van Breugel Suse J, Low Irene, Christie Mary L, Pokorny Morgan R, Nagarajan Ramya, Holtkamp Hannah U, Srinivasa Komal, Amirapu Satya, Nieuwoudt Michel K, Simpson M Cather, Zargar-Shoshtari Kamran, Aguergaray Claude
The Photon Factory, University of Auckland, Auckland, New Zealand.
School of Chemical Sciences, University of Auckland, Auckland, New Zealand.
J Biophotonics. 2023 May;16(5):e202200334. doi: 10.1002/jbio.202200334. Epub 2023 Feb 12.
Prostate cancer (PCa) is a significant healthcare problem worldwide. Current diagnosis and treatment methods are limited by a lack of precise in vivo tissue analysis methods. Real-time cancer identification and grading could dramatically improve current protocols. Here, we report the testing of a thin optical probe using Raman spectroscopy (RS) and classification methods to detect and grade PCa accurately in real-time. We present the first clinical trial on fresh ex vivo biopsy cores from an 84 patient cohort. Findings from 2395 spectra measured on 599 biopsy cores show high accuracy for diagnosing and grading PCa. We can detect clinically significant PCa from benign and clinically insignificant PCa with 90% sensitivity and 80.2% specificity. We also demonstrate the ability to differentiate cancer grades with 90% sensitivity and specificity ≥82.8%. This work demonstrates the utility of RS for real-time PCa detection and grading during routine transrectal biopsy appointments.
前列腺癌（PCa）是全球范围内一个重大的医疗保健问题。当前的诊断和治疗方法受到体内组织精确分析方法匮乏的限制。实时癌症识别和分级能够显著改善现有方案。在此，我们报告了使用拉曼光谱（RS）的细光学探头及分类方法进行测试，以实时准确检测和分级前列腺癌。我们展示了针对来自84名患者队列的新鲜离体活检组织芯的首次临床试验。在599个活检组织芯上测量的2395个光谱的结果显示出诊断和分级前列腺癌的高准确性。我们能够以90%的灵敏度和80.2%的特异性从良性和临床意义不显著的前列腺癌中检测出具有临床意义的前列腺癌。我们还展示了以90%的灵敏度和≥82.8%的特异性区分癌症分级的能力。这项工作证明了拉曼光谱在常规经直肠活检过程中用于实时前列腺癌检测和分级的实用性。
