Interventional radiology plays an important and increasing role in cancer treatment. Follow-up is important to be able to assess treatment success and detect locoregional and distant recurrence and recommendations for follow-up are needed. At ECIO 2018, a joint ECIO-ESOI session was organized to establish follow-up recommendations for oncologic intervention in liver, renal, and lung cancer. Treatments included thermal ablation, TACE, and TARE. In total five topics were evaluated: ablation in colorectal liver metastases (CRLM), TARE in CRLM, TACE and TARE in HCC, ablation in renal cancer, and ablation in lung cancer. Evaluated modalities were FDG-PET-CT, CT, MRI, and (contrast-enhanced) ultrasound. Prior to the session, five experts were selected and performed a systematic review and presented statements, which were voted on in a telephone conference prior to the meeting by all panelists. These statements were presented and discussed at the ECIO-ESOI session at ECIO 2018. This paper presents the recommendations that followed from these initiatives. Based on expert opinions and the available evidence, follow-up schedules were proposed for liver cancer, renal cancer, and lung cancer. FDG-PET-CT, CT, and MRI are the recommended modalities, but one should beware of false-positive signs of residual tumor or recurrence due to inflammation early after the intervention. There is a need for prospective preferably multicenter studies to validate new techniques and new response criteria. This paper presents recommendations that can be used in clinical practice to perform the follow-up of patients with liver, lung, and renal cancer who were treated with interventional locoregional therapies.

Introduction The role of phosphodiesterase type 5 inhibitors in the treatment of post-radiotherapy erectile dysfunction (ED) has not been extensively investigated. Aim To compare the efficacy and safety of on-demand 20-mg tadalafil (arm A) with the newly released tadalafil 5-mg once-a-day dosing (arm B) in patients with ED following radiotherapy for prostate cancer (PC). Methods Randomized study to receive on-demand 20-mg or once-a-day 5-mg tadalafil for 12 weeks. Main Outcome Measures Changes in the International Index of Erectile Function (IIEF) domain scores and Sexual Encounter Profile (SEP) question 2 and 3 positive response rates. Results Fifty-two out of 86 screened patients were randomized. Forty-four patients were evaluable for efficacy. A significant improvement in all domains of the IIEF was observed in both arms (P = 0.0001) with mean erectile function domain scores values of 25 and 27.1 for the 20-mg and 5-mg tadalafil, respectively (P = 0.19). SEP 2 and 3 positive response rates increased from 0% in both arms at baseline to 81% and 70% in the 20-mg arm and 90% and 73% in the 5-mg arm, respectively, at the end of treatment (P = 0.27). End of treatment global efficacy question positive answers were 86% in the 20-mg arm and 95% in the 5-mg arm (P = 0.27). Higher treatment compliance was shown in arm B (100%) as compared with arm A (86%). There was a nonstatistically significant trend toward fewer side effects in favor of the 5-mg daily dose arm. Conclusions In the study population, both tadalafil formulations generated significantly high response rates according to the outcome measures and were well tolerated. The once-a-day 5-mg dosing showed higher compliance and marginally reduced side effects, thus making it an attractive alternative to on-demand therapy for ED in post-radiotherapy PC patients.

Purpose Predicting early local tumor progression after thermal ablation treatment for colorectal liver metastases patients is critical for the decision of subsequent follow-up and treatment. Radiomics features derived from medical images show great potential for prediction and prognosis. The aim is to develop and validate a machine learning radiomics model to predict local tumor progression based on the pre-ablation CT scan of colorectal liver metastases patients. Materials and Methods Ninety patients with colorectal liver metastases (140 lesions) treated by ablation were included in the study and were randomly divided into a training (n = 63 patients/n = 94 lesions) and validation (n = 27 patients/n = 46 lesions) cohort. After manual lesion volume segmentation and preprocessing, 1593 radiomics features were extracted for each lesion. Three machine learning survival models were constructed based on (1) radiomics features, (2) clinical features and (3) a combination of clinical and radiomics features to predict local tumor progression free survival. Feature reduction and machine learning modeling were performed and optimized with sequential model-based optimization. Results Median follow-up was 24 months (range 6-115). Thirty-one (22%) lesions developed local tumor progression. The concordance index in the validation set to predict local tumor progression free survival was 0.78 (95% confidence interval [CI]: 0.77-0.79) for the radiomics model, 0.56 (95%CI: 0.55-0.57) for the clinical model and 0.79 (95%CI: 0.78-0.80) for the combined model. Conclusion A machine learning-based radiomics analysis of routine clinical CT imaging pre-ablation could act as a valuable biomarker model to predict local tumor progression with curative intent for colorectal liver metastases patients. Keywords Tomography Á X-ray computed Á Colorectal cancer Á Neoplasm metastasis Á Liver neoplasms Á Liver ablation Á Machine learning Abbreviations CRC Colorectal cancer CRLM Colorectal liver metastases ML Machine learning CI Confidence interval LTP Local tumor progression RFA Radiofrequency ablation MWA Microwave ablation