Background:　Accurate　outcome　prediction　prior　to　treatment　can　facilitate　trial　design　and　clinical　deci-sion　making　to　achieve　better　treatment　outcome.Method:　We　developed　the　DeepTOP　tool　with　deep　learning　approach　for　region-of-interest　segmenta-tion　and　clinical　outcome　prediction　using　magnetic　resonance　imaging　(MRI).　DeepTOP　was　constructed　with　an　automatic　pipeline　from　tumor　segmentation　to　outcome　prediction.　In　DeepTOP,　the　segmenta-tion　model　used　U-Net　with　a　codec　structure,　and　the　prediction　model　was　built　with　a　three-layer　con-volutional　neural　network.　In　addition,　the　weight　distribution　algorithm　was　developed　and　applied　in　the　prediction　model　to　optimize　the　performance　of　DeepTOP.Results:　A　total　of　1889　MRI　slices　from　99　patients　in　the　phase　III　multicenter　randomized　clinical　trial　(NCT01211210)　on　neoadjuvant　treatment　for　rectal　cancer　was　used　to　train　and　validate　DeepTOP.　We　systematically　optimized　and　validated　DeepTOP　with　multiple　devised　pipelines　in　the　clinical　trial,　demonstrating　a　better　performance　than　other　competitive　algorithms　in　accurate　tumor　segmentation　(Dice　coefficient:　0.79;　IoU:　0.75;　slice-specific　sensitivity:　0.98)　and　predicting　pathological　complete　response　to　chemo/radiotherapy　(accuracy:　0.789;　specificity:　0.725;　and　sensitivity:　0.812).　DeepTOP　is　a　deep　learning　tool　that　could　avoid　manual　labeling　and　feature　extraction　and　realize　automatic　tumor　segmentation　and　treatment　outcome　prediction　by　using　the　original　MRI　images.Conclusion:　DeepTOP　is　open　to　provide　a　tractable　framework　for　the　development　of　other　segmenta-tion　and　predicting　tools　in　clinical　settings.　DeepTOP-based　tumor　assessment　can　provide　a　reference　for　clinical　decision　making　and　facilitate　imaging　marker-driven　trial　design.(c)　2023　Elsevier　B.V.　All　rights　reserved.　Radiotherapy　and　Oncology　183　(2023)　109550