A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention
Identifieur interne : 001232 ( Ncbi/Merge ); précédent : 001231; suivant : 001233A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention
Auteurs : K. YanSource :
- Medical Physics [ 0094-2405 ] ; 2009.
Abstract
In this article, the authors present a novel real-time cancer detection technique by using needle insertion forces in conjunction with patient-specific criteria during percutaneous interventions. Needle insertion experiments and pathological analysis were performed for developing a computer-aided detection (CAD) model. Backward stepwise regression method was performed to identify the statistically significant patient-specific factors. A baseline force model was then developed using these significant factors. The threshold force model that estimated the lower bound of the cancerous tissue forces was formulated by adding an adjustable classifier to the baseline force model. Trade-off between sensitivity and specificity was obtained by varying the threshold value of the classifier, from which the receiver-operating characteristic (ROC) curve was generated. Sequential quadratic programming was used to optimize the CAD model by maximizing the area under the ROC curve (AUC) using a set of model-training patient data. When the CAD model was evaluated using an independent set of model-validation patient data, an AUC of 0.90 was achieved. The feasibility of cancer detection in real time during percutaneous interventions was established.
Url:
DOI: 10.1118/1.3148834
PubMed: 19673230
PubMed Central: 2719494
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention</title><author><name sortKey="Yan, K" sort="Yan, K" uniqKey="Yan K" first="K." last="Yan">K. Yan</name></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">19673230</idno><idno type="pmc">2719494</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2719494</idno><idno type="RBID">PMC:2719494</idno><idno type="doi">10.1118/1.3148834</idno><date when="2009">2009</date><idno type="wicri:Area/Pmc/Corpus">000622</idno><idno type="wicri:Area/Pmc/Curation">000622</idno><idno type="wicri:Area/Pmc/Checkpoint">002214</idno><idno type="wicri:Area/Ncbi/Merge">001232</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention</title><author><name sortKey="Yan, K" sort="Yan, K" uniqKey="Yan K" first="K." last="Yan">K. Yan</name></author></analytic><series><title level="j">Medical Physics</title><idno type="ISSN">0094-2405</idno><idno type="eISSN">0094-2405</idno><imprint><date when="2009">2009</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p>In this article, the authors present a novel real-time cancer detection technique by using needle insertion forces in conjunction with patient-specific criteria during percutaneous interventions. Needle insertion experiments and pathological analysis were performed for developing a computer-aided detection (CAD) model. Backward stepwise regression method was performed to identify the statistically significant patient-specific factors. A baseline force model was then developed using these significant factors. The threshold force model that estimated the lower bound of the cancerous tissue forces was formulated by adding an adjustable classifier to the baseline force model. Trade-off between sensitivity and specificity was obtained by varying the threshold value of the classifier, from which the receiver-operating characteristic (ROC) curve was generated. Sequential quadratic programming was used to optimize the CAD model by maximizing the area under the ROC curve (AUC) using a set of model-training patient data. When the CAD model was evaluated using an independent set of model-validation patient data, an AUC of 0.90 was achieved. The feasibility of cancer detection in real time during percutaneous interventions was established.</p></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Med Phys</journal-id><journal-title-group><journal-title>Medical Physics</journal-title></journal-title-group><issn pub-type="ppub">0094-2405</issn><issn pub-type="epub">0094-2405</issn><publisher><publisher-name>American Association of Physicists in Medicine</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">19673230</article-id><article-id pub-id-type="pmc">2719494</article-id><article-id pub-id-type="publisher-id">032907MPH</article-id><article-id pub-id-type="doi">10.1118/1.3148834</article-id><article-categories><subj-group subj-group-type="heading"><subject>Tissue Measurements</subject></subj-group></article-categories><title-group><article-title>A real-time prostate cancer detection technique using needle insertion force and patient-specific criteria during percutaneous intervention</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Yan</surname><given-names>K.</given-names></name><xref ref-type="author-notes" rid="n1">a)</xref></contrib><aff>Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Li</surname><given-names>L.</given-names></name></contrib><aff>Department of Pathology, University of Rochester, Rochester, New York 14627</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Joseph</surname><given-names>J.</given-names></name></contrib><aff>Department of Urology and Department of Surgery, University of Rochester, Rochester, New York 14627</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Rubens</surname><given-names>D. R.</given-names></name></contrib><aff>Department of Imaging Science and Department of Surgery, University of Rochester, Rochester, New York 14627</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Messing</surname><given-names>E. M.</given-names></name></contrib><aff>Department of Urology and Department of Surgery, University of Rochester, Rochester, New York 14627</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Liao</surname><given-names>L.</given-names></name></contrib><aff>Department of Radiology, Cooper University Hospital, Camden, New Jersey 08103</aff></contrib-group><contrib-group><contrib contrib-type="author"><name><surname>Yu</surname><given-names>Y.</given-names></name></contrib><aff>Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, Pennsylvania 19107</aff></contrib-group><author-notes><fn id="n1"><label>a)</label><p>Author to whom correspondence should be addressed. Electronic mail: <email>kaiguo.yan@jeffersonhospital.org</email>; Telephone: (215)-955-6043; Fax: (215)-955-0412.</p></fn></author-notes><pub-date pub-type="ppub"><month>7</month><year>2009</year></pub-date><pub-date pub-type="epub"><day>23</day><month>6</month><year>2009</year></pub-date><volume>36</volume><issue>7</issue><fpage>3356</fpage><lpage>3362</lpage><history><date date-type="received"><day>25</day><month>6</month><year>2008</year></date><date date-type="rev-recd"><day>23</day><month>4</month><year>2009</year></date><date date-type="accepted"><day>14</day><month>5</month><year>2009</year></date></history><permissions><copyright-statement>Copyright © 2009 American Association of Physicists in Medicine</copyright-statement><copyright-year>2009</copyright-year><copyright-holder>American Association of Physicists in Medicine</copyright-holder></permissions><abstract><p>In this article, the authors present a novel real-time cancer detection technique by using needle insertion forces in conjunction with patient-specific criteria during percutaneous interventions. Needle insertion experiments and pathological analysis were performed for developing a computer-aided detection (CAD) model. Backward stepwise regression method was performed to identify the statistically significant patient-specific factors. A baseline force model was then developed using these significant factors. The threshold force model that estimated the lower bound of the cancerous tissue forces was formulated by adding an adjustable classifier to the baseline force model. Trade-off between sensitivity and specificity was obtained by varying the threshold value of the classifier, from which the receiver-operating characteristic (ROC) curve was generated. Sequential quadratic programming was used to optimize the CAD model by maximizing the area under the ROC curve (AUC) using a set of model-training patient data. When the CAD model was evaluated using an independent set of model-validation patient data, an AUC of 0.90 was achieved. 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