Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer

Even, Aniek J.G.; Reymen, Bart; Fontaine, Matthew D. La; Das, Marco; Mottaghy, Felix M.; Belderbos, J.; Ruysscher, Dirk De; Lambin, Philippe; Elmpt, Wouter van

doi:10.1016/j.radonc.2017.09.041

Cited by 27 publications

(15 citation statements)

References 31 publications

(37 reference statements)

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“…Multiregion sequencing studies have revealed significant variations in the genetic makeup and molecular pathways across different regions in the same tumor (31). Image-based partitioning could be used to identify aggressive subregions that are important for determining prognosis and treatment response (32)(33)(34). Gillies et al used a threshold-based method to segment intratumoral regions (i.e., habitats) with prognostic value in glioblastoma (35).…”

Section: Discussionmentioning

confidence: 99%

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer

Gensheimer

Zhang

et al. 2019

J Nucl Med

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The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18 F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.

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Section: Discussionmentioning

confidence: 99%

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer

Gensheimer

Zhang

et al. 2019

J Nucl Med

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“…A strong association with HIF-2α overexpression and poor clinical outcomes such as OS (HR = 1.69, 95%CI: 1.39–2.06), DFS (HR = 1.87, 95%CI: 1.2–2.92), RPS (HR = 2.67, 95%CI: 1.32–5.38), and PFS (HR = 2.18, 95%CI: 1.25–3.78), including in NSCLC were reported. Finally, non-invasive methods were also investigated such as PET, using hypoxic-dedicated tracers [ 18 F]-FAZA in a cohort of localised NSCLC [ 229 , 230 ]. Tumours with hypoxic radiological features were associated with poorer PFS and OS [ 173 , 230 , 231 ].…”

Section: Prognostic Implications Of Hypoxia In Lung Cancermentioning

confidence: 99%

Hypoxia in Lung Cancer Management: A Translational Approach

Ancel

Perotin

Dewolf

et al. 2021

Cancers

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Lung cancer represents the first cause of death by cancer worldwide and remains a challenging public health issue. Hypoxia, as a relevant biomarker, has raised high expectations for clinical practice. Here, we review clinical and pathological features related to hypoxic lung tumours. Secondly, we expound on the main current techniques to evaluate hypoxic status in NSCLC focusing on positive emission tomography. We present existing alternative experimental approaches such as the examination of circulating markers and highlight the interest in non-invasive markers. Finally, we evaluate the relevance of investigating hypoxia in lung cancer management as a companion biomarker at various lung cancer stages. Hypoxia could support the identification of patients with higher risks of NSCLC. Moreover, the presence of hypoxia in treated tumours could help clinicians predict a worse prognosis for patients with resected NSCLC and may help identify patients who would benefit potentially from adjuvant therapies. Globally, the large quantity of translational data incites experimental and clinical studies to implement the characterisation of hypoxia in clinical NSCLC management.

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“…Using a datadriven partitioning approach with PET and DWI, intratumor subregions with high SUVs and low apparent diffusion coefficients reflect high aggressiveness of a tumor and are significant predictors of survival in lung adenocarcinoma (35). Another study divided the subregions using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4 PET/CT), and tumor vasculature (DCE-CT) in NSCLC patients treated with definitive chemoradiation (36). According to that study, metabolically active subregions that were highly hypoxic and demonstrated intermediate tumor perfusion were related to high-risk tumor type and worse survival, and data-driven sub-regional analysis for multimodal imaging may be used as a biomarker to predict the prognosis of the NSCLC patients (36).…”

Section: Imaging Genomicsmentioning

confidence: 99%

“…Another study divided the subregions using imaging of metabolic activity (FDG-PET/CT), hypoxia (HX4 PET/CT), and tumor vasculature (DCE-CT) in NSCLC patients treated with definitive chemoradiation (36). According to that study, metabolically active subregions that were highly hypoxic and demonstrated intermediate tumor perfusion were related to high-risk tumor type and worse survival, and data-driven sub-regional analysis for multimodal imaging may be used as a biomarker to predict the prognosis of the NSCLC patients (36). Thus, although significant work remains to be done, the role of the radiologist is expanding to the combined application of medical images obtained from multiple modalities, leading to an informed decision for better diagnosis, staging, and treatment response.…”

Section: Imaging Genomicsmentioning

confidence: 99%

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

et al. 2020

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Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.

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Clustering of multi-parametric functional imaging to identify high-risk subvolumes in non-small cell lung cancer

Abstract: We designed a subregional analysis for multi-parametric imaging in NSCLC, and showed the potential of subregion classification as a biomarker for prognosis. This methodology allows for a comprehensive data-driven analysis of multi-parametric functional images.

Cited by 27 publications

References 31 publications

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer

Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer

Hypoxia in Lung Cancer Management: A Translational Approach

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

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