Author of 2 Presentations
SS 5.1 - Radiomics machine-learning model for the prediction of local tumour progression after thermal ablation for colorectal liver metastases
Abstract
Purpose
Knowledge about the risk of local tumor progression (LTP) after thermal ablation for colorectal liver metastases (CRLM) patients is critical to optimize percutaneous ablation results and for subsequent follow-up. The aim of this study was to develop and validate a machine-learning radiomics model to predict LTP based on pre-ablation CT in CRLM patients.
Material and methods
93 CRLM patients (143 lesions) treated by means of ablation were included and divided into a training (n=65 patients, n=102 lesions) and validation (n=28 patients, n=41 lesions) set. The validation set was independent of the training set. After manual segmentation and preprocessing, 1,316 radiomics features were extracted for each lesion. Three predictive models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features to predict two-year LTP-free survival (LTPFS). Bayesian-optimized gradient boosting with wrapper feature selection was trained on the training set and optimized with sequential model-based optimization for prediction models based on radiomics features.
Results
Median follow-up was 24 months (range 6-115). 26 patients had LTP in 32 lesions. The concordance_index in the validation set to predict LTPFS was (0.76; 95% confidence interval [CI]: 0.75-0.77, p=0.01 risk stratification) for the radiomics model, (0.60; 95%CI: 0.58-0.61, p=0.69; risk stratification) for the clinical model and (0.76; 95%CI: 0.75-0.77, p=0.01 risk stratification) for the combined model.
Conclusion
Machine learning-based predictive models incorporating pre-ablation radiomics features showed good prognostic potential and allowed significant stratification for LTPFS after thermal ablation in CRCLM patients.
Video-on-demand
SS 14.8 - Cryoablation for abdominal tumoral implants: a case series
Abstract
Purpose
Percutaneous cryoablation (CA) is widely used for the treatment of primary cancers and metastases with excellent outcomes. Results of CA for abdominal tumoral implants are limited. The purpose of this report is to show our preliminary results of CA of abdominal implants.
Material and methods
A retrospective analysis was performed of metastatic patients treated by means of CA for an abdominal tumoral implant between November 2018 and October 2019. All patients were discussed in a multidisciplinary tumour board. Complete ablation was defined as no local tumour enhancement on the first follow-up imaging. Adverse events (AE) were registered according to the SIR classification.
Results
Eight patients received CA for an abdominal tumoral implant of their renal cancer (n=3), colorectal cancer (n=2), endometrial cancer (n=2), granulosa cell cancer (n=1) and lung cancer (n=1). Abdominal implants were located retroperitoneally (n=4), in the abdominal wall (n=2), pancreas (n=1) and anterior of the stomach (n=1). Median size of the implants was 1.7 cm (R 1.1-3.7cm). Complete ablation was achieved in all lesions. One patient developed an AE grade 2 consisting of psoas muscle pain that was successfully treated with medication.
Conclusion
Cryoablation in the abdomen can be safely and effectively used for tumour control in an oligometastatic setting.
Video-on-demand
Author of 2 Presentations
SS 5.1 - Radiomics machine-learning model for the prediction of local tumour progression after thermal ablation for colorectal liver metastases (ID 567)
Abstract
Purpose
Knowledge about the risk of local tumor progression (LTP) after thermal ablation for colorectal liver metastases (CRLM) patients is critical to optimize percutaneous ablation results and for subsequent follow-up. The aim of this study was to develop and validate a machine-learning radiomics model to predict LTP based on pre-ablation CT in CRLM patients.
Material and methods
93 CRLM patients (143 lesions) treated by means of ablation were included and divided into a training (n=65 patients, n=102 lesions) and validation (n=28 patients, n=41 lesions) set. The validation set was independent of the training set. After manual segmentation and preprocessing, 1,316 radiomics features were extracted for each lesion. Three predictive models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features to predict two-year LTP-free survival (LTPFS). Bayesian-optimized gradient boosting with wrapper feature selection was trained on the training set and optimized with sequential model-based optimization for prediction models based on radiomics features.
Results
Median follow-up was 24 months (range 6-115). 26 patients had LTP in 32 lesions. The concordance_index in the validation set to predict LTPFS was (0.76; 95% confidence interval [CI]: 0.75-0.77, p=0.01 risk stratification) for the radiomics model, (0.60; 95%CI: 0.58-0.61, p=0.69; risk stratification) for the clinical model and (0.76; 95%CI: 0.75-0.77, p=0.01 risk stratification) for the combined model.
Conclusion
Machine learning-based predictive models incorporating pre-ablation radiomics features showed good prognostic potential and allowed significant stratification for LTPFS after thermal ablation in CRCLM patients.
Video-on-demand
SS 14.8 - Cryoablation for abdominal tumoral implants: a case series (ID 1070)
Abstract
Purpose
Percutaneous cryoablation (CA) is widely used for the treatment of primary cancers and metastases with excellent outcomes. Results of CA for abdominal tumoral implants are limited. The purpose of this report is to show our preliminary results of CA of abdominal implants.
Material and methods
A retrospective analysis was performed of metastatic patients treated by means of CA for an abdominal tumoral implant between November 2018 and October 2019. All patients were discussed in a multidisciplinary tumour board. Complete ablation was defined as no local tumour enhancement on the first follow-up imaging. Adverse events (AE) were registered according to the SIR classification.
Results
Eight patients received CA for an abdominal tumoral implant of their renal cancer (n=3), colorectal cancer (n=2), endometrial cancer (n=2), granulosa cell cancer (n=1) and lung cancer (n=1). Abdominal implants were located retroperitoneally (n=4), in the abdominal wall (n=2), pancreas (n=1) and anterior of the stomach (n=1). Median size of the implants was 1.7 cm (R 1.1-3.7cm). Complete ablation was achieved in all lesions. One patient developed an AE grade 2 consisting of psoas muscle pain that was successfully treated with medication.
Conclusion
Cryoablation in the abdomen can be safely and effectively used for tumour control in an oligometastatic setting.