%, 82%) for the concurrent-reading mode (p<0.001). There was no significant difference between the two modes in terms of the mean sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) for both junior and senior readers and all readers (p>0.05). The reading time of all readers was significantly shorter for the concurrent-reading mode (124 ± 25 seconds) compared to without CAD (156 ± 34 seconds; p<0.001) and the second-reading mode (197 ± 46 seconds; p<0.001). CONCLUSION In CAD for lung nodules at CT, the second-reading mode and concurrent-reading mode may improve detection performance for all readers in both screening and clinical routine practice. Concurrent use of CAD is more efficient for both junior and senior readers.
Collapse
Affiliation(s)
- H-H Hsu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan.
| | - K-H Ko
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Chou
- School of Public Health, National Defense Medical Center, Taipei, Taiwan
| | - Y-C Wu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - S-H Chiu
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - C-K Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - W-C Chang
- Department of Radiology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| |
Collapse
|
4
|
Takaishi T, Ozawa Y, Bando Y, Yamamoto A, Okochi S, Suzuki H, Shibamoto Y. Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings. Jpn J Radiol 2020; 39:159-164. [PMID: 32940850 DOI: 10.1007/s11604-020-01043-y] [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: 07/30/2020] [Accepted: 09/10/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. MATERIALS AND METHODS We used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4 mm in diameter. After an interval of at least 15 days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time. RESULTS The average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11). CONCLUSION Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.
Collapse
Affiliation(s)
- Taku Takaishi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.
| | - Yoshiyuki Ozawa
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Yuya Bando
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Akiko Yamamoto
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Sachiko Okochi
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Hirochika Suzuki
- Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan
| | - Yuta Shibamoto
- Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| |
Collapse
|
5
|
JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function. AJR Am J Roentgenol 2018; 210:480-488. [PMID: 29336601 DOI: 10.2214/ajr.17.18718] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
OBJECTIVE The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system. MATERIALS AND METHODS A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis. RESULTS In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p < 0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%. CONCLUSION The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.
Collapse
|
6
|
Del Ciello A, Franchi P, Contegiacomo A, Cicchetti G, Bonomo L, Larici AR. Missed lung cancer: when, where, and why? Diagn Interv Radiol 2017; 23:118-126. [PMID: 28206951 DOI: 10.5152/dir.2016.16187] [Citation(s) in RCA: 85] [Impact Index Per Article: 12.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Missed lung cancer is a source of concern among radiologists and an important medicolegal challenge. In 90% of the cases, errors in diagnosis of lung cancer occur on chest radiographs. It may be challenging for radiologists to distinguish a lung lesion from bones, pulmonary vessels, mediastinal structures, and other complex anatomical structures on chest radiographs. Nevertheless, lung cancer can also be overlooked on computed tomography (CT) scans, regardless of the context, either if a clinical or radiologic suspect exists or for other reasons. Awareness of the possible causes of overlooking a pulmonary lesion can give radiologists a chance to reduce the occurrence of this eventuality. Various factors contribute to a misdiagnosis of lung cancer on chest radiographs and on CT, often very similar in nature to each other. Observer error is the most significant one and comprises scanning error, recognition error, decision-making error, and satisfaction of search. Tumor characteristics such as lesion size, conspicuity, and location are also crucial in this context. Even technical aspects can contribute to the probability of skipping lung cancer, including image quality and patient positioning and movement. Albeit it is hard to remove missed lung cancer completely, strategies to reduce observer error and methods to improve technique and automated detection may be valuable in reducing its likelihood.
Collapse
Affiliation(s)
- Annemilia Del Ciello
- Institute of Radiology, Department of Radiological Sciences, Università Cattolica del Sacro Cuore, Largo Agostino Gemelli 8, Rome, Italy.
| | | | | | | | | | | |
Collapse
|
7
|
Nair A, Screaton NJ, Holemans JA, Jones D, Clements L, Barton B, Gartland N, Duffy SW, Baldwin DR, Field JK, Hansell DM, Devaraj A. The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial. Eur Radiol 2017. [PMID: 28643093 PMCID: PMC5717117 DOI: 10.1007/s00330-017-4903-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Objectives To compare radiologists’ performance reading CTs independently with their performance using radiographers as concurrent readers in lung cancer screening. Methods 369 consecutive baseline CTs performed for the UK Lung Cancer Screening (UKLS) trial were double-read by radiologists reading either independently or concurrently with a radiographer. In concurrent reading, the radiologist reviewed radiographer-identified nodules and then detected any additional nodules. Radiologists recorded their independent and concurrent reading times. For each radiologist, sensitivity, average false-positive detections (FPs) per case and mean reading times for each method were calculated. Results 694 nodules in 246/369 (66.7%) studies comprised the reference standard. Radiologists’ mean sensitivity and average FPs per case both increased with concurrent reading compared to independent reading (90.8 ± 5.6% vs. 77.5 ± 11.2%, and 0.60 ± 0.53 vs. 0.33 ± 0.20, respectively; p < 0.05 for 3/4 and 2/4 radiologists, respectively). The mean reading times per case decreased from 9.1 ± 2.3 min with independent reading to 7.2 ± 1.0 min with concurrent reading, decreasing significantly for 3/4 radiologists (p < 0.05). Conclusions The majority of radiologists demonstrated improved sensitivity, a small increase in FP detections and a statistically significantly reduced reading time using radiographers as concurrent readers. Key Points • Radiographers as concurrent readers could improve radiologists’ sensitivity in lung nodule detection. • An increase in false-positive detections with radiographer-assisted concurrent reading occurred. • The false-positive detection rate was still lower than reported for computer-aided detection. • Concurrent reading with radiographers was also faster than single reading. • The time saved per case using concurrently reading radiographers was relatively modest. Electronic supplementary material The online version of this article (doi:10.1007/s00330-017-4903-z) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Arjun Nair
- Department of Radiology, Guy's and St Thomas' NHS Foundation Trust, Great Maze Pond, London, SE1 9RT, UK.
| | - Nicholas J Screaton
- Department of Radiology, Papworth Hospital NHS Foundation Trust, Papworth Everard, Cambridge, CB23 3RE, UK
| | - John A Holemans
- Department of Radiology, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, Merseyside, L14 3PE, UK
| | - Diane Jones
- Department of Radiology, Liverpool Heart and Chest Hospital, Thomas Drive, Liverpool, Merseyside, L14 3PE, UK
| | - Leigh Clements
- Department of Radiology, Papworth Hospital NHS Foundation Trust, Papworth Everard, Cambridge, CB23 3RE, UK
| | - Bruce Barton
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Natalie Gartland
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Stephen W Duffy
- Centre for Cancer Prevention, Wolfson Institute of Preventive Medicine, Barts and The London School of Medicine and Dentistry, Charterhouse Square, London, EC1M 6BQ, UK
| | - David R Baldwin
- Respiratory Medicine Unit, David Evans Research Centre, Nottingham University Hospitals, Nottingham, NG5 1PB, UK
| | - John K Field
- Roy Castle Lung Cancer Research Programme, Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, The University of Liverpool, The William Duncan Building, 6 West Derby Street, L7 8TX, Liverpool, UK
| | - David M Hansell
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| | - Anand Devaraj
- Department of Radiology, Royal Brompton Hospital, Sydney Street, London, SW3 6NP, UK
| |
Collapse
|
8
|
Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: A comparative study. Eur J Radiol 2013; 82:1332-7. [DOI: 10.1016/j.ejrad.2013.02.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2012] [Revised: 02/02/2013] [Accepted: 02/04/2013] [Indexed: 11/18/2022]
|
9
|
Detection of noncalcified pulmonary nodules on low-dose MDCT: comparison of the sensitivity of two CAD systems by using a double reference standard. Radiol Med 2012; 117:953-67. [DOI: 10.1007/s11547-012-0795-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2011] [Accepted: 06/06/2011] [Indexed: 10/14/2022]
|