Soft tissue sarcoma (STS) patients with pulmonary metastases (PM) experience a significant decrease in 5-year survival rates, ranging from 15 % to 50 % compared to 81 % without metastases. Despite this clinical challenge, there is a lack of consensus regarding the optimal treatment approach for PM in STS. To address this, a convolutional neural network (CNN) was developed, utilising transfer learning from a MED3D base model with added custom layers. The CNN aimed to predict surgical treatment response and extract relevant radiological parameters via attribution maps from the CT images of PMs.  The CNN demonstrated promising performance with a balanced distribution of true positive and true negative predictions, giving precision, recall and F1-scores of 0.8. However, the limited size of the data set calls for caution in interpreting the statistical validity of these results.  The evaluation of the attribution maps revealed the classifier assigning significance to regions lacking anatomical relevance, except for one region – the dorsal lobe near a metastasis – showing lower blood vessel density. Nonetheless, no definitive pathological conclusions can be drawn from this observation currently.  In conclusion, this study presents a CNN-based approach for predicting surgical treatment response in STS patients with PMs. However, the small data set warrants further validation and exploration of clinical implications associated with the identified regions of significance.