Whole-body PET tumor segmentation remains challenging due to limited training data and substantial tumor heterogeneity, which impact the segmentation accuracy and the clinical utility. Tumor distribution information is usually contained in patient medical records and routinely utilized in medical image interpretation, which can also be used to improve the segmentation accuracy. This study introduces a novel 3D PET/MR tumor segmentation framework which integrates tumor distribution priors extracted from medical records. The proposed Tumor Localization Priors(TLP) are generated based on medical records using Large Language Models (LLMs) and organ localization based on MRI. Furthermore, the Region-Aware Fusion Module (RAFM) is designed to fuse TLP and encoded PET information through attention within the Anatomically-Consistent Multitask Model(ACMM). Moreover, Anatomical Consistency Loss(AC Loss) is introduced, integrating tumor localization and its anatomical distribution to enhance segmentation performance. Our method achieves an 9.30% Dice improvement over the baseline nnU-Net v2, with particularly notable 23.06% gains in precision while maintaining high recall (+6.19%). Clinical evaluations confirm superior detection of both primary and metastatic lesions, alongside reduced physiological uptake artifacts.