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Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics

Vorhersage von Überleben und lokaler Kontrolle nach Radiochemotherapie bei lokal fortgeschrittenen Kopf-Hals-Tumoren mittels computertomographiebasierter Radiomics-Technik

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Abstract

Purpose

To appraise the ability of a radiomics signature to predict clinical outcome after definitive radiochemotherapy (RCT) of stage III–IV head and neck cancer.

Methods

A cohort of 110 patients was included in a retrospective analysis. Radiomics texture features were extracted from the gross tumor volumes contoured on planning computed tomography (CT) images. The cohort of patients was randomly divided into a training (70 patients) and a validation (40 patients) cohorts. Textural features were correlated to survival and control data to build predictive models. All the significant predictors of the univariate analysis were included in a multivariate model. The quality of the models was appraised by means of the concordance index (CI).

Results

A signature with 3 features was identified as predictive of overall survival (OS) with CI = 0.88 and 0.90 for the training and validation cohorts, respectively. A signature with 2 features was identified for progression-free survival (PFS; CI = 0.72 and 0.80); 2 features also characterized the signature for local control (LC; CI = 0.72 and 0.82). In all cases, the stratification in high- and low-risk groups for the training and validation cohorts led to significant differences in the actuarial curves. In the validation cohort the mean OS times (in months) were 78.9 ± 2.1 vs 67.4 ± 6.0 in the low- and high-risk groups, respectively, the PFS was 73.1 ± 3.7 and 50.7 ± 7.2, while the LC was 78.7 ± 2.1 and 63.9 ± 6.5.

Conclusion

CT-based radiomic signatures that correlate with survival and control after RCT were identified and allow low- and high-risk groups of patients to be identified.

Zusammenfassung

Zielsetzung

Ziel war die Untersuchung der Möglichkeit, mittels einer Radiomics-Signatur klinische Ergebnisse nach definitiver Radiochemotherapie (RCT) von Kopf-Hals-Tumoren im Stadium II–IV vorherzusagen.

Methoden

Eine Kohorte von 110 Patienten wurde in eine retrospektive Analyse einbezogen. Die Radiomics-Texturmerkmale wurden aus den Gross-Tumor-Volume-Konturen der Planungs-CT-Aufnahmen extrahiert. Die Patientengruppe wurde nach dem Zufallsprinzip in eine Trainingskohorte (70 Patienten) und eine Validierungskohorte (40 Patienten) unterteilt. Die Strukturmerkmale wurden mit den Überlebens- und Kontrolldaten korreliert, um Vorhersagemodelle zu erstellen. Alle signifikanten Prädiktoren der univariaten Analyse wurden in ein multivariates Modell aufgenommen. Die Qualität der Modelle wurde anhand des Konkordanzindex (CI) beurteilt.

Ergebnisse

Eine Signatur mit 3 Merkmalen wurde als prädiktiv für das Gesamtüberleben, OS, identifiziert, mit einem CI = 0,88 bzw. 0,90 für die Trainings- bzw. Validierungskohorte. Eine Signatur mit 2 Merkmalen wurde für das progressionsfreie Überleben, PFS, identifiziert (CI = 0,72 bzw. 0,80). Ebenfalls 2 Merkmale charakterisierten die Signatur für die lokale Kontrolle, LC (CI = 0,72 und 0,82). In allen Fällen führt die Stratifizierung in Hoch- und Niedrigrisikogruppen für die Trainings- und Validierungskohorte zu signifikanten Unterschieden in den aktuariellen Überlebenskurven. In der Validierungskohorte betrug die mittlere Überlebenszeit (in Monaten) 78,9 ± 2,1 gegenüber 67,4 ± 6,0 in der Niedrig- bzw. Hochrisikogruppe. Das progressionsfreie Überleben betrug 73,1 ± 3,7 gegenüber 50,7 ± 7,2; bei lokaler Kontrolle von 78,7 ± 2,1 gegenüber 63,9 ± 6,5.

Schlussfolgerung

Es wurden CT-basierte radiomische Signaturen identifiziert, die mit dem Überleben und der Kontrolle nach RCT korrelieren und es somit ermöglichen, Patienten mit niedrigem und hohem Risiko zu identifizieren.

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Authors and Affiliations

  1. Department of Biomedical Sciences, Humanitas University, Pieve Emanuele (Milan), Italy

    Luca Cozzi PhD & Marta Scorsetti MD

  2. Radiotherapy and Radiosurgery, Humanitas Clinical and Research Center, Rozzano (Milan), Italy

    Luca Cozzi PhD, Ciro Franzese MD, Antonella Fogliata MSc, Davide Franceschini MD, Pierina Navarria MD, Stefano Tomatis MSc & Marta Scorsetti MD

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  1. Luca Cozzi PhD
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  2. Ciro Franzese MD
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  3. Antonella Fogliata MSc
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  4. Davide Franceschini MD
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  5. Pierina Navarria MD
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  6. Stefano Tomatis MSc
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  7. Marta Scorsetti MD
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Corresponding author

Correspondence to Luca Cozzi PhD.

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Conflict of interest

L. Cozzi acts as a scientific advisor to Varian Medical Systems and is a Clinical Research Scientist at Humanitas Cancer Center. C. Franzese, A. Fogliata, D. Franceschini, P. Navarria, S. Tomatis and M. Scorsetti declare that they have no competing interests.

Additional information

The authors L. Cozzi and C. Franzese contributed equally to the manuscript.

Caption Electronic Supplementary Material

66_2019_1483_MOESM1_ESM.docx

The complementary materials contains low and high risk stratification for the entire cohort of patients grouped per disease site as well as pictorial and numerical examples of Low risk (A and C) and high risk (B and D) volumes from the hypopharynx/larynx group

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Cozzi, L., Franzese, C., Fogliata, A. et al. Predicting survival and local control after radiochemotherapy in locally advanced head and neck cancer by means of computed tomography based radiomics. Strahlenther Onkol 195, 805–818 (2019). https://doi.org/10.1007/s00066-019-01483-0

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