Li Qian, Ali Zakiullah, Zakian Christian, di Pietro Massimiliano, Honing Judith, O'Donovan Maria, Flisikowski Krzysztof, Sarantos Vassilis, Pierre Guillaume, Gloriod Jerome, Drexler Wolfgang, Ntziachristos Vasilis
Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria.
Chair of Biological Imaging, Central Institute for Translational Cancer Research (TranslaTUM), School of Medicine and Health & School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
Nat Biomed Eng. 2025 Aug 6. doi: 10.1038/s41551-025-01462-0.
Endoscopic detection of oesophageal cancer (EC) often occurs late in disease development, leading to high mortality rates. Improved technologies are urgently needed for earlier EC detection. Here we research an endoscopic ultra-broadband acoustic detection scheme and introduce a 360-degree hybrid optoacoustic and optical coherence endoscopy to enable interrogation of surface and subsurface precancerous and cancerous features at a three-dimensional micrometre scale. In the following pilot tissue investigation, the dual-modal imaging features are assessed for classifying different mucosal types in Barrett's oesophagus (BE)-a precursor of EC. We find that human lesions of different grades, such as metaplastic, dysplastic and cancerous mucosa, exhibit distinctly different imaging features that are unique to the hybrid modality. Based on these features, a classification system is developed and evaluated for identifying BE neoplasia. The results show accurate BE neoplasia detection due to the complementarity of the two imaging modalities. Therefore, this study highlights the ability of the new dual-modality feature set to improve the detection performance of any of the two modalities operating in stand-alone mode and enhance diagnostic accuracy.
食管癌(EC)的内镜检测通常在疾病发展后期进行,导致死亡率很高。迫切需要改进技术以实现早期食管癌检测。在此,我们研究了一种内镜超宽带声学检测方案,并引入了360度混合光声和光学相干内窥镜,以便在三维微米尺度上对表面和亚表面的癌前和癌性特征进行检测。在接下来的初步组织研究中,评估了双模态成像特征,以对巴雷特食管(BE)(一种食管癌前体)中的不同黏膜类型进行分类。我们发现,不同等级的人类病变,如化生、发育异常和癌性黏膜,表现出明显不同的成像特征,这些特征是混合模式所特有的。基于这些特征,开发并评估了一种用于识别BE肿瘤形成的分类系统。结果表明,由于两种成像模式的互补性,能够准确检测出BE肿瘤形成。因此,本研究突出了新的双模态特征集在提高任何一种独立模式下的检测性能以及增强诊断准确性方面的能力。