Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?

Diamant, André; Chatterjee, A.; Faria, Sergio; Naqa, Issam El; Bahig, Houda; Filion, Édith; Robinson, Clifford G.; Al-Halabi, H.; Seuntjens, J

doi:10.1016/j.radonc.2018.05.012

Cited by 21 publications

(14 citation statements)

References 29 publications

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“…If the interaction term was removed in this analysis, there was no association between density features and dosimetric parameters with outcome. However, this contrasts with the work of Diamant et al where there was an increased risk of metastasis for lower dose across all patients (9). Further investigation is required to determine how the interaction validates in different cohorts.…”

Section: Discussionmentioning

confidence: 61%

“…Distant metastasis can occur due to untreated microscopic disease that spreads throughout the body via lymphatic, vascular, or local invasion ( 6 , 8 ). The impact of inadequate coverage of MDE was demonstrated in a study where a biologically equivalent dose (EQD2) of less than 21 Gy outside the planning target volume (PTV) was associated with increased risk of DM ( 9 ). In this study, there is an implicit assumption that inadequate coverage of MDE is associated with the same increase in risk of DM for all patients, but not all patients have extensive microscopic disease.…”

Section: Introductionmentioning

confidence: 99%

“…It is currently unknown whether the tumour or peritumour contains the most prognostic information, so studies have investigated shells surrounding the GTV ( 10 , 15 , 16 ). In addition, there is no consensus on what location should be investigated for dose ( 3 , 9 , 17 ).…”

Section: Introductionmentioning

confidence: 99%

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Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

et al. 2022

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PurposeLower dose outside the planned treatment area in lung stereotactic radiotherapy has been linked to increased risk of distant metastasis (DM) possibly due to underdosage of microscopic disease (MDE). Independently, tumour density on pretreatment computed tomography (CT) has been linked to risk of MDE. No studies have investigated the interaction between imaging biomarkers and incidental dose. The interaction would showcase whether the impact of dose on outcome is dependent on imaging and, hence, if imaging could inform which patients require dose escalation outside the gross tumour volume (GTV). We propose an image-based data mining methodology to investigate density–dose interactions radially from the GTV to predict DM with no a priori assumption on location.MethodsDose and density were quantified in 1-mm annuli around the GTV for 199 patients with early-stage lung cancer treated with 60 Gy in 5 fractions. Each annulus was summarised by three density and three dose parameters. For parameter combinations, Cox regressions were performed including a dose–density interaction in independent annuli. Heatmaps were created that described improvement in DM prediction due to the interaction. Regions of significant improvement were identified and studied in overall outcome models.ResultsDose–density interactions were identified that significantly improved prediction for over 50% of bootstrap resamples. Dose and density parameters were not significant when the interaction was omitted. Tumour density variance and high peritumour density were associated with DM for patients with more cold spots (less than 30-Gy EQD2) and non-uniform dose about 3 cm outside of the GTV. Associations identified were independent of the mean GTV dose.ConclusionsPatients with high tumour variance and peritumour density have increased risk of DM if there is a low and non-uniform dose outside the GTV. The dose regions are independent of tumour dose, suggesting that incidental dose may play an important role in controlling occult disease. Understanding such interactions is key to identifying patients who will benefit from dose-escalation. The methodology presented allowed spatial dose–density interactions to be studied at the exploratory stage for the first time. This could accelerate the clinical implementation of imaging biomarkers by demonstrating the impact of incidental dose for tumours of varying characteristics in routine data.

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Section: Discussionmentioning

confidence: 61%

Section: Introductionmentioning

confidence: 99%

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Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

et al. 2022

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“…The maximum standardized uptake value (SUVmax) in PET-CT was used to predict local recurrence after SBRT, but the results varied from institutions to patient groups, suggesting that its prognostic value was uncertain (6,7). Several studies reported some clinical and dosimetric factors were influential parameters for local control prediction (8)(9)(10)(11) and dose-response model to calculate local control possibility for lung SBRT patients employed clinical and dosimetric parameters were established (12)(13)(14)(15). However, their models did not accurately predict patients' outcome, while other tumor individual characteristics were not considered.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

et al. 2022

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PurposeStereotactic body radiotherapy (SBRT) is an important treatment modality for lung cancer patients, however, tumor local recurrence rate remains some challenge and there is no reliable prediction tool. This study aims to develop a prediction model of local control for lung cancer patients undergoing SBRT based on radiomics signature combining with clinical and dosimetric parameters.MethodsThe radiomics model, clinical model and combined model were developed by radiomics features, incorporating clinical and dosimetric parameters and radiomics signatures plus clinical and dosimetric parameters, respectively. Three models were established by logistic regression (LR), decision tree (DT) or support vector machine (SVM). The performance of models was assessed by receiver operating characteristic curve (ROC) and DeLong test. Furthermore, a nomogram was built and was assessed by calibration curve, Hosmer-Lemeshow and decision curve.ResultsThe LR method was selected for model establishment. The radiomics model, clinical model and combined model showed favorite performance and calibration (Area under the ROC curve (AUC) 0.811, 0.845 and 0.911 in the training group, 0.702, 0.786 and 0.818 in the validation group, respectively). The performance of combined model was significantly superior than the other two models. In addition, Calibration curve and Hosmer-Lemeshow (training group: P = 0.898, validation group: P = 0.891) showed good calibration of combined nomogram and decision curve proved its clinical utility.ConclusionsThe combined model based on radiomics features plus clinical and dosimetric parameters can improve the prediction of 1-year local control for lung cancer patients undergoing SBRT.

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“…Tumor cell death by high dose irradiation in SRT cannot be explained only by the direct cell death caused by DNA double-strand break and although the linear-quadratic model is applicable, it is not sufficient on its own to describe the immunogenic cell death and the cell death that results from vascular destruction, as observed after large-dose fractions [44–46]. The original work of Diamant et al shows that the dose in an area 3 cm thick outside the PTV for stage I NSCLC patients treated with SRT is correlated with the rate of distant metastasis but not the rate of local control, suggesting a dose-dependent immunogenic effect of radiation to the tumor's microenvironment [47].…”

Section: Radiation Therapymentioning

confidence: 99%

From Whole-Brain Radiotherapy to Immunotherapy: A Multidisciplinary Approach for Patients with Brain Metastases from NSCLC

Protopapa

Kouloulias

Nikoloudi

et al. 2019

Journal of Oncology

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Non-small cell lung cancer patients with brain metastases have a multitude of treatment options, but there is currently no international and multidisciplinary consensus concerning their optimal treatment. Local therapies have the principal role, especially in symptomatic patients. Advances in surgery and radiation therapy manage considerable local control. Systemic treatments have shown effect in clinical trials and in real life clinical settings; yet, at present, this is restricted to patients with asymptomatic or stable intracranial lesions. Targeted agents can have a benefit only in patients with EGFR mutations or ALK rearrangement. Immunotherapy has shown impressive results in patients with PD-L1 expression in tumor cells. Its effects can be further enhanced by a synergy with radiotherapy, possibly by increasing the percentage of responders. The present review summarizes the need for more effective systemic treatments, so that the increased intracranial control achieved by local treatments can be translated in an increase in overall survival.

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Can dose outside the PTV influence the risk of distant metastases in stage I lung cancer patients treated with stereotactic body radiotherapy (SBRT)?

Cited by 21 publications

References 29 publications

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

Radial Data Mining to Identify Density–Dose Interactions That Predict Distant Failure Following SABR

A Combined Model to Improve the Prediction of Local Control for Lung Cancer Patients Undergoing Stereotactic Body Radiotherapy Based on Radiomic Signature Plus Clinical and Dosimetric Parameters

From Whole-Brain Radiotherapy to Immunotherapy: A Multidisciplinary Approach for Patients with Brain Metastases from NSCLC

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