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166 Ho-microspheres have recently been approved for clinical use for hepatic radioembolization in the European Union. The aim of this study was to investigate the absorbed dose-response relationship and its association with overall survival for 166 Ho radioembolization in patients with liver metastases. Methods: Patients treated in the HEPAR I and II studies who underwent an 18 F-FDG PET/CT scan at baseline, a posttreatment 166 Ho SPECT/CT scan, and another 18 F-FDG PET/CT scan at the 3-mo follow-up were included for analysis. The posttreatment 166 Ho-microsphere activity distributions were estimated with quantitative SPECT/ CT reconstructions using a quantitative Monte Carlo-based method. The response of each tumor was based on the change in total lesion glycolysis (TLG) between baseline and follow-up and was placed into 1 of 4 categories, according to the PERCIST criteria, ranging from complete response to progressive disease. Patient-level response was grouped according to the average change in TLG per patient. The absorbed dose-response relationship was assessed using a linear mixed model to account for correlation of tumors within patients. Median overall survival was compared between patients with and without a metabolic liver response, using a log-rank test. Results: Thirty-six patients with a total of 98 tumors were included. The relation between tumor-absorbed dose and both tumor-level and patient-level response was explored. At a tumor level, a significant difference in geometric mean absorbed dose was found between complete response (232 Gy; 95% confidence interval [CI], 178-303 Gy; n 5 32) and stable disease (147 Gy; 95% CI, 113-191 Gy; n 5 28) (P 5 0.01) and between complete response and progressive disease (117 Gy; 95% CI, 87-159 Gy; n 5 21) (P 5 0.0008). This constitutes a robust absorbed dose-response relationship. At a patient level, a significant difference was found between patients with complete or partial response (210 Gy; 95% CI, 161-274 Gy; n 5 13) and patients with progressive disease (116 Gy; 95% CI, 81-165 Gy; n 5 9) (P 5 0.01). Patients were subsequently grouped according to their average change in TLG. Patients with an objective response (complete or partial) exhibited a significantly higher overall survival than nonresponding patients (stable or progressive disease) (median, 19 mo vs. 7.5 mo; log-rank, P 5 0.01). Conclusion: These results confirm a significant absorbed dose-response relationship in 166 Ho radioembolization. Treatment response is associated with a higher overall survival.

Introduction: Prognosis prediction is central in treatment decision making and quality of life for nonsmall cell lung cancer (NSCLC) patients. However, conventional computed tomography (CT) related prognostic factors may not apply to the challenging stage III NSCLC group. The aim of this systematic review was therefore to identify and evaluate CT-related prognostic factors for overall survival (OS) of stage III NSCLC. Methods: The Medline, Embase, and Cochrane electronic databases were searched. After study selection, risk of bias was estimated for the included studies. Meta-analysis of univariate results was performed when sufficient data were available. Results: 1595 of the 11,996 retrieved records were selected for full text review, leading to inclusion of 65 studies that reported data of 144,513 stage III NSCLC patients andcompromising 26 unique CT-related prognostic factors. Relevance and validity varied substantially, few studies had low relevance and validity. Only four studies evaluated the added value of new prognostic factors compared with recognized clinical factors. Included studies suggested gross tumor volume (meta-analysis: HR = 1.22, 95%CI: 1.05-1.42), tumor diameter, nodal volume, and pleural effusion, are prognostic in patients treated with chemoradiation. Clinical T-stage and location (right/left) were likely not prognostic within stage III NSCLC. Inconclusive are several radiomic features, tumor volume, atelectasis, location (pulmonary lobes, central/peripheral), interstitial lung abnormalities, great vessel invasion, pit-fall sign, and cavitation. Conclusions: Tumor-size and nodal size-related factors are prognostic for OS in stage III NSCLC. Future studies should carefully report study characteristics and contrast factors with guideline recognized factors to improve evidence evaluation and validation.

Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications in the setting of observational data, deep learning methods must be made compatible with the required causal assumptions. We present a scenario with real-world medical images (CT-scans of lung cancer) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity), respectively. When a deep network would use all the information available in the image to predict survival, it would condition on the collider and thereby introduce bias in the estimation of the treatment effect. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of linear independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long-standing goal of personalized medicine supported by artificial intelligence.