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Jin J, Zhou H, Sun S, Tian Z, Ren H, Feng J. Supervised Learning Based Systemic Inflammatory Markers Enable Accurate Additional Surgery for pT1NxM0 Colorectal Cancer: A Comparative Analysis of Two Practical Prediction Models for Lymph Node Metastasis. Cancer Manag Res 2021; 13:8967-8977. [PMID: 34880677 PMCID: PMC8645952 DOI: 10.2147/cmar.s337516] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 11/22/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Predicting lymph node metastasis (LNM) after endoscopic resection is crucial in determining whether patients with pT1NxM0 colorectal cancer (CRC) should undergo additional surgery. This study was aimed to develop a predictive model that can be used to reduce the current likelihood of overtreatment. Patients and Methods We recruited a total of 1194 consecutive CRC patients with pT1NxM0 who underwent endoscopic or surgical resection at the Gezhouba Central Hospital of Sinopharm between January 1, 2006, and August 31, 2021. The random forest classifier (RFC) and generalized linear algorithm (GLM) were used to screen out the variables that greatly affected the LNM prediction, respectively. The area under the curve (AUC) and decision curve analysis (DCA) were applied to assess the accuracy of predictive models. Results Analysis identified the top 10 candidate factors including depth of submucosal invasion, neutrophil-lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), platelet-to-neutrophil ratio(PNR), venous invasion, poorly differentiated clusters, tumor budding, grade, lymphatic vascular invasion, and background adenoma. The performance of the GLM achieved the highest AUC of 0.79 (95% confidence interval [CI]: 0.30 to 1.28) in the training cohort and robust AUC of 0.80 (95% confidence interval [CI]: 0.36 to 1.24) in the validation cohort. Meanwhile, the RFC exhibited a robust AUC of 0.84 (95% confidence interval [CI]: 0.40 to 1.28) in the training cohort and a high AUC of 0.85 (95% CI: 0.41 to 1.29) in the validation cohort. DCAs also showed that the RFC had superior predictive ability. Conclusion Our supervised learning-based model incorporating histopathologic parameters and inflammatory markers showed a more accurate predictive performance compared to the GLM. This newly supervised learning-based predictive model can be used to determine an individually tailored treatment strategy.
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Affiliation(s)
- Jinlian Jin
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Haiyan Zhou
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Shulin Sun
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Zhe Tian
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Haibing Ren
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
| | - Jinwu Feng
- Department of Gastroenterology, The Third Clinical Medical College of China Three Gorges University, Gezhouba Central Hospital of Sinopharm, Yichang, Hubei, 443002, People's Republic of China
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Graziadio S, Winter A, Lendrem BC, Suklan J, Jones WS, Urwin SG, O’Leary RA, Dickinson R, Halstead A, Kurowska K, Green K, Sims A, Simpson AJ, Power HM, Allen AJ. How to Ease the Pain of Taking a Diagnostic Point of Care Test to the Market: A Framework for Evidence Development. MICROMACHINES 2020; 11:mi11030291. [PMID: 32164393 PMCID: PMC7142698 DOI: 10.3390/mi11030291] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/06/2020] [Accepted: 03/07/2020] [Indexed: 01/08/2023]
Abstract
Bringing a diagnostic point of care test (POCT) to a healthcare market can be a painful experience as it requires the manufacturer to meet considerable technical, financial, managerial, and regulatory challenges. In this opinion article we propose a framework for developing the evidence needed to support product development, marketing, and adoption. We discuss each step in the evidence development pathway from the invention phase to the implementation of a new POCT in the healthcare system. We highlight the importance of articulating the value propositions and documenting the care pathway. We provide guidance on how to conduct care pathway analysis as little has been published on this. We summarize the clinical, economic and qualitative studies to be considered for developing evidence, and provide useful links to relevant software, on-line applications, websites, and give practical advice. We also provide advice on patient and public involvement and engagement (PPIE), and on product management. Our aim is to help device manufacturers to understand the concepts and terminology used in evaluation of in vitro diagnostics (IVDs) so that they can communicate effectively with evaluation methodologists, statisticians, and health economists. Manufacturers of medical tests and devices can use the proposed framework to plan their evidence development strategy in alignment with device development, applications for regulatory approval, and publication.
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Affiliation(s)
- Sara Graziadio
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - Amanda Winter
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - B. Clare Lendrem
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - Jana Suklan
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - William S. Jones
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - Samuel G. Urwin
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - Rachel A. O’Leary
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - Rachel Dickinson
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - Anna Halstead
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - Kasia Kurowska
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - Kile Green
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - Andrew Sims
- NIHR Newcastle In Vitro Diagnostics Co-operative, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE1 4LP, UK; (S.G.); (A.W.); (S.G.U.); (R.A.O.); (R.D.); (A.S.)
| | - A. John Simpson
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - H. Michael Power
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
| | - A. Joy Allen
- NIHR Newcastle In Vitro Diagnostics Co-operative, Room M2.088, Translational and Clinical Research Institute, William Leech Building, Medical School, Newcastle University, Newcastle NE2 4HH, UK; (B.C.L.); (J.S.); (W.S.J.); (A.H.); (K.K.); (K.G.); (A.J.S.); (H.M.P.)
- Correspondence: ; Tel.: +44-(0)-191-208-3708
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