Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients

Otsuka, Masakazu; Monzen, Hajime; Matsumoto, Kenji; Tamura, Mikoto; Inada, Masahiro; Nishimura, Yasumasa

doi:10.1371/journal.pone.0204721

Cited by 7 publications

(5 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They determined that the best predictors of RILT based on ROC analysis were the mean dose, V20Gy, and V5Gy within the poorly ventilated regions. 18 It is important to reiterate that during this current work, regions of 20 Gy dose in the abnormal function PRM contours were not funneled to other regions of the lung that had more normal function, as previously described, which is shown by the significant V20Gy and mean dose decreases in Table 3 . Also, one could feasibly customize the workflow from this study to avoid regions of normal lung function.…”

Section: Discussionsupporting

confidence: 76%

“…[15][16][17] Furthermore, some recent studies have found that a lower functioning lung appears inherently more susceptible to radiation. 15,18 The works of both Otsuka et al and Owen et al found that the volume receiving greater than or equal to 20 Gy (V20Gy) in regions of poor lung function showed high predictive power of a patient's RILT risk, suggesting that the dose to low function regions should be mitigated.…”

Section: Introductionmentioning

confidence: 99%

“… 15 , 16 , 17 Furthermore, some recent studies have found that a lower functioning lung appears inherently more susceptible to radiation. 15 , 18 The works of both Otsuka et al. and Owen et al.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Feasibility of function‐guided lung treatment planning with parametric response mapping

Matrosic

Owen

Polan

et al. 2021

J Applied Clin Med Phys

View full text Add to dashboard Cite

Purpose: Recent advancements in functional lung imaging have been developed to improve clinicians' knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)-based functional lung imaging method that utilizes a voxel-wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning. Methods and materials: A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric-modulated arc therapy and PRM-guided treatment plans were designed for each patient. Results: PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM-guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity. Conclusions: PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM-guided treatment planning.

show abstract

Section: Discussionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Feasibility of function‐guided lung treatment planning with parametric response mapping

Matrosic

Owen

Polan

et al. 2021

J Applied Clin Med Phys

View full text Add to dashboard Cite

show abstract

“…52 Similarly, in 2018, Otsuka et al published a study that concluded that a high dose to poorly functioning regions, as determined by 4-dimensional CT V, was associated with the highest risk of toxicity. 53 However, dysfunctional lung sensitivity to RT has never been shown on V/Q SPECT imaging.…”

Section: Structurementioning

confidence: 99%

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy

Owen

Sun

Boonstra

et al. 2021

Advances in Radiation Oncology

View full text Add to dashboard Cite

Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT). Methods and Materials: SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving !20 Gy and mean functional intensity receiving !20 Gy (iV 20 ) were calculated. RILT was defined as grade 2þ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dosefunction metrics and risk of RILT.

show abstract

“…Studies have shown that dose-function metrics are more predictive of radiation toxicity than dose alone (33)(34)(35).…”

Section: Introductionmentioning

confidence: 99%

Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters

Huang¹,

Yan²,

Hu³

et al. 2021

Quant Imaging Med Surg

View full text Add to dashboard Cite

Background: To develop a fuzzy clustering neural network to predict radiation-induced pneumonitis (RP) using four-dimensional computed tomography (4DCT) ventilation image (VI) based dosimetric parameters for thoracic cancer patients. Methods:The VI were retrospectively calculated from pre-treatment 4DCT data using a deformable image registration (DIR) and an improved VI algorithm. Similar to dose-volume histogram (DVH) of intensity modulated radiotherapy (IMRT), dose-function histogram (DFH) was derived from dose distribution and VI. Then, the dose-function metrics were calculated from DFH. For comparison, the dose-volume metrics were calculated from DVH. Correspondingly, two sets of feature vectors were formed from the dose-volume metrics and the dose-function metrics, respectively. For the feature vectors of each set, they were first preprocessed by principal component analysis (PCA) to reduce feature dimensions. Then, they were grouped to few clusters determined by fuzzy c-means (FCM) algorithm. Next, the neural network was trained to correlate the dosimetric parameters with RP based on the feature vectors of each cluster. Finally, the occurrence of RP was predicted by the neural network on the test data.Results: Through PCA analysis, the top 5 principal components were selected. Their contribution is more than 98%, which is adequate to represent the original feature space of input data. Based on the clustering validity indexes, the optimal number of clusters is 4 and used for subsequent fuzzy clustering of the input data. After network training, the areas under the curve (AUC) of the prediction model is 0.77 using VI-based dosimetric parameters and 0.67 using structure-based dosimetric parameters.Conclusions: Compared to the structure-based dosimetric features, the VI-based dosimetric features are more relevant to lung function and presented higher prediction accuracy of RP. The fuzzy clustering neural network improved the prediction accuracy of RP compared to the conventional neural network. The combination of VI-based dose-function metrics and fuzzy clustering neural network provides an effective predictive model for assessing lung toxicity risk after radiotherapy.

show abstract

Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients

Cited by 7 publications

References 31 publications

Feasibility of function‐guided lung treatment planning with parametric response mapping

Feasibility of function‐guided lung treatment planning with parametric response mapping

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy

Predicting radiation pneumonitis with fuzzy clustering neural network using 4DCT ventilation image based dosimetric parameters

Contact Info

Product

Resources

About