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Nuijens BW, Lindeboom R, van den Broek JJ, Geenen RWF, Schreurs WH. A prediction model for lung metastases in patients with indeterminate pulmonary nodules in newly diagnosed colorectal cancer. EUROPEAN JOURNAL OF SURGICAL ONCOLOGY 2024; 50:108305. [PMID: 38552417 DOI: 10.1016/j.ejso.2024.108305] [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: 12/18/2023] [Revised: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 05/26/2024]
Abstract
INTRODUCTION Multidisciplinary teams treating patients with newly diagnosed Colorectal Cancer (CRC) often encounter the appearance of Indeterminate Pulmonary Nodules (IPNs) that warrants follow-up with repetitive medical imaging and anxiety for patients. We determined the incidence of IPNs in patients with newly diagnosed CRC and developed and validated a model for individualized risk prediction of IPNs being lung metastases. MATERIAL AND METHODS Newly diagnosed CRC who underwent surgery between November 2011 to June 2014 were included to create the risk model, developed using both clinical experience and statistical selection. Discrimination and calibration slopes of the risk score were evaluated in an independent temporal validation sample. A nomogram is presented to assist clinicians in estimating an individual risk score. RESULTS Out of 2111 CRC patients staged with chest CT, 204 (9.6%) had IPNs and 54/204 (26%) had lung metastases. We identified 4 predictors: "location of primary tumour", "pathological nodal stage", "size of the largest nodule" and "extrapulmonary synchronous metastases at diagnosis". Discrimination of the final model in the validation sample was demonstrated by the difference in mean predicted risk between progressed cases en non-progressed cases (49% versus 21%, p = <0.001). CONCLUSION A prediction model with 4 clinical risk factors can be used to assist multidisciplinary teams in the prediction of individualized risk of lung metastases and imaging strategy in patients with IPNs and newly diagnosed colorectal cancer. The model performed well in new patients not included in the model development.
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Affiliation(s)
| | - Robert Lindeboom
- Department of Epidemiology and Data Science, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Remy W F Geenen
- Department of Radiology, Northwest Clinics, Alkmaar, the Netherlands
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Hassan MS, Ariyaratne S, Azzopardi C, Iyengar KP, Davies AM, Botchu R. The clinical significance of indeterminate pulmonary nodules in patients with primary bone sarcoma: a systematic review. Br J Radiol 2024; 97:747-756. [PMID: 38346703 PMCID: PMC11027319 DOI: 10.1093/bjr/tqae040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/14/2023] [Accepted: 02/08/2024] [Indexed: 04/02/2024] Open
Abstract
OBJECTIVE To report the incidence of indeterminate pulmonary nodules (IPN) and the rate of progression of IPNs to metastasis in patients with primary bone cancers. We also aimed to evaluate clinical or radiological parameters that may identify IPNs more likely to progress to metastatic disease and their effect on overall or event-free survival in patients with primary bone sarcoma. METHODS A systematic search of the electronic databases Medline, Embase, and Cochrane Library was undertaken for eligible articles on IPNs in patients with primary bone sarcomas, published in the English language from inception of the databases to 2023. The Newcastle-Ottawa Quality Assessment Form for Cohort Studies was utilized to evaluate risk of bias in included studies. RESULTS Six studies, involving 1667 patients, were included in this systematic review. Pooled quantitative analysis found the rate of incidence of IPN to be 18.1% (302 out of 1667) and the rate of progression to metastasis to be 45.0% (136 out of 302). Nodule size (more than 5 mm diameter), number (more than or equal to 4), distribution (bilaterally distributed), incomplete calcification, and lobulated margins were associated with an increased likelihood of IPNs progressing to metastasis, however, their impact on overall or event-free survival remains unclear. CONCLUSION The risk of IPNs progressing to metastasis in patients with primary bone sarcoma is non-negligible. Large IPNs have a high risk to be an actual metastasis. We suggest that IPNs in these patients be followed up for a minimum of 2 years with CT imaging at 3, 6, and 12 month intervals, particularly for nodules measuring >5 mm in average diameter. ADVANCES IN KNOWLEDGE This is the first systematic review on IPNs in patients with primary bone sarcomas only and proposes viable management strategies for such patients.
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Affiliation(s)
- M Shihabul Hassan
- College of Medical and Dental Sciences, University of Birmingham, Birmingham, B15 2TT, United Kingdom
| | - Sisith Ariyaratne
- Department of Musculoskeletal Radiology, The Royal Orthopaedic Hospital NHS Foundation Trust, Birmingham, B31 2AP, United Kingdom
| | - Christine Azzopardi
- Department of Musculoskeletal Radiology, The Royal Orthopaedic Hospital NHS Foundation Trust, Birmingham, B31 2AP, United Kingdom
| | - Karthikeyan P Iyengar
- Department of Orthopaedics, Mersey and West Lancashire Teaching Hospitals NHS Trust, Southport, PR8 6PN, United Kingdom
| | - Arthur Mark Davies
- Department of Musculoskeletal Radiology, The Royal Orthopaedic Hospital NHS Foundation Trust, Birmingham, B31 2AP, United Kingdom
| | - Rajesh Botchu
- Department of Musculoskeletal Radiology, The Royal Orthopaedic Hospital NHS Foundation Trust, Birmingham, B31 2AP, United Kingdom
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Chen K, Zhang F, Yu X, Huang Z, Gong L, Xu Y, Li H, Yu S, Fan Y. A molecular approach integrating genomic and DNA methylation profiling for tissue of origin identification in lung-specific cancer of unknown primary. J Transl Med 2022; 20:158. [PMID: 35382836 PMCID: PMC8981640 DOI: 10.1186/s12967-022-03362-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 03/26/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Determining the tissue of origin (TOO) is essential for managing cancer of unknown primary (CUP). In this study, we evaluated the concordance between genome profiling and DNA methylation analysis in determining TOO for lung-specific CUP and assessed their performance by comparing the clinical responses and survival outcomes of patients predicted with multiple primary or with metastatic cancer. METHODS We started by retrospectively screening for CUP patients who presented with both intra- and extrathoracic tumors. Tumor samples from included patients were analyzed with targeted sequencing with a 520-gene panel and targeted bisulfite sequencing. TOO inferences were made in parallel via an algorithm using genome profiles and time interval between tumors and via machine learning-based classification of DNA methylation profiles. RESULTS Four hundred patients were screened retrospectively. Excluding patients definitively diagnosed with conventional diagnostic work-up or without available samples, 16 CUP patients were included. Both molecular approaches alone enabled inference of clonality for all analyzed patients. Genome profile enabled TOO inference for 43.8% (7/16) patients, and the percentage rose to 68.8% (11/16) after considering inter-tumor time lag. On the other hand, DNA methylation analysis was conclusive for TOO prediction for 100% (14/14) patients with available samples. The two approaches gave 100% (9/9) concordant inferences regarding clonality and TOO identity. Moreover, patients predicted with metastatic disease showed significantly shorter overall survival than those with multiple primary tumors. CONCLUSIONS Genome and DNA methylation profiling have shown promise as individual analysis for TOO identification. This study demonstrated the feasibility of incorporating the two methods and proposes an integrative scheme to facilitate diagnosing and treating lung-specific CUPs.
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Affiliation(s)
- Kaiyan Chen
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Fanrong Zhang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Breast Surgery, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Xiaoqing Yu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Clinical Trial, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Zhiyu Huang
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Lei Gong
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Yanjun Xu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Hui Li
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Sizhe Yu
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China.,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China
| | - Yun Fan
- The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China. .,Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China. .,Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, Hangzhou, 310022, China. .,Department of Thoracic Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, 310022, China.
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Kocher MR, Chamberlin J, Waltz J, Snoddy M, Stringer N, Stephenson J, Kahn J, Mercer M, Baruah D, Aquino G, Kabakus I, Hoelzer P, Sahbaee P, Schoepf UJ, Burt JR. Tumor burden of lung metastases at initial staging in breast cancer patients detected by artificial intelligence as a prognostic tool for precision medicine. Heliyon 2022; 8:e08962. [PMID: 35243082 PMCID: PMC8873537 DOI: 10.1016/j.heliyon.2022.e08962] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Revised: 12/20/2021] [Accepted: 02/11/2022] [Indexed: 12/05/2022] Open
Abstract
Background Determination of the total number and size of all pulmonary metastases on chest CT is time-consuming and as such has been understudied as an independent metric for disease assessment. A novel artificial intelligence (AI) model may allow for automated detection, size determination, and quantification of the number of pulmonary metastases on chest CT. Objective To investigate the utility of a novel AI program applied to initial staging chest CT in breast cancer patients in risk assessment of mortality and survival. Methods Retrospective imaging data from a cohort of 226 subjects with breast cancer was assessed by the novel AI program and the results validated by blinded readers. Mean clinical follow-up was 2.5 years for outcomes including cancer-related death and development of extrapulmonary metastatic disease. AI measurements including total number of pulmonary metastases and maximum nodule size were assessed by Cox-proportional hazard modeling and adjusted survival. Results 752 lung nodules were identified by the AI program, 689 of which were identified in 168 subjects having confirmed lung metastases (Lmet+) and 63 were identified in 58 subjects without confirmed lung metastases (Lmet-). When compared to the reader assessment, AI had a per-patient sensitivity, specificity, PPV and NPV of 0.952, 0.639, 0.878, and 0.830. Mortality in the Lmet + group was four times greater compared to the Lmet-group (p = 0.002). In a multivariate analysis, total lung nodule count by AI had a high correlation with overall mortality (OR 1.11 (range 1.07–1.15), p < 0.001) with an AUC of 0.811 (R2 = 0.226, p < 0.0001). When total lung nodule count and maximum nodule diameter were combined there was an AUC of 0.826 (R2 = 0.243, p < 0.001). Conclusion Automated AI-based detection of lung metastases in breast cancer patients at initial staging chest CT performed well at identifying pulmonary metastases and demonstrated strong correlation between the total number and maximum size of lung metastases with future mortality. Clinical impact As a component of precision medicine, AI-based measurements at the time of initial staging may improve prediction of which breast cancer patients will have negative future outcomes. Automated detection software can quantify lung metastases on initial staging chest CT in breast cancer patients. AI-detected lung metastases number and max diameter on CT at initial cancer staging were strong predictors of mortality. AI detection and segmentation tool contributes to accurate individualized prognostication in breast cancer patients.
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Affiliation(s)
- Madison R Kocher
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jordan Chamberlin
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeffrey Waltz
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Madalyn Snoddy
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Natalie Stringer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Joseph Stephenson
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jacob Kahn
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Megan Mercer
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Dhiraj Baruah
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Gilberto Aquino
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Ismail Kabakus
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | | | | | - U Joseph Schoepf
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
| | - Jeremy R Burt
- Medical University of South Carolina, Department of Radiology, 96 Jonathan Lucas Street Suite 210, MSC 323 Charleston, SC 29425, USA
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