Cherenkov imaging during volumetric modulated arc therapy for real-time radiation beam tracking and treatment response monitoring

Objectives: In real-time portal dosimetry, thresholds are set for several measures of difference between predicted and measured images, and signals larger than those thresholds signify an error. The aim of this work is to investigate the use of an additional composite difference metric (CDM) for earlier detection of errors. Methods: Portal images were predicted for the volumetric modulated arc therapy plans of six prostate patients. Errors in monitor units, aperture opening, aperture position and path length were deliberately introduced into all 180 segments of the treatment plans, and these plans were delivered to a water-equivalent phantom. Four different metrics, consisting of central axis signal, mean image value and two image difference measures, were used to identify errors, and a CDM was added, consisting of a weighted power sum of the individual metrics. To optimise the weights of the CDM and to evaluate the resulting timeliness of error detection, a leave-pair-out strategy was used. For each combination of four patients, the weights of the CDM were determined by an exhaustive search, and the result was evaluated on the remaining two patients. Results: The median segment index at which the errors were identified was 87 (range 40–130) when using all of the individual metrics separately. Using a CDM as well as multiple separate metrics reduced this to 73 (35–95). The median weighting factors of the four metrics constituting the composite were (0.15, 0.10, 0.15, 0.00). Due to selection of suitable threshold levels, there was only one false positive result in the six patients. Conclusion: This study shows that, in conjunction with appropriate error thresholds, use of a CDM is able to identify increased image differences around 20% earlier than the separate measures. Advances in knowledge: This study shows the value of combining difference metrics to allow earlier detection of errors during real-time portal dosimetry for volumetric modulated arc therapy treatment.

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Real-time interactive planning for radiotherapy of head and neck cancer with volumetric modulated arc therapy

Physics and Imaging in Radiation Oncology ◽

10.1016/j.phro.2019.03.002 ◽

2019 ◽

Vol 9 ◽

pp. 83-88 ◽

Cited By ~ 1

Author(s):

Lindsey Baker ◽

Robert Olson ◽

Taran Braich ◽

Theodora Koulis ◽

Allison Ye ◽

...

Keyword(s):

Head And Neck Cancer ◽

Head And Neck ◽

Real Time ◽

Neck Cancer ◽

Volumetric Modulated Arc Therapy ◽

Interactive Planning ◽

Arc Therapy

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Protease activity sensors enable real-time treatment response monitoring in lymphangioleiomyomatosis

European Respiratory Journal ◽

10.1183/13993003.00664-2021 ◽

2021 ◽

pp. 2100664

Author(s):

Jesse D. Kirkpatrick ◽

Ava P. Soleimany ◽

Jaideep S. Dudani ◽

Heng-Jia Liu ◽

Hilaire C. Lam ◽

...

Keyword(s):

Machine Learning ◽

Treatment Response ◽

Real Time ◽

Cysteine Proteases ◽

Response Monitoring ◽

Preclinical Model ◽

Real Time Monitoring ◽

Lung Function Decline ◽

Time Treatment

Biomarkers of disease progression and treatment response are urgently needed for patients with lymphangioleiomyomatosis (LAM). Activity-based nanosensors, an emerging biosensor class, detect dysregulated proteases in vivo and release a reporter to provide a urinary readout of disease. Because proteases are dysregulated in LAM and may directly contribute to lung function decline, activity-based nanosensors may enable quantitative, real-time monitoring of LAM progression and treatment response. We aimed to assess the diagnostic utility of activity-based nanosensors in a preclinical model of pulmonary LAM.Tsc2-null cells were injected intravenously into female nude mice to establish a mouse model of pulmonary LAM. A library of 14 activity-based nanosensors, designed to detect proteases across multiple catalytic classes, was administered into the lungs of LAM mice and healthy controls, urine was collected, and mass spectrometry was performed to measure nanosensor cleavage products. Mice were then treated with rapamycin and monitored with activity-based nanosensors. Machine learning was performed to distinguish diseased from healthy and treated from untreated mice.Multiple activity-based nanosensors [PP03 (cleaved by metallo, aspartic, and cysteine proteases), padj<0.0001; PP10 (cleaved by serine, aspartic, and cysteine proteases), padj=0.017)] were differentially cleaved in diseased and healthy lungs, enabling strong classification with a machine learning model (AUC=0.95 from healthy). Within two days after rapamycin initiation, we observed normalisation of PP03 and PP10 cleavage, and machine learning enabled accurate classification of treatment response (AUC=0.94 from untreated).Activity-based nanosensors enable noninvasive, real-time monitoring of disease burden and treatment response in a preclinical model of LAM.

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