The impact of local control on overall survival after stereotactic body radiotherapy for liver and lung metastases from colorectal cancer: a combined analysis of 388 patients with 500 metastases

Abstract

Background: The aim of this analysis was to model the effect of local control (LC) on overall survival (OS) in patients treated with stereotactic body radiotherapy (SBRT) for liver or lung metastases from colorectal cancer.

Methods: The analysis is based on pooled data from two retrospective SBRT databases for pulmonary and hepatic metastases from 27 centers from Germany and Switzerland. Only patients with metastases from colorectal cancer were considered to avoid histology as a confounding factor. An illness-death model was employed to model the relationship between LC and OS.

Results: Three hundred eighty-eight patients with 500 metastatic lesions (lung n = 209, liver n = 291) were included and analyzed. Median follow-up time for local recurrence assessment was 12.1 months. Ninety-nine patients with 112 lesions experienced local failure. Seventy-one of these patients died after local failure. Median survival time was 27.9 months in all patients and 25.4 months versus 30.6 months in patients with and without local failure after SBRT. The baseline risk of death after local failure exceeds the baseline risk of death without local failure at 10 months indicating better survival with LC.

Conclusion: In CRC patients with lung or liver metastases, our findings suggest improved long-term OS by achieving metastatic disease control using SBRT in patients with a projected OS estimate of > 12 months.

Keywords: Colorectal cancer; Illness-death model; Liver metastases; Lung metastases; Stereotactic body radiation therapy; Tumor control probability.

Conflict of interest statement

Ethics approval and consent to participate

The multicenter data collection and analysis was approved by the Ethics committee of the Kanton Zurich, Switzerland (BASEC-Nr. 2016–00744) and in addition to local regulations also covered the following institutions:

1. University Hospital Zürich, Department of Radiation Oncology, University of Zurich, Zurich, Switzerland.

2. Strahlentherapie Bautzen, Department of Radiation Oncology, Bautzen, Germany

3. University of Munich – LMU Munich, Department of Radiation Oncology,Munich, German

4. University Hospital Basel, Department of Radiation Oncology, Basel, Switzerland

5. University Medical Center Hamburg-Eppendorf, Department of Radiation Oncology, Hamburg, Germany

6. Strahlenzentrum Hamburg, Department of Radiation Oncology, Hamburg, Germany

7. University Hospital of Cologne, Department of Radiation Oncology, Cologne, Germany

8. University Hospital Würzburg, Department of Radiation Oncology, Würzburg, Germany

9. University Hospital Halle, Department of Radiation Oncology, Halle, Germany

10. Klinikum Passau, Radiation Oncology, Passau, Germany

If necessary, the data collection of the individual participating centers was approved according to local regulations and approved by the respective local ethics committees. The following ethics committees and regulatory bodies were involved in this local approval process:

1. Medizinische Ethik-Komission II, Medizinische Fakultät Mannheim; 2014-413 M-MA-§23bMPG: University Hospital Mannheim, Department of Radiation Oncology, University of Heidelberg, Mannheim, Germany.

2. Ethikkommission der Medizinischen Fakultät Heidelberg; S459–2010:

3. Ethikkommission der Medizinischen Fakultät der Technischen Universität München; 84/16S: Klinikum rechts der Isar- Technische Universität München, Department of Radiation Oncology, Munich, Germany

4. Ethikkommission an der Medizinischen Fakultät der Universität Rostock, A2016–0008:

   1. Universitätsklinikum Schleswig-Holstein, Department of Radiation Oncology, Kiel/Lübeck, Germany.

   2. University Hospital Rostock, Department of Radiation Oncology, Rostock, Germany.

5. Ethikkommission der Universität Freiburg, 462/12: University Hospital Freiburg, Department of Radiation Oncology, Freiburg, Germany

6. Ärztekammer: Bezirksärztekammer Nord-Württemberg, Jahnstr. 5, 70,597 Stuttgart: RadioChirurgicum CyberKnife Südwest, Radiation Oncology, Göppingen, Germany.

7. Ethikkommission der Bayerischen Ärztekammer, mb BO 16002: Krankenhaus Barmherzige Brüder, Department of Radiation Oncology, Regensburg, Germany

The participants consent was written as part of the main ethics approval.

Consent for publication

Not applicable.

Competing interests

Marciana Duma and Christian Ostheimer are members of the editorial board (Associate editors) of BMC Cancer. NA confirms that all other authors have nothing to declare at the time of submission and that there are no competing interests to declare.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1

Conception of the illness-death modeling framework applied to the study of local failure and death in metastatic rectal cancer patients treated with SBRT. Starting from the state “SBRT treatment”, patients can either transition into the state “Local failure” (the non-terminal event occuring at time T1) or “Death” (the terminal event occurring at time T2). A third transition from “Local failure” to “Death” is also possible, but not vice versa. The rates at which patients transition from one state to the other are specified by three corresponding hazard functions that we model using Eqs. (1–3). h1(t1) is the hazard rate for local failure from SBRT at a given point in time t1, given that neither local failure or death have occurred before t1. h2(t2) is the hazard rate for death after SBRT at a given point in time t2, given that neither local failure nor death have occurred before t2. Finally, h3(t2 ∣ t1) is the hazard rate of death at a given time point t2 given that local failure has been observed at T1 = t1 and that death has not occurred before t2

Fig. 2

Tumor control probability predictions for treatment of a lung and liver metastasis with an average dose of BED = 132 Gy10. The left panel shows the prediction for a liver metastasis, the right panel for a lung metastasis. The black dotted line is a 95% CI for the black solid line based on 500 Monte Carlo samples. In both cases the other treatment characteristics (motion management, dose calculation algorithm, chemotherapy prior to SBRT) are the same. The Kaplan-Meier tumor control probability curves for liver and lung metastases are shown in red for comparison

Fig. 3

Baseline hazard ratio between transitions 3 and 2 as a function of follow-up time after treatment. Ratios greater than 1 indicate a greater risk of death if a patient has experienced a local recurrence prior to the time considered. The dashed lines indicate the 95% confidence band based on 500 Monte Carlo simulations of the baseline hazards. A very similar trend is observed when computing the baseline hazard ratio for a lung metastasis patient (coded with tumor site = 1), although the confidence bands are wider (not shown)

Fig. 4

Cumulative probability of making transitions 2 (black) and 3 (red) as a function of follow-up time after treatment. Predictions are for an average patient (male, KPS ≥ 90, age 

Fig. 5

Same as Fig. 4, but based on an analysis using only the subset of 311 metastases with no missing variables. Note that specifically for lung metastases patients, the confidence bands are somewhat narrower than for the imputed dataset which could be explained by the larger variation induced through pooling 50 different imputated datasates together as was done in Fig. 4