Evaluating Tumor Response of Non-Small Cell Lung Cancer Patients With 18F-Fludeoxyglucose Positron Emission Tomography: Potential for Treatment Individualization

Toma-Daşu, Iuliana; Uhrdin, J.; Lazzeroni, M.; Carvalho, Sara; Elmpt, Wouter van; Lambin, Philippe; Daşu, Alexandru

doi:10.1016/j.ijrobp.2014.10.012

Cited by 28 publications

(20 citation statements)

References 30 publications

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“…Although the atlas has an impact on a significant number of patients, its applicability is limited to 40% to 50% of the patients receiving chemotherapy and radiation therapy by excluding patients whose tumor grows or shrinks very little. Studies using clinical data and radiomics (17–19) have demonstrated that quantitative image features extracted from pretreatment scans can be used to predict tumor response (eg, volume shrinkage) to chemotherapy and radiation therapy, thereby identifying patients who may not benefit from PTP in advance of radiation therapy. However, even if the stratification of patients to PTP is wrong, the simultaneous integrated boost to the target predicted by PTP becomes a randomly located hot spot within the initial PTV, which would either not affect TCP or, more likely, would increase it without increasing OAR toxicity because of the isotoxic design of PTP compared with the standard uniform dosing approach.…”

Section: Discussionmentioning

confidence: 99%

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer

Zhang

Yorke

Mageras

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

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Section: Discussionmentioning

confidence: 99%

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer

Zhang

Yorke

Mageras

et al. 2018

International Journal of Radiation Oncology*Biology*Physics

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“…The study of radiomics promises to improve patient‐specific care in radiation oncology by using the vast amounts of imaging data, acquired during radiation therapy (RT) . A method that has recently generated considerable interest is the extraction and analysis of texture features and their change over treatment (i.e., delta‐radiomics), as a means to quantitatively evaluate treatment response during RT delivery . Features may be designed in order to assess the amount of heterogeneity over a region of interest (ROI), or to quantify spatial information.…”

Section: Introductionmentioning

confidence: 99%

Time stability of delta‐radiomics features and the impact on patient analysis in longitudinal CT images

et al. 2019

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Purpose: This study first aims to show that the values of texture features extracted from phantoms are stable over clinical timescales. Second, that changes in patients' feature values over the course of radiation therapy (RT) are treatment induced and statistically significant. Methods: The CT datasets of a 3D printed anatomically informed texture phantom containing liver and low-contrast modules, and the homogeneous module of the Catphan 500-Series phantom, were acquired once per week over the course of a 6-week period, to simulate the timescale of conventional RT duration. A Definition AS Open CT scanner on rails (Siemens) and our institution's standard abdominal protocol were used. In each phantom module, 8 regions of interest (20 cm 3 ) were selected and 50 texture features were extracted from each module over the longitudinal dataset. The time stability of each feature was evaluated. The expected variation over the treatment timescale was quantified for each texture (module). Subsequently, the pancreas heads of 10 patients who underwent RT for adenocarcinoma of the pancreas head with a pathologic response of at least "moderate" (grade 2), were contoured on the daily CTs acquired using the same scanner. The pancreas heads were contoured on one image per week. Mean CT number, skewness, kurtosis, and coarseness were extracted from these data. The phantom modules were shown to be accurate representations of these features in the pancreas data. The change in the feature value between fractions 2 and 26 was compared with the phantom data in order to identify significant changes in feature value. Results: Of the 50 features examined in all 3 phantom modules, 47 were found to have zero timetrend when a fit assuming homogeneous variance was used. When a fit allowing for heterogeneous variance was used, 49 features were found to have zero time-trend. Features were stable and repeatable within a feature-specific confidence interval over the 6-week period of acquisition in all three phantom modules. Changes in feature value between fractions 2 and 26 were highly patient specific. Mean CT number was found to decrease significantly in 7 of 10 patients and increase significantly in one patient. Skewness increased significantly in one patient and decreased significantly in one patient. Kurtosis decreased significantly in four patients and increased significantly in one patient. Coarseness increased significantly in seven patients and decreased significantly in one patient. Only one patient experienced no significant changes in feature value. Conclusion: The CT texture feature measurements of phantoms are stable and repeatable within a feature-specific confidence interval in all three phantom modules. This suggests that the changes observed in features extracted from longitudinal patient CT data may be treatment induced, and demonstrates their potentiality for early assessment of treatment response.

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“…In patients undergoing thoracic radiotherapy or chemoradiotherapy, decreases in overall tumor activity (not SUV max ) 2 to 3 weeks into treatment correlated with overall survival and/or progression free survival. [46][47][48] Series examining PET-CT changes 4 to 5 weeks into chemoradiotherapy treatment also appeared to stratify responders and nonresponders, [49][50][51][52] but only one study showed an association of PET-CT changes with survival, 50 and these studies differed significantly in their benchmarks to evaluate PET-CT response. Table 4 summarizes these results.…”

Section: Identifying Those At Significant Risk Of Locoregional Failurementioning

confidence: 99%

Local Radiotherapy Intensification for Locally Advanced Non–small-cell Lung Cancer – A Call to Arms

Kalman

Weiss

Walker

et al. 2018

Clinical Lung Cancer

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Chemoradiotherapy, the standard of care for locally advanced non-small-cell lung cancer (NSCLC), often fails to eradicate all known disease. Despite advances in chemotherapeutic regimens, locally advanced NSCLC remains a difficult disease to treat, and locoregional failure remains common. Improved radiographic detection can identify patients at significant risk of locoregional failure after definitive treatment, and newer methods of escalating locoregional treatment may allow for improvements in locoregional control with acceptable toxicity. This review addresses critical issues in escalating local therapy, focusing on using serial positron emission tomography-computed tomography to select high-risk patients and employing stereotactic radiotherapy to intensify treatment. We further propose a clinical trial concept that incorporates the review's findings.

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Evaluating Tumor Response of Non-Small Cell Lung Cancer Patients With 18F-Fludeoxyglucose Positron Emission Tomography: Potential for Treatment Individualization

Cited by 28 publications

References 30 publications

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer

Validating a Predictive Atlas of Tumor Shrinkage for Adaptive Radiotherapy of Locally Advanced Lung Cancer

Time stability of delta‐radiomics features and the impact on patient analysis in longitudinal CT images

Local Radiotherapy Intensification for Locally Advanced Non–small-cell Lung Cancer – A Call to Arms

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