Osteosarcoma (OS) is an aggressive primary bone malignancy. Accurate histopathological assessment of viable versus non-viable tumor regions after neoadjuvant chemotherapy is critical for prognosis and treatment planning, yet manual evaluation remains labor-intensive, subjective, and prone to inter-observer variability. Recent advances in digital pathology have enabled automated necrosis quantification. Evaluating on test data, independently sampled on patient-level, revealed that the deep learning model performance dropped significantly from the tile-level generalization ability reported in previous studies. First, this work proposes the use of radiomic features as additional input in model training. We show that, despite that they are derived from the images, such a multimodal input effectively improved the classification performance, in addition to its added benefits in interpretability. Second, this work proposes to optimize two binary classification tasks with hierarchical classes (i.e. tumor-vs-non-tumor and viable-vs-non-viable), as opposed to the alternative ``flat'' three-class classification task (i.e. non-tumor, non-viable tumor, viable tumor), thereby enabling a hierarchical loss. We show that such a hierarchical loss, with trainable weightings between the two tasks, the per-class performance can be improved significantly. Using the TCIA OS Tumor Assessment dataset, we experimentally demonstrate the benefits from each of the proposed new approaches and their combination, setting a what we consider new state-of-the-art performance on this open dataset for this application. Code and trained models: https://github.com/YaxiiC/RadiomicsOS.git.

翻译：骨肉瘤是一种侵袭性原发性骨恶性肿瘤。新辅助化疗后，对存活与非存活肿瘤区域进行准确的病理组织学评估对预后与治疗规划至关重要，但人工评估仍存在劳动强度大、主观性强且观察者间差异显著等问题。数字病理学的最新进展已实现坏死区域的自动量化。在患者层面独立采样的测试数据上评估发现，深度学习模型的性能较先前研究中报道的瓦片层面泛化能力显著下降。首先，本研究提出在模型训练中使用放射组学特征作为附加输入。我们证明，尽管这些特征源自图像本身，此类多模态输入除了能提升模型可解释性外，还能有效改善分类性能。其次，本研究提出采用具有层级关系的二元分类任务（即肿瘤vs非肿瘤、存活vs非存活）进行优化，而非传统的“扁平化”三分类任务（即非肿瘤、非存活肿瘤、存活肿瘤），从而构建分层损失函数。研究表明，通过可训练的双任务权重配置，这种分层损失能显著提升各类别的分类性能。基于TCIA骨肉瘤肿瘤评估数据集，我们通过实验验证了所提两种新方法及其组合的优势，在该开放数据集上取得了我们认为该应用领域新的最先进性能。代码与训练模型：https://github.com/YaxiiC/RadiomicsOS.git。