Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled comput...Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled computational pathology.However,there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images,which can match physician-level basic perception.In this study,we design a deep learning framework,thyroid segmentation and hierarchy fine-needle aspiration(TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image.In the TshFNA-Examiner,cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network;cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)system,by a classification neural network which is further enhanced by leveraging unlabeled data.A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied,with pixel-wise and image-wise image patches annotated.Empirically,TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches.The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871.The cancer risk classifier achieves a macro-F1-score of 0.959,macro-AUC of 0.998,and accuracy of 0.959 following TBSRTC.The corresponding metrics can be enhanced to a macro-F1-score of 0.970,macro-AUC of 0.999,and accuracy of 0.970 by leveraging informative unlabeled data.In clinical practice,TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.展开更多
目的前瞻性地评价超声内镜引导下细针抽吸活检(endoscopic ultrasound-guided fine needleaspiration,EUS-FNA)在胰腺疾病诊断中的价值及影响其诊断能力的潜在因素分析。方法 2010年9月至2011年8月间就诊于复旦大学附属中山医院内镜中心...目的前瞻性地评价超声内镜引导下细针抽吸活检(endoscopic ultrasound-guided fine needleaspiration,EUS-FNA)在胰腺疾病诊断中的价值及影响其诊断能力的潜在因素分析。方法 2010年9月至2011年8月间就诊于复旦大学附属中山医院内镜中心,经影像学诊断(CT或MRI)为胰腺病变、拟行EUS-FNA的44例患者连续性地纳入本研究。详细记录患者的年龄、性别、病变位置、病变大小、穿刺次数,评价是否获取足够样本供细胞学或组织病理学诊断及穿刺相关并发症等。结果 44例患者中,42例成功实行EUS-FNA(95.5%,42/44),病灶的平均最大直径为(44.7±18.2)mm。31例获得肉眼可见的组织条;意向性分析结果显示,34例穿刺样本(77.3%,34/44)足够用于细胞或组织病理学诊断,包括31例患者获得明确的细胞或组织病理学恶性肿瘤依据,3例诊断为胰腺炎症。病灶的位置、大小与穿刺成功率、明确病理学诊断的获得率无明显关系;而穿刺过程中获得肉眼可见组织样本病例的明确病理学诊断获得率明显高于未获得者(P=0.000)。2例患者于穿刺时发生穿刺点渗血(4.8%,2/42),通过电凝及止血夹处理好转。结论在胰腺疾病的诊断中,EUS-FNA具有良好的安全性和有效性。如何在安全的前提下获取更多的组织量用于病理学评估是提高EUS-FNA诊断能力的关键。展开更多
基金the National Natural Science Foundation of China(No.62102247)the Natural Science Foundation of Shanghai(No.23ZR1430700)。
文摘Examining thyroid fine-needle aspiration(FNA)can grade cancer risks,derive prognostic information,and guide follow-up care or surgery.The digitization of biopsy and deep learning techniques has recently enabled computational pathology.However,there is still lack of systematic diagnostic system for the complicated gigapixel cytopathology images,which can match physician-level basic perception.In this study,we design a deep learning framework,thyroid segmentation and hierarchy fine-needle aspiration(TshFNA)-Examiner to quantitatively profile the cancer risk of a thyroid FNA image.In the TshFNA-Examiner,cellular-intensive areas strongly correlated with diagnostic medical information are detected by a nuclei segmentation neural network;cell-level image patches are catalogued following The Bethesda System for Reporting Thyroid Cytopathology(TBSRTC)system,by a classification neural network which is further enhanced by leveraging unlabeled data.A cohort of 333 thyroid FNA cases collected from 2019 to 2022 from I to VI is studied,with pixel-wise and image-wise image patches annotated.Empirically,TshFNA-Examiner is evaluated with comprehensive metrics and multiple tasks to demonstrate its superiority to state-of-the-art deep learning approaches.The average performance of cellular area segmentation achieves a Dice of 0.931 and Jaccard index of 0.871.The cancer risk classifier achieves a macro-F1-score of 0.959,macro-AUC of 0.998,and accuracy of 0.959 following TBSRTC.The corresponding metrics can be enhanced to a macro-F1-score of 0.970,macro-AUC of 0.999,and accuracy of 0.970 by leveraging informative unlabeled data.In clinical practice,TshFNA-Examiner can help cytologists to visualize the output of deep learning networks in a convenient way to facilitate making the final decision.
