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Peters AA, Munz J, Klaus JB, Macek A, Huber AT, Obmann VC, Alsaihati N, Samei E, Valenzuela W, Christe A, Heverhagen JT, Solomon JB, Ebner L. Impact of Simulated Reduced-Dose Chest CT on Diagnosing Pulmonary T1 Tumors and Patient Management. Diagnostics (Basel) 2024; 14:1586. [PMID: 39125461 PMCID: PMC11311729 DOI: 10.3390/diagnostics14151586] [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: 05/28/2024] [Revised: 07/17/2024] [Accepted: 07/18/2024] [Indexed: 08/12/2024] Open
Abstract
To determine the diagnostic performance of simulated reduced-dose chest CT scans regarding pulmonary T1 tumors and assess the potential impact on patient management, a repository of 218 patients with histologically proven pulmonary T1 tumors was used. Virtual reduced-dose images were simulated at 25%- and 5%-dose levels. Tumor size, attenuation, and localization were scored by two experienced chest radiologists. The impact on patient management was assessed by comparing hypothetical LungRADS scores. The study included 210 patients (41% females, mean age 64.5 ± 9.2 years) with 250 eligible T1 tumors. There were differences between the original and the 5%-but not the 25%-dose simulations, and LungRADS scores varied between the dose levels with no clear trend. Sensitivity of Reader 1 was significantly lower using the 5%-dose vs. 25%-dose vs. original dose for size categorization (0.80 vs. 0.85 vs. 0.84; p = 0.007) and segmental localization (0.81 vs. 0.86 vs. 0.83; p = 0.018). Sensitivities of Reader 2 were unaffected by a dose reduction. A CT dose reduction may affect the correct categorization and localization of pulmonary T1 tumors and potentially affect patient management.
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Affiliation(s)
- Alan Arthur Peters
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Jaro Munz
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Jeremias Bendicht Klaus
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Ana Macek
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Adrian Thomas Huber
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Verena Carola Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Njood Alsaihati
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA; (N.A.)
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA; (N.A.)
| | - Waldo Valenzuela
- Department of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, 3012 Bern, Switzerland
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
| | - Johannes Thomas Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
- Department of BioMedical Research, Experimental Radiology, University of Bern, 3012 Bern, Switzerland
- Department of Radiology, The Ohio State University, Columbus, OH 43210, USA
| | - Justin Bennion Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC 27705, USA; (N.A.)
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Rosenbühlgasse 27, 3010 Bern, Switzerland (A.C.)
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Peters AA, Solomon JB, von Stackelberg O, Samei E, Alsaihati N, Valenzuela W, Debic M, Heidt C, Huber AT, Christe A, Heverhagen JT, Kauczor HU, Heussel CP, Ebner L, Wielpütz MO. Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules. Eur Radiol 2024; 34:3444-3452. [PMID: 37870625 PMCID: PMC11126495 DOI: 10.1007/s00330-023-10348-1] [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: 05/08/2023] [Revised: 08/10/2023] [Accepted: 09/03/2023] [Indexed: 10/24/2023]
Abstract
OBJECTIVES The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). METHODS CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach). RESULTS In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. CONCLUSION CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. CLINICAL RELEVANCE STATEMENT Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. KEY POINTS • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.
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Affiliation(s)
- Alan A Peters
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland.
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany.
| | - Justin B Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Oyunbileg von Stackelberg
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Njood Alsaihati
- Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA
| | - Waldo Valenzuela
- University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland
| | - Manuel Debic
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Christian Heidt
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Adrian T Huber
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland
| | - Johannes T Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland
- Department of BioMedical Research, Experimental Radiology, University of Bern, Bern, Switzerland
- Department of Radiology, The Ohio State University, Columbus, OH, USA
| | - Hans-Ulrich Kauczor
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Claus P Heussel
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland
| | - Mark O Wielpütz
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany
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Wu S, Zhong D, Zhao G, Liu Y, Wang Y. Comparison of clinical outcomes between unilateral biportal endoscopic discectomy and percutaneous endoscopic interlaminar discectomy for migrated lumbar disc herniation at lower lumbar spine: a retrospective controlled study. J Orthop Surg Res 2024; 19:21. [PMID: 38167000 PMCID: PMC10763452 DOI: 10.1186/s13018-023-04484-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 12/16/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Both Unilateral Biportal Endoscopic Discectomy (UBED) and Percutaneous Endoscopic Interlaminar Discectomy (PEID) have resulted in favorable clinical outcomes in the management of LDH. The aim of this study is to comprehensively compare the efficacy of UBED and PEID in treating migrated LDH in the lower lumbar spine, with a specific focus on high-grade migrated LDH. METHODS 96 patients who underwent UBED (31 cases) and PEID (65 cases) procedures were enrolled in the study. All patients received a minimum follow-up period of 6 months. Clinical outcomes of the patients were assessed with incision length, operation time, total hemoglobin loss, hospital stay, intraoperative fluoroscopy times, visual analogue scale (VAS) for lower back and leg pain, Oswestry disability index (ODI), modified MacNab criteria, complications, area of lamina loss and increased intervertebral height. RESULTS The VAS scores for lower back and leg pain and ODI significantly decreased in both groups after the operation. Preoperatively, at 1 day, 1 month, and 6 months after the procedure, the VAS and ODI scores exhibited no significant differences between the two groups. There was no significant difference in terms of modified MacNab criteria, area of lamina loss, and increased intervertebral height. The UBED group had a longer incision length, operation time and postoperative hospital stay, and fewer intraoperative fluoroscopy times than to the PEID group. Complications were noted in both groups throughout the follow-up period, but there was no significant difference in the rate of complications. Moreover, there were no notable differences in clinical outcomes between the two groups in the high-grade migrated LDH. CONCLUSIONS Both UBED and PEID could achieve favorable clinical outcomes for treating migrated LDH at the lower lumbar spine. Despite the longer operative time and postoperative hospital stay associated with the UBED group, UBED remains safe and innovative for treating migrated LDH at the lower lumbar spine.
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Affiliation(s)
- Shan Wu
- Department of Spine Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.76, Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Dian Zhong
- Department of Spine Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.76, Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Guosheng Zhao
- Department of Spine Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.76, Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Yang Liu
- Department of Spine Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.76, Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Yang Wang
- Department of Spine Surgery, The Second Affiliated Hospital of Chongqing Medical University, No.76, Linjiang Road, Yuzhong District, Chongqing, 400010, China.
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Peters AA, Christe A, von Stackelberg O, Pohl M, Kauczor HU, Heußel CP, Wielpütz MO, Ebner L. "Will I change nodule management recommendations if I change my CAD system?"-impact of volumetric deviation between different CAD systems on lesion management. Eur Radiol 2023; 33:5568-5577. [PMID: 36894752 PMCID: PMC10326095 DOI: 10.1007/s00330-023-09525-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 11/17/2022] [Accepted: 02/05/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVES To evaluate and compare the measurement accuracy of two different computer-aided diagnosis (CAD) systems regarding artificial pulmonary nodules and assess the clinical impact of volumetric inaccuracies in a phantom study. METHODS In this phantom study, 59 different phantom arrangements with 326 artificial nodules (178 solid, 148 ground-glass) were scanned at 80 kV, 100 kV, and 120 kV. Four different nodule diameters were used: 5 mm, 8 mm, 10 mm, and 12 mm. Scans were analyzed by a deep-learning (DL)-based CAD and a standard CAD system. Relative volumetric errors (RVE) of each system vs. ground truth and the relative volume difference (RVD) DL-based vs. standard CAD were calculated. The Bland-Altman method was used to define the limits of agreement (LOA). The hypothetical impact on LungRADS classification was assessed for both systems. RESULTS There was no difference between the three voltage groups regarding nodule volumetry. Regarding the solid nodules, the RVE of the 5-mm-, 8-mm-, 10-mm-, and 12-mm-size groups for the DL CAD/standard CAD were 12.2/2.8%, 1.3/ - 2.8%, - 3.6/1.5%, and - 12.2/ - 0.3%, respectively. The corresponding values for the ground-glass nodules (GGN) were 25.6%/81.0%, 9.0%/28.0%, 7.6/20.6%, and 6.8/21.2%. The mean RVD for solid nodules/GGN was 1.3/ - 15.2%. Regarding the LungRADS classification, 88.5% and 79.8% of all solid nodules were correctly assigned by the DL CAD and the standard CAD, respectively. 14.9% of the nodules were assigned differently between the systems. CONCLUSIONS Patient management may be affected by the volumetric inaccuracy of the CAD systems and hence demands supervision and/or manual correction by a radiologist. KEY POINTS • The DL-based CAD system was more accurate in the volumetry of GGN and less accurate regarding solid nodules than the standard CAD system. • Nodule size and attenuation have an effect on the measurement accuracy of both systems; tube voltage has no effect on measurement accuracy. • Measurement inaccuracies of CAD systems can have an impact on patient management, which demands supervision by radiologists.
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Affiliation(s)
- Alan A Peters
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany.
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland.
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland
| | - Oyunbileg von Stackelberg
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Moritz Pohl
- Institute of Medical Biometry, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany
| | - Hans-Ulrich Kauczor
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Claus Peter Heußel
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Mark O Wielpütz
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 672, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Lung Research Center (DZL), Marsilius-Arkaden 130, 69120, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology With Nuclear Medicine, University Hospital of Heidelberg, Thoraxklinik Heidelberg, Roentgenstrasse 1, 69126, Heidelberg, Germany
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, 3010, Freiburgstrasse, Switzerland
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Peters AA, Huber AT, Obmann VC, Heverhagen JT, Christe A, Ebner L. Diagnostic validation of a deep learning nodule detection algorithm in low-dose chest CT: determination of optimized dose thresholds in a virtual screening scenario. Eur Radiol 2022; 32:4324-4332. [PMID: 35059804 DOI: 10.1007/s00330-021-08511-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 12/06/2021] [Accepted: 12/09/2021] [Indexed: 12/17/2022]
Abstract
OBJECTIVES This study was conducted to evaluate the effect of dose reduction on the performance of a deep learning (DL)-based computer-aided diagnosis (CAD) system regarding pulmonary nodule detection in a virtual screening scenario. METHODS Sixty-eight anthropomorphic chest phantoms were equipped with 329 nodules (150 ground glass, 179 solid) with four sizes (5 mm, 8 mm, 10 mm, 12 mm) and scanned with nine tube voltage/current combinations. The examinations were analyzed by a commercially available DL-based CAD system. The results were compared by a comparison of proportions. Logistic regression was performed to evaluate the impact of tube voltage, tube current, nodule size, nodule density, and nodule location. RESULTS The combination with the lowest effective dose (E) and unimpaired detection rate was 80 kV/50 mAs (sensitivity: 97.9%, mean false-positive rate (FPR): 1.9, mean CTDIvol: 1.2 ± 0.4 mGy, mean E: 0.66 mSv). Logistic regression revealed that tube voltage and current had the greatest impact on the detection rate, while nodule size and density had no significant influence. CONCLUSIONS The optimal tube voltage/current combination proposed in this study (80 kV/50 mAs) is comparable to the proposed combinations in similar studies, which mostly dealt with conventional CAD software. Modification of tube voltage and tube current has a significant impact on the performance of DL-based CAD software in pulmonary nodule detection regardless of their size and composition. KEY POINTS • Modification of tube voltage and tube current has a significant impact on the performance of deep learning-based CAD software. • Nodule size and composition have no significant impact on the software's performance. • The optimal tube voltage/current combination for the examined software is 80 kV/50 mAs.
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Affiliation(s)
- Alan A Peters
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.
| | - Adrian T Huber
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Verena C Obmann
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Johannes T Heverhagen
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland.,Department of BioMedical Research, Experimental Radiology, University of Bern, 3008, Bern, Switzerland.,Department of Radiology, The Ohio State University, Columbus, OH, USA
| | - Andreas Christe
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
| | - Lukas Ebner
- Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Bern University Hospital, University of Bern, Inselspital Bern, 3010, Switzerland
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Silva M, Milanese G, Ledda RE, Nayak SM, Pastorino U, Sverzellati N. European lung cancer screening: valuable trial evidence for optimal practice implementation. Br J Radiol 2022; 95:20200260. [PMID: 34995141 PMCID: PMC10993986 DOI: 10.1259/bjr.20200260] [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: 03/18/2020] [Revised: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 11/05/2022] Open
Abstract
Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.
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Affiliation(s)
- Mario Silva
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Gianluca Milanese
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Roberta E Ledda
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
| | - Sundeep M Nayak
- Department of Radiology, Kaiser Permanente Northern
California, San Leandro,
California, USA
| | - Ugo Pastorino
- Section of Thoracic Surgery, IRCCS Istituto Nazionale
Tumori, Milano,
Italy
| | - Nicola Sverzellati
- Scienze Radiologiche, Department of Medicine and Surgery
(DiMeC), University of Parma,
Parma, Italy
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7
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Testoni SGG, Petrone MC, Reni M, Rossi G, Barbera M, Nicoletti V, Gusmini S, Balzano G, Linzenbold W, Enderle M, Della-Torre E, De Cobelli F, Doglioni C, Falconi M, Capurso G, Arcidiacono PG. Efficacy of Endoscopic Ultrasound-Guided Ablation with the HybridTherm Probe in Locally Advanced or Borderline Resectable Pancreatic Cancer: A Phase II Randomized Controlled Trial. Cancers (Basel) 2021; 13:4512. [PMID: 34572743 PMCID: PMC8464946 DOI: 10.3390/cancers13184512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/30/2022] Open
Abstract
Endoscopic ultrasound-ablation with HybridTherm-Probe (EUS-HTP) significantly reduces tumour volume (TV) in locally-advanced pancreatic ductal adenocarcinoma (LA-PDAC). We aimed at investigating the clinical efficacy of EUS-HTP plus chemotherapy versus chemotherapy (HTP-CT and CT arms) in LA- and borderline-resectable (BR) PDAC, with 6-months progression-free survival (6-PFS) rate as primary endpoint. In a phase-II randomized-controlled-trial, 33 LA/BR-PDAC patients per-arm were planned to verify 20% improved 6-PFS rate. Radiological response (Choi criteria), TV and serum CA19.9 were assessed up to 6-months. Seventeen and 20 LA/BR-PDAC patients were randomized to HTP-CT or CT. Baseline and CT-related features were balanced. At 6-months, 6-PFS rate was 41.2% and 30% in HTP-CT and CT arms (p = 0.48), respectively. A decrease ≥50% of serum CA19.9 was achieved in 75% and 64.3% of HTP-CT and CT patients (p = 0.53), respectively. TV reduced up to 6-months in 64.3% and 47.1% of HTP-CT and CT patients (p = 0.35), respectively. Resection rate, PFS-time and overall survival (OS-time) were similar. HTP-CT achieves a non-significant 11.2%, 10.7% and 17.2% improved 6-PFS, CA19.9 decrease ≥50% and TV reduction rates over CT, without any impact on resection rate, PFS-time and OS-time. As the study was underpowered, these results suggest further investigation of EUS-local ablation in selected patients with localized disease after induction CT.
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Affiliation(s)
- Sabrina Gloria Giulia Testoni
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (S.G.G.T.); (M.C.P.); (G.R.); (G.C.)
| | - Maria Chiara Petrone
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (S.G.G.T.); (M.C.P.); (G.R.); (G.C.)
| | - Michele Reni
- Pancreas Translational & Clinical Research Center, Oncology Department, San Raffaele Scientific Institute IRCCS, 20132 Milan, Italy;
| | - Gemma Rossi
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (S.G.G.T.); (M.C.P.); (G.R.); (G.C.)
| | - Maurizio Barbera
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (V.N.); (S.G.); (F.D.C.)
| | - Valeria Nicoletti
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (V.N.); (S.G.); (F.D.C.)
| | - Simone Gusmini
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (V.N.); (S.G.); (F.D.C.)
| | - Gianpaolo Balzano
- Pancreas Translational & Clinical Research Center, Pancreatic Surgery Department, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (G.B.); (M.F.)
| | - Walter Linzenbold
- ERBE Research Elektromedizin GmbH, 72072 Tübingen, Germany; (W.L.); (M.E.)
| | - Markus Enderle
- ERBE Research Elektromedizin GmbH, 72072 Tübingen, Germany; (W.L.); (M.E.)
| | - Emanuel Della-Torre
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Francesco De Cobelli
- Pancreas Translational & Clinical Research Center, Department of Radiology & Center for Experimental Imaging, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (M.B.); (V.N.); (S.G.); (F.D.C.)
| | - Claudio Doglioni
- Pancreas Translational & Clinical Research Center, Pathology Department, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Massimo Falconi
- Pancreas Translational & Clinical Research Center, Pancreatic Surgery Department, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (G.B.); (M.F.)
| | - Gabriele Capurso
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (S.G.G.T.); (M.C.P.); (G.R.); (G.C.)
| | - Paolo Giorgio Arcidiacono
- Pancreas Translational & Clinical Research Center, Pancreato-Biliary Endoscopy & Endosonography Division, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, 20132 Milan, Italy; (S.G.G.T.); (M.C.P.); (G.R.); (G.C.)
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Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives. Nat Rev Clin Oncol 2020; 18:135-151. [PMID: 33046839 DOI: 10.1038/s41571-020-00432-6] [Citation(s) in RCA: 221] [Impact Index Per Article: 55.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/04/2020] [Indexed: 12/17/2022]
Abstract
In the past decade, the introduction of molecularly targeted agents and immune-checkpoint inhibitors has led to improved survival outcomes for patients with advanced-stage lung cancer; however, this disease remains the leading cause of cancer-related mortality worldwide. Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening in high-risk populations - the US National Lung Screening Trial (NLST) and NELSON - have provided evidence of a statistically significant mortality reduction in patients. LDCT-based screening programmes for individuals at a high risk of lung cancer have already been implemented in the USA. Furthermore, implementation programmes are currently underway in the UK following the success of the UK Lung Cancer Screening (UKLS) trial, which included the Liverpool Health Lung Project, Manchester Lung Health Check, the Lung Screen Uptake Trial, the West London Lung Cancer Screening pilot and the Yorkshire Lung Screening trial. In this Review, we focus on the current evidence on LDCT-based lung cancer screening and discuss the clinical developments in high-risk populations worldwide; additionally, we address aspects such as cost-effectiveness. We present a framework to define the scope of future implementation research on lung cancer screening programmes referred to as Screening Planning and Implementation RAtionale for Lung cancer (SPIRAL).
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Testoni SGG, Capurso G, Petrone MC, Barbera M, Linzenbold W, Enderle M, Gusmini S, Nicoletti R, Della Torre E, Mariani A, Rossi G, Archibugi L, De Cobelli F, Reni M, Falconi M, Arcidiacono PG. Necrosis volume and Choi criteria predict the response to endoscopic ultrasonography-guided HybridTherm ablation of locally advanced pancreatic cancer. Endosc Int Open 2020; 8:E1511-E1519. [PMID: 33043122 PMCID: PMC7541180 DOI: 10.1055/a-1221-9879] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 06/25/2020] [Indexed: 12/16/2022] Open
Abstract
Background and study aims Endoscopic ultrasound (EUS)-guided ablation of pancreatic ductal adenocarcinoma (PDAC) with HybridTherm-Probe (EUS-HTP) is feasible and safe, but the radiological response and ideal tool to measure it have not been investigated yet. The aims of this study were to: 1) assess the radiological response to EUS-HTP evaluating the vital tumor volume reduction rate, Response Evaluation Criteria in Solid Tumors (RECIST1.1) and Choi criteria; 2) determine the prognostic predictive yield of these criteria. Patients and methods A retrospective analysis was performed of patients with locally advanced PDAC after primary treatment or unfit for chemotherapy prospectively treated by EUS-HTP. Computed tomography scan was performed 1 month after EUS-HTP to evaluate: 1) vital tumor volume reduction rate (VTVRR) by measuring necrosis and tumor volumes through a computer-aided detection system; and 2) RECIST1.1 and Choi criteria. Results EUS-HTP was feasible in 22 of 31 patients (71 %), with no severe adverse events. Median post-HTP survival was 7 months (1 - 35). Compared to pre-HTP tumor volume, a significant 1-month VTVRR (mean 21.4 %) was observed after EUS-HTP ( P = 0.005). We identified through ROC analysis a VTVRR > 11.46 % as the best cut-off to determine post-HTP 6-month survival outcome (AUC = 0.733; sensitivity = 70.0 %, specificity = 83.3 %). This cut-off was significantly associated with longer overall survival (HR = 0.372; P = 0.039). According to RECIST1.1 and Choi criteria, good responders to EUS-HTP were 60 % and 46.7 %, respectively. Good responders according to Choi, but not to RECIST1.1, had longer survival (HR = 0.407; P = 0.04). Conclusions EUS-HTP induces a significant 1-month VTVRR. This effect is assessed accurately by evaluation of necrosis and tumor volumes. Use of VTVRR and Choi criteria, but not RECIST 1.1 criteria, might identify patients who could benefit clinically from EUS-HTP.
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Affiliation(s)
- Sabrina Gloria Giulia Testoni
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Gabriele Capurso
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Maria Chiara Petrone
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Maurizio Barbera
- Department of Radiology & Center for Experimental Imaging, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | | | | | - Simone Gusmini
- Department of Radiology & Center for Experimental Imaging, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Roberto Nicoletti
- Department of Radiology & Center for Experimental Imaging, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Emanuel Della Torre
- Unit of Immunology, Rheumatology, Allergy and Rare Diseases. Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Alberto Mariani
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Gemma Rossi
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Livia Archibugi
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Francesco De Cobelli
- Department of Radiology & Center for Experimental Imaging, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Michele Reni
- Oncology Department, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Falconi
- Pancreatic Surgery Department, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
| | - Paolo Giorgio Arcidiacono
- Pancreatico-Biliary Endoscopy & Endosonography Division, Pancreas Translational & Clinical Research Center, San Raffaele Scientific Institute IRCCS, Vita-Salute San Raffaele University, Milan, Italy
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The Use of Artificial Intelligence in the Differentiation of Malignant and Benign Lung Nodules on Computed Tomograms Proven by Surgical Pathology. Cancers (Basel) 2020; 12:cancers12082211. [PMID: 32784681 PMCID: PMC7464412 DOI: 10.3390/cancers12082211] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 08/04/2020] [Accepted: 08/05/2020] [Indexed: 01/23/2023] Open
Abstract
The purpose of this work was to evaluate the performance of an existing commercially available artificial intelligence (AI) software system in differentiating malignant and benign lung nodules. The AI tool consisted of a vessel-suppression function and a deep-learning-based computer-aided-detection (VS-CAD) analyzer. Fifty patients (32 females, mean age 52 years) with 75 lung nodules (47 malignant and 28 benign) underwent low-dose computed tomography (LDCT) followed by surgical excision and the pathological analysis of their 75 nodules within a 3 month time frame. All 50 cases were then processed by the AI software to generate corresponding VS images and CAD outcomes. All 75 pathologically proven lung nodules were well delineated by vessel-suppressed images. Three (6.4%) of the 47 lung cancer cases, and 11 (39.3%) of the 28 benign nodules were ignored and not detected by the AI without showing a CAD analysis summary. The AI system/radiologists produced a sensitivity and specificity (shown in %) of 93.6/89.4 and 39.3/82.1 in distinguishing malignant from benign nodules, respectively. AI sensitivity was higher than that of radiologists, though not statistically significant (p = 0.712). Specificity obtained by the radiologists was significantly higher than that of the VS-CAD AI (p = 0.003). There was no significant difference between the malignant and benign lesions with respect to age, gender, pure ground-glass pattern, the diameter and location of the nodules, or nodules <6 vs. ≥6 mm. However, more part-solid nodules were proven to be malignant than benign (90.9% vs. 9.1%), and more solid nodules were proven to be benign than malignant (86.7% vs. 13.3%) with statistical significance (p = 0.001 and <0.001, respectively). A larger cohort and prospective study are required to validate the AI performance.
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Effect of Slab Thickness on the Detection of Pulmonary Nodules by Use of CT Maximum and Minimum Intensity Projection. AJR Am J Roentgenol 2019; 213:562-567. [PMID: 31063429 DOI: 10.2214/ajr.19.21325] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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Liu X, Zhou H, Hu Z, Jin Q, Wang J, Ye B. [Clinical Application of Artificial Intelligence Recognition Technology
in the Diagnosis of Stage T1 Lung Cancer]. ZHONGGUO FEI AI ZA ZHI = CHINESE JOURNAL OF LUNG CANCER 2019; 22:319-323. [PMID: 31109442 PMCID: PMC6533191 DOI: 10.3779/j.issn.1009-3419.2019.05.09] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
BACKGROUND Lung cancer is the cancer with the highest morbidity and mortality at home and abroad at present. Using computed tomography (CT) to screen lung cancer nodules is a huge workload. To test the effect of artificial intelligence in automatic identification of lung cancer by using artificial intelligence to find the lung cancer nodules automatically in the chest CT of 1 mm and 5 mm thick. METHODS 5,000 cases of T1 stage lung cancer patients with 1 mm and 5 mm layer thickness were respectively labeled and learned by computer neural network, the algorithm of forming pulmonary nodules was carried out. 500 cases of chest CT in T1 stage lung cancer patients with 1 mm and 5 mm thickness were tested by artificial intelligence formation, and the sensitivity and specificity were compared with artificial reading. RESULTS Using artificial intelligence to read chest CT 500 in 5 mm, the sensitivity was 95.20%, the specificity was 93.20%, and the Kappa value of two times repeated read was 0.926,1. For 1 mm chest CT 500 cases, the sensitivity is 96.40%, the specificity is 95.60%, and the Kappa reads two times is 0.938,6. Compared with 5 doctors, the same CT sets with 1 mm thickness were read. The detection rates of artificial intelligence and artificial reading were similar to those of lung cancer nodules and negative control read films, and there was no significant difference between them. In the comparison of the same CT slices with 5 mm thickness, the number of detection of lung cancer nodules by artificial intelligence is better than that of artificial reading, and the sensitivity is higher, but the number of false messages is increased and the specificity is slightly worse. CONCLUSIONS The automatic learning of early lung cancer chest CT images by artificial intelligence can achieve high sensitivity and specificity of early lung cancer identification, and assist doctors in the diagnosis of lung cancer.
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Affiliation(s)
- Xiaopeng Liu
- Department of Respiratory Disease, Jinshan Hospital of Fudan University, Shanghai 201508, China
| | - Haiying Zhou
- Department of Respiratory Disease, Jinshan Hospital of Fudan University, Shanghai 201508, China
| | - Zhixiong Hu
- Department of Respiratory Disease, Jinshan Hospital of Fudan University, Shanghai 201508, China
| | - Quan Jin
- Department of Respiratory Disease, Jinshan Hospital of Fudan University, Shanghai 201508, China
| | - Jing Wang
- Department of Respiratory Disease, Jinshan Hospital of Fudan University, Shanghai 201508, China
| | - Bo Ye
- Department of Thoracic Surgery, Thoracic Hospital Affiliated to Shanghai Jiaotong University, Shanghai 200030, China
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The Added Value of Computer-aided Detection of Small Pulmonary Nodules and Missed Lung Cancers. J Thorac Imaging 2019; 33:390-395. [PMID: 30239461 DOI: 10.1097/rti.0000000000000362] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Lung cancer at its earliest stage is typically manifested on computed tomography as a pulmonary nodule, which could be detected by low-dose multidetector computed tomography technology and the use of thinner collimation. Within the last 2 decades, computer-aided detection (CAD) of pulmonary nodules has been developed to meet the increasing demand for lung cancer screening computed tomography with a larger set of images per scan. This review introduced the basic techniques and then summarized the up-to-date applications of CAD systems in clinical and research programs and in the low-dose lung cancer screening trials, especially in the detection of small pulmonary nodules and missed lung cancers. Many studies have already shown that the CAD systems could increase the sensitivity and reduce the false-positive rate in the diagnosis of pulmonary nodules, especially for the small and isolated nodules. Further improvements to the current CAD schemes are needed to detect nodules accurately, particularly for subsolid nodules.
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Robins M, Solomon J, Koweek LMH, Christensen J, Samei E. Validation of lesion simulations in clinical CT data for anonymized chest and abdominal CT databases. Med Phys 2019; 46:1931-1937. [PMID: 30703259 DOI: 10.1002/mp.13412] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2018] [Revised: 12/04/2018] [Accepted: 01/18/2019] [Indexed: 11/11/2022] Open
Abstract
PURPOSE To make available to the medical imaging community a computed tomography (CT) image database composed of hybrid datasets (patient CT images with digitally inserted anthropomorphic lesions) where lesion ground truth is known a priori. It is envisioned that such a dataset could be a resource for the assessment of CT image quality, machine learning, and imaging technologies [e.g., computer aided detection (CAD) and segmentation algorithms]. ACQUISITION AND VALIDATION METHODS This HIPPA compliant, IRB waiver of approval study consisted of utilizing 120 chest and 100 abdominal clinically acquired adult CT exams. One image series per patient exam was utilized based on coverage of the anatomical region of interest (either the thorax or abdomen). All image series were de-identified. Simulated lesions were derived from a library of anatomically informed digital lesions (93 lung and 50 liver lesions) where six and four digital lesions with nominal diameters ranging from 4 to 20 mm were inserted into lung and liver image series, respectively. Locations for lesion insertion were randomly chosen. A previously validated lesion simulation and virtual insertion technique were utilized. The resulting hybrid images were reviewed by three experienced radiologists to assure similarity with routine clinical imaging in a diverse adult population. DATA FORMAT AND USAGE NOTES The database is composed of four datasets that contain 100 patient cases each, for a total of 400 image series accompanied by Matlab.mat tables that provide descriptive information about the virtually inserted lesions (i.e., size, shape, opacity, and insertion location in physical (world) coordinates and voxel indices). All image and metadata are stored in DICOM format on the Quantitative Imaging Data Warehouse (https://qidw.rsna.org/#collection/57d463471cac0a4ec8ff8f46/folder/5b23dceb1cac0a4ec800a770?dialog=login), in two sets: (a) QIBA CT Hybrid Dataset I which contains Lung I and Liver I datasets, and (b) QIBA CT Hybrid Dataset II which contains Lung II and Liver II datasets. The QIDW is supported by the Radiological Society of North America (RSNA). Registration is required upon initial log in. POTENTIAL APPLICATIONS By simulating lesion opacity (full solid, part solid and ground glass), size, and texture, the relationship between lesion morphology and segmentation or CAD algorithm performance can be investigated without the need for repetitive patient exams. This database can also serve as a reference standard for device and reader performance studies.
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Affiliation(s)
- Marthony Robins
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Justin Solomon
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Lynne M Hurwitz Koweek
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA
| | - Jared Christensen
- Department of Radiology, Duke University Medical Center, Durham, NC, 27705, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Medical Physics Graduate Program, Duke University Medical Center, Durham, NC, 27705, USA.,Departments of Biomedical Engineering, Electrical and Computer Engineering, and Physics, Duke University Medical Center, Durham, NC, 27705, USA
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Vlahos I, Stefanidis K, Sheard S, Nair A, Sayer C, Moser J. Lung cancer screening: nodule identification and characterization. Transl Lung Cancer Res 2018; 7:288-303. [PMID: 30050767 DOI: 10.21037/tlcr.2018.05.02] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The accurate identification and characterization of small pulmonary nodules at low-dose CT is an essential requirement for the implementation of effective lung cancer screening. Individual reader detection performance is influenced by nodule characteristics and technical CT parameters but can be improved by training, the application of CT techniques, and by computer-aided techniques. However, the evaluation of nodule detection in lung cancer screening trials differs from the assessment of individual readers as it incorporates multiple readers, their inter-observer variability, reporting thresholds, and reflects the program accuracy in identifying lung cancer. Understanding detection and interpretation errors in screening trials aids in the implementation of lung cancer screening in clinical practice. Indeed, as CT screening moves to ever lower radiation doses, radiologists must be cognisant of new technical challenges in nodule assessment. Screen detected lung cancers demonstrate distinct morphological features from incidentally or symptomatically detected lung cancers. Hence characterization of screen detected nodules requires an awareness of emerging concepts in early lung cancer appearances and their impact on radiological assessment and malignancy prediction models. Ultimately many nodules remain indeterminate, but further imaging evaluation can be appropriate with judicious utilization of contrast enhanced CT or MRI techniques or functional evaluation by PET-CT.
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Affiliation(s)
- Ioannis Vlahos
- St George's NHS Foundation Hospitals Trust and School of Medicine, London, UK
| | | | | | - Arjun Nair
- Guy's and St Thomas' Hospital NHS Foundation Trust, London, UK
| | - Charles Sayer
- Brighton and Sussex University Hospitals Trust, Haywards Heath, UK
| | - Joanne Moser
- St George's NHS Foundation Hospitals Trust and School of Medicine, London, UK
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Johnson PT, Fishman EK. Enhancing Image Quality in the Era of Radiation Dose Reduction: Postprocessing Techniques for Body CT. J Am Coll Radiol 2018; 15:486-488. [DOI: 10.1016/j.jacr.2017.11.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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17
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Fintelmann FJ, Gottumukkala RV, McDermott S, Gilman MD, Lennes IT, Shepard JAO. Lung Cancer Screening. Radiol Clin North Am 2017; 55:1163-1181. [DOI: 10.1016/j.rcl.2017.06.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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18
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Nomura Y, Higaki T, Fujita M, Miki S, Awaya Y, Nakanishi T, Yoshikawa T, Hayashi N, Awai K. Effects of Iterative Reconstruction Algorithms on Computer-assisted Detection (CAD) Software for Lung Nodules in Ultra-low-dose CT for Lung Cancer Screening. Acad Radiol 2017; 24:124-130. [PMID: 27986507 DOI: 10.1016/j.acra.2016.09.023] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2016] [Revised: 09/21/2016] [Accepted: 09/25/2016] [Indexed: 02/06/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to evaluate the effects of iterative reconstruction (IR) algorithms on computer-assisted detection (CAD) software for lung nodules in ultra-low-dose computed tomography (ULD-CT) for lung cancer screening. MATERIALS AND METHODS We selected 85 subjects who underwent both a low-dose CT (LD-CT) scan and an additional ULD-CT scan in our lung cancer screening program for high-risk populations. The LD-CT scans were reconstructed with filtered back projection (FBP; LD-FBP). The ULD-CT scans were reconstructed with FBP (ULD-FBP), adaptive iterative dose reduction 3D (AIDR 3D; ULD-AIDR 3D), and forward projected model-based IR solution (FIRST; ULD-FIRST). CAD software for lung nodules was applied to each image dataset, and the performance of the CAD software was compared among the different IR algorithms. RESULTS The mean volume CT dose indexes were 3.02 mGy (LD-CT) and 0.30 mGy (ULD-CT). For overall nodules, the sensitivities of CAD software at 3.0 false positives per case were 78.7% (LD-FBP), 9.3% (ULD-FBP), 69.4% (ULD-AIDR 3D), and 77.8% (ULD-FIRST). Statistical analysis showed that the sensitivities of ULD-AIDR 3D and ULD-FIRST were significantly higher than that of ULD-FBP (P < .001). CONCLUSIONS The performance of CAD software in ULD-CT was improved by using IR algorithms. In particular, the performance of CAD in ULD-FIRST was almost equivalent to that in LD-FBP.
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Zhou Z, Zhan P, Jin J, Liu Y, Li Q, Ma C, Miao Y, Zhu Q, Tian P, Lv T, Song Y. The imaging of small pulmonary nodules. Transl Lung Cancer Res 2017; 6:62-67. [PMID: 28331825 DOI: 10.21037/tlcr.2017.02.02] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Lung cancer is the leading cause of cancer death worldwide. The major goal in lung cancer research is the improvement of long-term survival. Pulmonary nodules have high clinical importance, they may not only prove to be an early manifestation of lung cancer, but decide to choose the right therapy. This review will introduce the development and current situation of several imaging examination methods: computed tomography (CT), positron emission tomography/computed tomography (PET/CT), endobronchial ultrasound (EBUS).
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Affiliation(s)
- Zejun Zhou
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Ping Zhan
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Jiajia Jin
- Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China
| | - Yafang Liu
- Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
| | - Qian Li
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Chenhui Ma
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Yingying Miao
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Qingqing Zhu
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China
| | - Panwen Tian
- Department of Respiratory and Critical Care Medicine, Lung Cancer Treatment Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Tangfeng Lv
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
| | - Yong Song
- Department of Respiratory Medicine, Jinling Hospital, Nanjing University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southeast University School of Medicine, Nanjing 210002, China;; Department of Respiratory Medicine, Jinling Hospital, Southern Medical University, Nanjing 210002, China
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Reply to "Comment on 'Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels'". AJR Am J Roentgenol 2017; 208:W133. [PMID: 28095021 DOI: 10.2214/ajr.16.17674] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Comment on "Maximum-Intensity-Projection and Computer-Aided-Detection Algorithms as Stand-Alone Reader Devices in Lung Cancer Screening Using Different Dose Levels and Reconstruction Kernels". AJR Am J Roentgenol 2016; 208:W132. [PMID: 28004981 DOI: 10.2214/ajr.16.17392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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