Karoline Pilz

A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling

Radiomics applies machine learning algorithms to quantitative imaging data to characterise the tumour phenotype and predict clinical outcome. For the development of radiomics risk models, a variety of different algorithms is available and it is not clear which one gives optimal results. Therefore, we assessed the performance of 11 machine learning algorithms combined with 12 feature selection methods by the concordance index (C-Index), to predict loco-regional tumour control (LRC) and overall survival for patients with head and neck squamous cell carcinoma. The considered algorithms are able to deal with continuous time-to-event survival data. Feature selection and model building were performed on a multicentre cohort (213 patients) and validated using an independent cohort (80 patients). We found several combinations of machine learning algorithms and feature selection methods which achieve similar results, e.g., MSR-RF: C-Index = 0.71 and BT-COX: C-Index = 0.70 in combination with Spearman feature selection. Using the best performing models, patients were stratified into groups of low and high risk of recurrence. Significant differences in LRC were obtained between both groups on the validation cohort. Based on the presented analysis, we identified a subset of algorithms which should be considered in future radiomics studies to develop stable and clinically relevant predictive models for time-to-event endpoints.

CT imaging during treatment improves radiomic models for patients with locally advanced head and neck cancer

BACKGROUND AND PURPOSE The development of radiomic risk models to predict clinical outcome is usually based on pre-treatment imaging, such as computed tomography (CT) scans used for radiation treatment planning. Imaging data acquired during the course of treatment may improve their prognostic performance. We compared the performance of radiomic risk models based on the pre-treatment CT and CT scans acquired in the second week of therapy. MATERIAL AND METHODS Treatment planning and second week CT scans of 78 head and neck squamous cell carcinoma patients treated with primary radiochemotherapy were collected. 1538 image features were extracted from each image. Prognostic models for loco-regional tumour control (LRC) and overall survival (OS) were built using 6 feature selection methods and 6 machine learning algorithms. Prognostic performance was assessed using the concordance index (C-Index). Furthermore, patients were stratified into risk groups and differences in LRC and OS were evaluated by log-rank tests. RESULTS The performance of radiomic risk model in predicting LRC was improved using the second week CT scans (C-Index: 0.79), in comparison to the pre-treatment CT scans (C-Index: 0.65). This was confirmed by Kaplan-Meier analyses, in which risk stratification based on the second week CT could be improved for LRC (p = 0.002) compared to pre-treatment CT (p = 0.063). CONCLUSION Incorporation of imaging during treatment may be a promising way to improve radiomic risk models for clinical treatment adaption, i.e., to select patients that may benefit from dose modification.

Alleinige IF-RT bleibt Goldstandard beim nodulären lymphozytenprädominanten Hodgkin-Lymphom im Stadium IA

Patienten und Methoden. Es wurden die Langzeitergebnisse von 256 Patienten mit einem NLPHL im Stadium IA untersucht. Diese hatten – analog zu den klinischen Studienprotokollen der Deutschen Hodgkin Studiengruppe von 1988 bis 2009 – entweder eine multimodale Therapie („combined modality treatment“, CMT; n = 72), eine „Extended-field“-Strahlentherapie (EF-RT; n = 49), eine „Involved-field“-Strahlentherapie (IF-RT; n = 108) oder Rituximab allein in der Standarddosierung von 375 mg/m2 für 4 Wochen (n = 27) erhalten.

[IF-RT alone remains gold standard for stage IA nodular lymphocyte-predominant Hodgkin lymphoma].

Patienten und Methoden. Es wurden die Langzeitergebnisse von 256 Patienten mit einem NLPHL im Stadium IA untersucht. Diese hatten – analog zu den klinischen Studienprotokollen der Deutschen Hodgkin Studiengruppe von 1988 bis 2009 – entweder eine multimodale Therapie („combined modality treatment“, CMT; n = 72), eine „Extended-field“-Strahlentherapie (EF-RT; n = 49), eine „Involved-field“-Strahlentherapie (IF-RT; n = 108) oder Rituximab allein in der Standarddosierung von 375 mg/m2 für 4 Wochen (n = 27) erhalten.

Alleinige IF-RT bleibt Goldstandard beim nodulären lymphozytenprädominanten Hodgkin-Lymphom im Stadium IA@@@IF-RT alone remains gold standard for stage IA nodular lymphocyte-predominant Hodgkin lymphoma

Patienten und Methoden. Es wurden die Langzeitergebnisse von 256 Patienten mit einem NLPHL im Stadium IA untersucht. Diese hatten – analog zu den klinischen Studienprotokollen der Deutschen Hodgkin Studiengruppe von 1988 bis 2009 – entweder eine multimodale Therapie („combined modality treatment“, CMT; n = 72), eine „Extended-field“-Strahlentherapie (EF-RT; n = 49), eine „Involved-field“-Strahlentherapie (IF-RT; n = 108) oder Rituximab allein in der Standarddosierung von 375 mg/m2 für 4 Wochen (n = 27) erhalten.