Daniel Dillinger, Stephan Waldeck, Daniel Overhoff, Sebastian Faby, Markus Jürgens, Bernhard Schmidt, Albrecht Hesse, Justine Schoch, Hans Schmelz, Rico Stoll, Tim Nestler
{"title":"利用光子计数探测器 CT 进行肾结石成分自动分析的性能研究--一项模型研究。","authors":"Daniel Dillinger, Stephan Waldeck, Daniel Overhoff, Sebastian Faby, Markus Jürgens, Bernhard Schmidt, Albrecht Hesse, Justine Schoch, Hans Schmelz, Rico Stoll, Tim Nestler","doi":"10.1016/j.acra.2024.10.045","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.</p><p><strong>Methods: </strong>A total of 148 urinary stones with a known composition that was measured by the standard reference method infrared spectroscopy (reference) were placed in an abdomen phantom and scanned in the PCDCT. Our objectives were to assess the stone detection rates of PCDCT and the accuracy of the prediction of the stone composition in UA vs non-UA compared to the reference.</p><p><strong>Results: </strong>Automated detection recognized 86.5% of all stones, with best detection rate for stones larger > 5 mm in diameter (95.4%, 88.8% for stones larger than 3 mm, 94.7% for stones larger than 4 mm). Depending on the volume, we found a recognition rate of 92.8% for stones larger than 20 mm<sup>3</sup> and 94.0% for stones with more than 30 mm<sup>3</sup>. Prediction of UA composition showed an overall sensitivity and a positive predictive value of 66.7% and a specificity and negative predictive value of 94.5%. Best diagnostic values volume wise were found by only including stones with a larger volume than 30 mm<sup>3</sup>, there we found a sensitivity of 91.7%, and a specificity of 92.4%. Sensitivity in dependance of the largest diameter was best for stones larger than 5 mm (85.7%), but specificity decreased with increasing diameter (to 91.3%).</p><p><strong>Conclusion: </strong>Automated urinary stone composition analysis with PCDCT showed a good automated detection rate of 86.5% up to 95.4% depending on stone diameter. The differentiation between non-UA and UA stones is performed with an NPV of 94.5% and a PPV of 66.7%. The prediction probability of non-UA stones was very good. This means the automatic detection and differentiation algorithm can identify the patients which will not profit from chemolitholysis.</p>","PeriodicalId":50928,"journal":{"name":"Academic Radiology","volume":" ","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Automated Kidney Stone Composition Analysis with Photon-Counting Detector CT, a Performance Study-A Phantom Study.\",\"authors\":\"Daniel Dillinger, Stephan Waldeck, Daniel Overhoff, Sebastian Faby, Markus Jürgens, Bernhard Schmidt, Albrecht Hesse, Justine Schoch, Hans Schmelz, Rico Stoll, Tim Nestler\",\"doi\":\"10.1016/j.acra.2024.10.045\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.</p><p><strong>Methods: </strong>A total of 148 urinary stones with a known composition that was measured by the standard reference method infrared spectroscopy (reference) were placed in an abdomen phantom and scanned in the PCDCT. Our objectives were to assess the stone detection rates of PCDCT and the accuracy of the prediction of the stone composition in UA vs non-UA compared to the reference.</p><p><strong>Results: </strong>Automated detection recognized 86.5% of all stones, with best detection rate for stones larger > 5 mm in diameter (95.4%, 88.8% for stones larger than 3 mm, 94.7% for stones larger than 4 mm). Depending on the volume, we found a recognition rate of 92.8% for stones larger than 20 mm<sup>3</sup> and 94.0% for stones with more than 30 mm<sup>3</sup>. Prediction of UA composition showed an overall sensitivity and a positive predictive value of 66.7% and a specificity and negative predictive value of 94.5%. Best diagnostic values volume wise were found by only including stones with a larger volume than 30 mm<sup>3</sup>, there we found a sensitivity of 91.7%, and a specificity of 92.4%. Sensitivity in dependance of the largest diameter was best for stones larger than 5 mm (85.7%), but specificity decreased with increasing diameter (to 91.3%).</p><p><strong>Conclusion: </strong>Automated urinary stone composition analysis with PCDCT showed a good automated detection rate of 86.5% up to 95.4% depending on stone diameter. The differentiation between non-UA and UA stones is performed with an NPV of 94.5% and a PPV of 66.7%. The prediction probability of non-UA stones was very good. This means the automatic detection and differentiation algorithm can identify the patients which will not profit from chemolitholysis.</p>\",\"PeriodicalId\":50928,\"journal\":{\"name\":\"Academic Radiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.acra.2024.10.045\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.acra.2024.10.045","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Automated Kidney Stone Composition Analysis with Photon-Counting Detector CT, a Performance Study-A Phantom Study.
Background: For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.
Methods: A total of 148 urinary stones with a known composition that was measured by the standard reference method infrared spectroscopy (reference) were placed in an abdomen phantom and scanned in the PCDCT. Our objectives were to assess the stone detection rates of PCDCT and the accuracy of the prediction of the stone composition in UA vs non-UA compared to the reference.
Results: Automated detection recognized 86.5% of all stones, with best detection rate for stones larger > 5 mm in diameter (95.4%, 88.8% for stones larger than 3 mm, 94.7% for stones larger than 4 mm). Depending on the volume, we found a recognition rate of 92.8% for stones larger than 20 mm3 and 94.0% for stones with more than 30 mm3. Prediction of UA composition showed an overall sensitivity and a positive predictive value of 66.7% and a specificity and negative predictive value of 94.5%. Best diagnostic values volume wise were found by only including stones with a larger volume than 30 mm3, there we found a sensitivity of 91.7%, and a specificity of 92.4%. Sensitivity in dependance of the largest diameter was best for stones larger than 5 mm (85.7%), but specificity decreased with increasing diameter (to 91.3%).
Conclusion: Automated urinary stone composition analysis with PCDCT showed a good automated detection rate of 86.5% up to 95.4% depending on stone diameter. The differentiation between non-UA and UA stones is performed with an NPV of 94.5% and a PPV of 66.7%. The prediction probability of non-UA stones was very good. This means the automatic detection and differentiation algorithm can identify the patients which will not profit from chemolitholysis.
期刊介绍：
Academic Radiology publishes original reports of clinical and laboratory investigations in diagnostic imaging, the diagnostic use of radioactive isotopes, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasound, digital subtraction angiography, image-guided interventions and related techniques. It also includes brief technical reports describing original observations, techniques, and instrumental developments; state-of-the-art reports on clinical issues, new technology and other topics of current medical importance; meta-analyses; scientific studies and opinions on radiologic education; and letters to the Editor.
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