高分辨率全场光学相干断层扫描和动态细胞成像在一站式乳腺快速诊断诊所中的价值。

IF 2.9 2区 医学 Q2 ONCOLOGY Cancer Medicine Pub Date : 2023-09-29 DOI:10.1002/cam4.6560
Alexis Simon, Yasmina Badachi, Jacques Ropers, Isaura Laurent, Lida Dong, Elisabeth Da Maia, Agnès Bourcier, Geoffroy Canlorbe, Catherine Uzan
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引用次数: 0

摘要

背景:全场光学相干断层扫描结合动态细胞成像(D-FFOCT)是一种新的、易于使用、无损、快速的技术,可以提供足够的空间分辨率来模拟组织病理学分析。本研究的目的是评估D-FFOCT在一站式快速诊断乳腺临床中的诊断性能。方法:应用动态全场光学相干断层扫描对新鲜、未经治疗的乳腺和淋巴结活检进行检查。四位不同的读者(高级和初级放射科医生、外科医生和病理学家)在不知道最终组织学诊断或美国放射学会分类的情况下分析了样本。结果:共对152例患者进行了217次活检。乳腺活检144例,淋巴结61例,浸润性癌101例(49.27%),良性病变99例(48.29%),导管原位癌3例(1.46%),非典型性2例(0.98%),NPV:75%[0.72;0.78]。根据读者的经验创建了一个大型图像图谱和诊断算法。结论:D-FFOCT具有74%的PPV和75%的NPV,尚不准备在临床实践中用于识别癌症。这主要是因为四位主讲人对这项新技术缺乏经验和知识。通过使用诊断算法和图像图谱进行训练,放射科医生可以获得更好的结果,从而快速检测乳腺癌症和淋巴结转移。深度学习也可以被使用,随后将进行进一步的调查。
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Value of high-resolution full-field optical coherence tomography and dynamic cell imaging for one-stop rapid diagnosis breast clinic

Background

Full-field optical coherence tomography combined with dynamic cell imaging (D-FFOCT) is a new, simple-to-use, nondestructive, quick technique that can provide sufficient spatial resolution to mimic histopathological analysis. The objective of this study was to evaluate diagnostic performance of D-FFOCT for one-stop rapid diagnosis breast clinic.

Methods

Dynamic full-field optical coherence tomography was applied to fresh, untreated breast and nodes biopsies. Four different readers (senior and junior radiologist, surgeon, and pathologist) analyzed the samples without knowing final histological diagnosis or American College of Radiology classification. The results were compared to conventional processing and staining (hematoxylin–eosin).

Results

A total of 217 biopsies were performed on 152 patients. There were 144 breast biopsies and 61 lymph nodes with 101 infiltrative cancers (49.27%), 99 benign lesions (48.29%), 3 ductal in situ carcinoma (1.46%), and 2 atypias (0.98%). The diagnostic performance results were as follow: sensitivity: 77% [0.7;0.82], specificity: 64% [0.58;0.71], PPV: 74% [0.68;0.78], and NPV: 75% [0.72;0.78]. A large image atlas was created as well as a diagnosis algorithm from the readers' experience.

Conclusion

With 74% PPV and 75% NPV, D-FFOCT is not yet ready to be used in clinical practice to identify breast cancer. This is mainly explained by the lack of experience and knowledge of this new technic by the four lectors. By training with the diagnosis algorithm and the image atlas, radiologists could have better outcomes allowing quick detection of breast cancer and lymph node involvement. Deep learning could also be used, and further investigation will follow.

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来源期刊
Cancer Medicine
Cancer Medicine ONCOLOGY-
CiteScore
5.50
自引率
2.50%
发文量
907
审稿时长
19 weeks
期刊介绍: Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
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