Accurate prognostic stratification for patients with colorectal liver metastases (CRLM) remains a clinical challenge due to the limited discriminative power of conventional models. This study developed and evaluated CRLMPred, a machine learning framework that integrates preoperative metabolic host factors and radiomic tumor features derived from contrast-enhanced CT to predict postoperative recurrence at 3,6, and 12 months. To ensure clinical applicability and avoid methodological bias, all input variables were restricted to preoperative baseline parameters, thereby eliminating the risk of temporal data leakage that inflated performance in preliminary models (AUC$>0.98$). The 3 -month prediction model demonstrated optimal performance, with an area under the curve (AUC) of 0.837 (95 % CI:0.674-0.955), a high negative predictive value (NPV) of 97.4 %, and stable cross-validation metrics. Decision curve analysis confirmed consistent net clinical benefit across a range of decision thresholds. Beyond its predictive accuracy, this study emphasizes the importance of rigorous model design to avoid data leakage and introduces a reproducible pipeline for reliable, translational deployment of artificial intelligence in clinical oncology. CRLM-Pred offers a promising clinical tool to support personalized surveillance and treatment planning in patients with CRLM.

In the field of HER2 expression level assessment for breast cancer, clinical evaluations often rely on the synergistic analysis of both H&E and IHC stained images. However, acquiring dual-modality images for the same patient is frequently hindered by complex clinical workflows and high costs, resulting in missing modalities. To address this challenge, we propose an adaptive bimodal input prediction framework that flexibly supports both single-modality and dual-modality inputs. This framework employs a dynamic branch selection mechanism to overcome the rigid dependency of existing models on complete inputs, enabling accurate predictions using either H&E or IHC images alone, while retaining the ability for joint inference when both modalities are available. The core technical innovations include: a missing modality branch selector that dynamically activates either a modality completion process or an end-to-end dual-modality inference pipeline based on the available input; and a cross-modal generative adversarial network (CM-GAN) that facilitates context-aware reconstruction of the missing modality in the feature space. This design improves the prediction accuracy from 71.44% to 94.25% when using single-modality H&E images, significantly mitigating performance degradation caused by incomplete information. Experimental results demonstrate that the proposed framework achieves a prediction accuracy of 95.09% with full dual-modality input and maintains a high reliability of 90.28% under single-modality conditions. By adopting this ''dual-modality preferred, single-modality compatible'' flexible architecture, healthcare institutions can achieve near dual-modality accuracy without mandating synchronized acquisition of both image types. This is particularly valuable for regions with limited IHC staining infrastructure, offering a cost-effective clinical solution and substantially enhancing the accessibility of HER2 expression level assessment.