Maria Gonçalves-Reis , Daniela Proença , Laura P. Frazão , João L. Neto , Sílvia Silva , Hugo Pinto-Marques , José B. Pereira-Leal , Joana Cardoso
{"title":"肝移植前评估肝细胞癌预后的 HepatoPredict 套件的分析验证和算法改进","authors":"Maria Gonçalves-Reis , Daniela Proença , Laura P. Frazão , João L. Neto , Sílvia Silva , Hugo Pinto-Marques , José B. Pereira-Leal , Joana Cardoso","doi":"10.1016/j.plabm.2024.e00365","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT).</p></div><div><h3>Methods</h3><p>The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested.</p></div><div><h3>Results</h3><p>The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT.</p></div><div><h3>Conclusions</h3><p>Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.</p></div>","PeriodicalId":20421,"journal":{"name":"Practical Laboratory Medicine","volume":"39 ","pages":"Article e00365"},"PeriodicalIF":1.7000,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2352551724000118/pdfft?md5=95ebd2e890d2b47d236002b5f3c43a33&pid=1-s2.0-S2352551724000118-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation\",\"authors\":\"Maria Gonçalves-Reis , Daniela Proença , Laura P. Frazão , João L. Neto , Sílvia Silva , Hugo Pinto-Marques , José B. Pereira-Leal , Joana Cardoso\",\"doi\":\"10.1016/j.plabm.2024.e00365\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT).</p></div><div><h3>Methods</h3><p>The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested.</p></div><div><h3>Results</h3><p>The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT.</p></div><div><h3>Conclusions</h3><p>Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.</p></div>\",\"PeriodicalId\":20421,\"journal\":{\"name\":\"Practical Laboratory Medicine\",\"volume\":\"39 \",\"pages\":\"Article e00365\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-02-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2352551724000118/pdfft?md5=95ebd2e890d2b47d236002b5f3c43a33&pid=1-s2.0-S2352551724000118-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Practical Laboratory Medicine\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2352551724000118\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MEDICAL LABORATORY TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Practical Laboratory Medicine","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352551724000118","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MEDICAL LABORATORY TECHNOLOGY","Score":null,"Total":0}
Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation
Objectives
To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT).
Methods
The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested.
Results
The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT.
Conclusions
Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.
期刊介绍：
Practical Laboratory Medicine is a high-quality, peer-reviewed, international open-access journal publishing original research, new methods and critical evaluations, case reports and short papers in the fields of clinical chemistry and laboratory medicine. The objective of the journal is to provide practical information of immediate relevance to workers in clinical laboratories. The primary scope of the journal covers clinical chemistry, hematology, molecular biology and genetics relevant to laboratory medicine, microbiology, immunology, therapeutic drug monitoring and toxicology, laboratory management and informatics. We welcome papers which describe critical evaluations of biomarkers and their role in the diagnosis and treatment of clinically significant disease, validation of commercial and in-house IVD methods, method comparisons, interference reports, the development of new reagents and reference materials, reference range studies and regulatory compliance reports. Manuscripts describing the development of new methods applicable to laboratory medicine (including point-of-care testing) are particularly encouraged, even if preliminary or small scale.
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