Predicting outcomes in radiation oncology—multifactorial decision support systems

Lambin, Philippe; Stiphout, Ruud G.P.M. van; Starmans, Maud H.W.; Rios-Velazquez, Emmanuel; Nalbantov, Georgi; Aerts, Hugo J.W.L.; Elmpt, Wouter van; Boutros, Paul C.; Granone, Pierluigi; Valentini, Vincenzo; Begg, Adrian C.; Ruysscher, Dirk De; Dekker, André

doi:10.1038/nrclinonc.2012.196

Cited by 343 publications

(302 citation statements)

References 210 publications

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“…This hypoxia-based patient selection could also be used in other therapy strategies for instance to target hypoxic subvolumes by escalate radiation dose (31). Furthermore, this information could be implemented in decision-support systems to predict tumor response and optimize patient therapy (32). These applications demonstrate the importance of gaining pretreatment information by hypoxia imaging.…”

Section: Discussionmentioning

confidence: 99%

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging

Peeters

Zegers

Biemans

et al. 2015

Clinical Cancer Research

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Purpose: Conventional anticancer treatments are often impaired by the presence of hypoxia. TH-302 selectively targets hypoxic tumor regions, where it is converted into a cytotoxic agent. This study assessed the efficacy of the combination treatment of TH-302 and radiotherapy in two preclinical tumor models. The effect of oxygen modification on the combination treatment was evaluated and the effect of TH-302 on the hypoxic fraction (HF) was monitored using [ 18 F]HX4-PET imaging and pimonidazole IHC stainings. Experimental Design: Rhabdomyosarcoma R1 and H460 NSCLC tumor-bearing animals were treated with TH-302 and radiotherapy (8 Gy, single dose). The tumor oxygenation status was altered by exposing animals to carbogen (95% oxygen) and nicotinamide, 21% or 7% oxygen breathing during the course of the treatment. Tumor growth and treatment toxicity were monitored until the tumor reached four times its start volume (T4ÂSV).Results: Both tumor models showed a growth delay after TH-302 treatment, which further increased when combined with radiotherapy (enhancement ratio rhabdomyosarcoma 1.23; H460 1.49). TH-302 decreases the HF in both models, consistent with its hypoxia-targeting mechanism of action. Treatment efficacy was dependent on tumor oxygenation; increasing the tumor oxygen status abolished the effect of TH-302, whereas enhancing the HF enlarged TH-302 0 s therapeutic effect. An association was observed in rhabdomyosarcoma tumors between the pretreatment HF as measured by [ 18 F]HX4-PET imaging and the T4ÂSV.Conclusions: The combination of TH-302 and radiotherapy is promising and warrants clinical testing, preferably guided by the companion biomarker [18 F]HX4 hypoxia PET imaging for patient selection.

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Section: Discussionmentioning

confidence: 99%

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging

Peeters

Zegers

Biemans

et al. 2015

Clinical Cancer Research

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“…Both DCE-MRI and the 31-gene expression signature may have potential as a tool for assessing hypoxia in cervical cancer (20,22). A current major limitation of MRI-based classifiers is, however, the lack of image standardization across MR machines (23,24). Gene expression assays are easier to standardize, and multigene signatures have shown to be useful for guiding treatment decisions in many cancer types, like breast and prostate cancer (25,26).…”

Section: Introductionmentioning

confidence: 99%

“…Development of a direct link between the two disciplines would facilitate implementation of a multifactorial tool for a more accurate response prediction (24,28). In this work, we aimed to prepare our signature for clinical use by constructing an image-associated, dichotomous hypoxia gene classifier with a predefined threshold for classification that could be utilized in a prospective setting.…”

Section: Introductionmentioning

confidence: 99%

Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer

Fjeldbo

Julin

Lando

et al. 2016

Clinical Cancer Research

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Purpose: A 31-gene expression signature reflected in dynamic contrast enhanced (DCE)-MR images and correlated with hypoxia-related aggressiveness in cervical cancer was identified in previous work. We here aimed to construct a dichotomous classifier with key signature genes and a predefined classification threshold that separated cervical cancer patients into a more and less hypoxic group with different outcome to chemoradiotherapy.Experimental Design: A training cohort of 42 patients and two independent cohorts of 108 and 131 patients were included. Gene expression data were generated from tumor biopsies by two Illumina array generations (WG-6, HT-12). Technical transfer of the classifier to a reverse transcription quantitative PCR (RT-qPCR) platform was performed for 74 patients. The amplitude A Brix in the Brix pharmacokinetic model was extracted from DCE-MR images of 64 patients and used as an indicator of hypoxia.Results: Classifier candidates were constructed by integrative analysis of A Brix and gene expression profiles in the training cohort and evaluated by a leave-one-out cross-validation approach. On the basis of their ability to separate patients correctly according to hypoxia status, a 6-gene classifier was identified. The classifier separated the patients into two groups with different progression-free survival probability. The robustness of the classifier was demonstrated by successful validation of hypoxia association and prognostic value across cohorts, array generations, and assay platforms. The prognostic value was independent of existing clinical markers, regardless of clinical endpoints.Conclusions: A robust DCE-MRI-associated gene classifier has been constructed that may be used to achieve an early indication of patients' risk of hypoxia-related chemoradiotherapy failure.

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“…26 Hence, we would like to test if combining different cPCs could improve prediction for tumor response compared to using one cPC. To do so, first a joint histogram of (a 1 , a 2 .…”

Section: D3d Tumor Subvolume Defined By Combined Projection Coefmentioning

confidence: 99%

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

Farjam¹,

Tsien²,

Lawrence³

et al. 2013

Med. Phys.

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Purpose: To develop a pharmacokinetic modelfree framework to analyze the dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data for assessment of response of brain metastases to radiation therapy. Methods: Twenty patients with 45 analyzable brain metastases had MRI scans prior to whole brain radiation therapy (WBRT) and at the end of the 2-week therapy. The volumetric DCE images covering the whole brain were acquired on a 3T scanner with approximately 5 s temporal resolution and a total scan time of about 3 min. DCE curves from all voxels of the 45 brain metastases were normalized and then temporally aligned. A DCE matrix that is constructed from the aligned DCE curves of all voxels of the 45 lesions obtained prior to WBRT is processed by principal component analysis to generate the principal components (PCs). Then, the projection coefficient maps prior to and at the end of WBRT are created for each lesion. Next, a pattern recognition technique, based upon fuzzy-c-means clustering, is used to delineate the tumor subvolumes relating to the value of the significant projection coefficients. The relationship between changes in different tumor subvolumes and treatment response was evaluated to differentiate responsive from stable and progressive tumors. Performance of the PCdefined tumor subvolume was also evaluated by receiver operating characteristic (ROC) analysis in prediction of nonresponsive lesions and compared with physiological-defined tumor subvolumes. Results: The projection coefficient maps of the first three PCs contain almost all response-related information in DCE curves of brain metastases. The first projection coefficient, related to the area under DCE curves, is the major component to determine response while the third one has a complimentary role. In ROC analysis, the area under curve of 0.88 ± 0.05 and 0.86 ± 0.06 were achieved for the PC-defined and physiological-defined tumor subvolume in response assessment. Conclusions: The PC-defined subvolume of a brain metastasis could predict tumor response to therapy similar to the physiological-defined one, while the former is determined more rapidly for clinical decision-making support.

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Predicting outcomes in radiation oncology—multifactorial decision support systems

Cited by 343 publications

References 210 publications

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging

TH-302 in Combination with Radiotherapy Enhances the Therapeutic Outcome and Is Associated with Pretreatment [18F]HX4 Hypoxia PET Imaging

Integrative Analysis of DCE-MRI and Gene Expression Profiles in Construction of a Gene Classifier for Assessment of Hypoxia-Related Risk of Chemoradiotherapy Failure in Cervical Cancer

DCE-MRI defined subvolumes of a brain metastatic lesion by principle component analysis and fuzzy-c-means clustering for response assessment of radiation therapy

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