29/10/2026
理大研發腫瘤異質性指數 助精準識別乳癌患者藥物反應 完全緩解率達五成六
理大醫療科技及資訊學系研究團隊成功開發創新影像生物標記「腫瘤異質性指數」(Tumour Heterogeneity Index,簡稱THI),可於治療前無創評估乳癌患者對標靶藥AKT抑制劑的反應,協助醫生識別最有機會受惠的患者。
理大醫療科技及資訊學系系主任、醫學物理與智能腫瘤學講座教授蔡璟教授,聯同助理教授(研究)滕信智博士及研究團隊,運用先進影像組學技術,從動態對比增強磁力共振(DCE-MRI)數據中建立腫瘤異質性指數。DCE-MRI是一項廣泛應用於腫瘤診斷及治療規劃的影像技術,而研究團隊進一步將其應用於藥物反應評估,讓醫生可在治療前無創地預測患者的治療效果。
蔡璟教授表示:「這反映治療前精準預測療效的重要性。我們的研究提出一項有效策略,能夠識別最有機會從治療中受益的患者,甚至有望提升一些在大型臨床試驗中未達預期藥物的臨床價值。腫瘤異質性指數不僅是一項影像分析工具,更可量化腫瘤內部結構的複雜性,將過往依賴經驗判斷的評估方式,轉化為客觀及數據驅動的分析模式。」
𝐏𝐨𝐥𝐲𝐔 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐬 𝐓𝐮𝐦𝐨𝐮𝐫 𝐇𝐞𝐭𝐞𝐫𝐨𝐠𝐞𝐧𝐞𝐢𝐭𝐲 𝐈𝐧𝐝𝐞𝐱 𝐭𝐨 𝐩𝐫𝐞𝐜𝐢𝐬𝐞𝐥𝐲 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐛𝐫𝐞𝐚𝐬𝐭 𝐜𝐚𝐧𝐜𝐞𝐫 𝐩𝐚𝐭𝐢𝐞𝐧𝐭 𝐝𝐫𝐮𝐠 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞, 𝐚𝐜𝐡𝐢𝐞𝐯𝐢𝐧𝐠 𝐚 𝟓𝟔% 𝐩𝐚𝐭𝐡𝐨𝐥𝐨𝐠𝐢𝐜𝐚𝐥 𝐜𝐨𝐦𝐩𝐥𝐞𝐭𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐞 𝐫𝐚𝐭𝐞
PolyU has successfully developed an innovative imaging biomarker, the Tumour Heterogeneity Index (THI), which enables non-invasive assessment of breast cancer patients’ response to AKT inhibitors before treatment, helping clinicians identify patients most likely to benefit from therapy.
𝐏𝐫𝐨𝐟. 𝐂𝐀𝐈 𝐉𝐢𝐧𝐠, 𝐇𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐃𝐞𝐩𝐚𝐫𝐭𝐦𝐞𝐧𝐭 𝐨𝐟 𝐇𝐞𝐚𝐥𝐭𝐡 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲 𝐚𝐧𝐝 𝐈𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐜𝐬 (𝐇𝐓𝐈) 𝐚𝐧𝐝 𝐂𝐡𝐚𝐢𝐫 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐨𝐫 𝐨𝐟 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐏𝐡𝐲𝐬𝐢𝐜𝐬 𝐚𝐧𝐝 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐎𝐧𝐜𝐨𝐥𝐨𝐠𝐲 𝐚𝐭 𝐏𝐨𝐥𝐲𝐔, together with 𝐃𝐫 𝐓𝐄𝐍𝐆 𝐗𝐢𝐧𝐳𝐡𝐢, 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 𝐏𝐫𝐨𝐟𝐞𝐬𝐬𝐨𝐫 𝐨𝐟 𝐇𝐓𝐈, and their research team, applied advanced radiomics techniques to develop THI using data from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). DCE-MRI is widely used for cancer diagnosis and treatment planning. The team has further extended its application to the assessment of drug response, enabling clinicians to non-invasively predict treatment outcomes before therapy begins.
Prof. Cai Jing said, “This challenge highlights the importance of accurately predicting treatment response before therapy begins. Our study presents an effective strategy for identifying patients who are most likely to benefit from treatment, with the potential to enhance the clinical value of drugs that may not have achieved the expected outcomes in large-scale clinical trials. THI is more than an imaging analysis tool. It quantifies the complexity of a tumour’s internal structure, transforming assessment that has traditionally relied on experience into an objective, data-driven approach.”
More / 更多: https://polyu.hk/CNGwH