WE‐D‐204B‐05

Online Monitoring and Error Detection of Real‐Time Tumor Displacement Prediction Accuracy Using Statistical Process Control

K. T. Malinowski, T. J. mc Avoy, R. George, Sonja Dieterich, W. D. D'souza

Research output: Contribution to journalArticle

Abstract

Purpose: To investigate two statistical process control (SPC) metrics, the Hotelling (T2) statistic and the input‐variable‐squared‐prediction‐error (Q(x)), for predicting degradation in real‐time tumor displacement accuracy without explicit measurement of tumor displacement. Method and Materials: Independently but concurrently localized tumor and external surrogate positions from a database of Cyberknife Synchrony™ cases (130 treatment fractions from 63 lung tumors, 10 fractions from 5 liver tumors, and 48 fractions from 23 pancreas tumors) were analyzed. Each fraction consisted of 40–112 measurements obtained at an average rate of 0.018 Hz. The first 10 measured internal/external samples in each fraction were used to create fraction‐specific models of tumor displacement using external surrogates. The regression coefficients relating the 3D positions of the 3 skin markers to the 3D tumor positions were calculated using partial‐least‐squares (PLS) regression. The PLS model was applied to all subsequent localizations in the fraction. The T2‐ and Q(x)‐statistics in the training data were used to develop 90th, 95th and 99th percentile ranges of expected T2 and Q(x) values. The sensitivities and specificities of T2, Q(x), T2̆Q(x), and T2̑Q(x) for predicting real‐time tumor displacement errors greater than 3mm and 5mm were determined. Results: The T2, Q(x), T2̆Q(x), and T2̑Q(x) statistics' sensitivities and specificities varied with error threshold and acceptable percentile ranges of values. In general, the Q(x) statistic was associated with high sensitivity and low specificity, while the T2 statistic was associated with moderate sensitivity and moderate specificity. For 90th percentile T2, 99th percentile Q(x) and 3 mm error, the sensitivities of T2, Q(x), T2̆Q(x), and T2̑Q(x) were 69%, 88%, 92%, and 64%, respectively, and the specificities were 62%, 37%, 28%, and 72%, respectively. Conclusion: This study illustrates the feasibility of SPC metrics for detecting breakdowns in tumor displacement prediction accuracy using external sensors.

Original languageEnglish (US)
Number of pages1
JournalMedical Physics
Volume37
Issue number6
DOIs
StatePublished - 2010
Externally publishedYes

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Neoplasms
Sensitivity and Specificity
Feasibility Studies
Pancreas
Databases
Lung
Skin
Liver

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

WE‐D‐204B‐05 : Online Monitoring and Error Detection of Real‐Time Tumor Displacement Prediction Accuracy Using Statistical Process Control. / Malinowski, K. T.; mc Avoy, T. J.; George, R.; Dieterich, Sonja; D'souza, W. D.

In: Medical Physics, Vol. 37, No. 6, 2010.

Research output: Contribution to journalArticle

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abstract = "Purpose: To investigate two statistical process control (SPC) metrics, the Hotelling (T2) statistic and the input‐variable‐squared‐prediction‐error (Q(x)), for predicting degradation in real‐time tumor displacement accuracy without explicit measurement of tumor displacement. Method and Materials: Independently but concurrently localized tumor and external surrogate positions from a database of Cyberknife Synchrony™ cases (130 treatment fractions from 63 lung tumors, 10 fractions from 5 liver tumors, and 48 fractions from 23 pancreas tumors) were analyzed. Each fraction consisted of 40–112 measurements obtained at an average rate of 0.018 Hz. The first 10 measured internal/external samples in each fraction were used to create fraction‐specific models of tumor displacement using external surrogates. The regression coefficients relating the 3D positions of the 3 skin markers to the 3D tumor positions were calculated using partial‐least‐squares (PLS) regression. The PLS model was applied to all subsequent localizations in the fraction. The T2‐ and Q(x)‐statistics in the training data were used to develop 90th, 95th and 99th percentile ranges of expected T2 and Q(x) values. The sensitivities and specificities of T2, Q(x), T2̆Q(x), and T2̑Q(x) for predicting real‐time tumor displacement errors greater than 3mm and 5mm were determined. Results: The T2, Q(x), T2̆Q(x), and T2̑Q(x) statistics' sensitivities and specificities varied with error threshold and acceptable percentile ranges of values. In general, the Q(x) statistic was associated with high sensitivity and low specificity, while the T2 statistic was associated with moderate sensitivity and moderate specificity. For 90th percentile T2, 99th percentile Q(x) and 3 mm error, the sensitivities of T2, Q(x), T2̆Q(x), and T2̑Q(x) were 69{\%}, 88{\%}, 92{\%}, and 64{\%}, respectively, and the specificities were 62{\%}, 37{\%}, 28{\%}, and 72{\%}, respectively. Conclusion: This study illustrates the feasibility of SPC metrics for detecting breakdowns in tumor displacement prediction accuracy using external sensors.",
author = "Malinowski, {K. T.} and {mc Avoy}, {T. J.} and R. George and Sonja Dieterich and D'souza, {W. D.}",
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T2 - Online Monitoring and Error Detection of Real‐Time Tumor Displacement Prediction Accuracy Using Statistical Process Control

AU - Malinowski, K. T.

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AU - George, R.

AU - Dieterich, Sonja

AU - D'souza, W. D.

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AB - Purpose: To investigate two statistical process control (SPC) metrics, the Hotelling (T2) statistic and the input‐variable‐squared‐prediction‐error (Q(x)), for predicting degradation in real‐time tumor displacement accuracy without explicit measurement of tumor displacement. Method and Materials: Independently but concurrently localized tumor and external surrogate positions from a database of Cyberknife Synchrony™ cases (130 treatment fractions from 63 lung tumors, 10 fractions from 5 liver tumors, and 48 fractions from 23 pancreas tumors) were analyzed. Each fraction consisted of 40–112 measurements obtained at an average rate of 0.018 Hz. The first 10 measured internal/external samples in each fraction were used to create fraction‐specific models of tumor displacement using external surrogates. The regression coefficients relating the 3D positions of the 3 skin markers to the 3D tumor positions were calculated using partial‐least‐squares (PLS) regression. The PLS model was applied to all subsequent localizations in the fraction. The T2‐ and Q(x)‐statistics in the training data were used to develop 90th, 95th and 99th percentile ranges of expected T2 and Q(x) values. The sensitivities and specificities of T2, Q(x), T2̆Q(x), and T2̑Q(x) for predicting real‐time tumor displacement errors greater than 3mm and 5mm were determined. Results: The T2, Q(x), T2̆Q(x), and T2̑Q(x) statistics' sensitivities and specificities varied with error threshold and acceptable percentile ranges of values. In general, the Q(x) statistic was associated with high sensitivity and low specificity, while the T2 statistic was associated with moderate sensitivity and moderate specificity. For 90th percentile T2, 99th percentile Q(x) and 3 mm error, the sensitivities of T2, Q(x), T2̆Q(x), and T2̑Q(x) were 69%, 88%, 92%, and 64%, respectively, and the specificities were 62%, 37%, 28%, and 72%, respectively. Conclusion: This study illustrates the feasibility of SPC metrics for detecting breakdowns in tumor displacement prediction accuracy using external sensors.

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