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Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

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Chinese Medical Journal 2024年第4期137卷 421-430页

作者： Ming Cai Ke Zhao Lin Wu yanqi huang Minning Zhao Qingru Hu Qicong Chen Su Yao Zhenhui Li Xinjuan Fan Zaiyi Liu Department of Radiology Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application GuangzhouGuangdong 510080China Guangdong Cardiovascular Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Medical Research Institute Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Department of Pathology The Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunmingYunnan 650118China The Second School of Clinical Medicine Southern Medical UniversityGuangzhouGuangdong 510515China Institute of Computing Science and Technology Guangzhou UniversityGuangzhouGuangdong 510006China Department of Pathology Guangdong Provincial People’s Hospital(Guangdong Academy of Medical Sciences)Southern Medical UniversityGuangzhouGuangdong 510080China Department of Pathology The Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhouGuangdong 510655China

Background:Artificial intelligence(AI)technology represented by deep learning has made remarkable achievements in digital pathology,enhancing the accuracy and reliability of diagnosis and prognosis *** spatial distribution of CD3^(+)and CD8^(+)T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer(CRC).This study aimed to investigate CD3_(CT)(CD3^(+)T cells density in the core of the tumor[CT])prognostic ability in patients with CRC by using AI ***:The study involved the enrollment of 492 patients from two distinct medical centers,with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation *** facilitate tissue segmentation and T-cells quantification in whole-slide images(WSIs),a fully automated workflow based on deep learning was *** the completion of tissue segmentation and subsequent cell segmentation,a comprehensive analysis was ***:The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3_(CT) and CD3-CD8(the combination of CD3^(+)and CD8^(+)T cells density within the CT and invasive margin)in predicting mortality(C-index in training cohort:0.65 vs.0.64;validation cohort:0.69 vs.0.69).The CD3_(CT) was confirmed as an independent prognostic factor,with high CD3_(CT) density associated with increased overall survival(OS)in the training cohort(hazard ratio[HR]=0.22,95%confidence interval[CI]:0.12–0.38,P<0.001)and validation cohort(HR=0.21,95%CI:0.05–0.92,P=0.037).Conclusions:We quantify the spatial distribution of CD3^(+)and CD8^(+)T cells within tissue regions in WSIs using AI *** CD3_(CT) confirmed as a stage-independent predictor for OS in CRC ***,CD3_(CT) shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.

关键词： Colorectal cancer Artificial intelligence Deep learning Digital pathology Prognosis Immune cells CD3 CD8 TME

整合影像组学于多组学研究：揭开肿瘤精准诊疗新视角

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中国普外基础与临床杂志 2024年第3期31卷 257-264页

作者：黄燕琪刘再毅南方医科大学附属广东省人民医院(广东省医学科学院)放射科广东省医学影像智能分析与应用重点实验室

肿瘤是威胁全人类健康的重大公共卫生问题。近年来，随着基于大数据的生物信息学的高速发展，各种组学研究方法在肿瘤诊治方面展现了重要潜能。其中，影像组学的兴起扩大了医学影像在肿瘤精准诊疗领域的应用范畴。然而，由于肿瘤的发生... 详细信息

肿瘤是威胁全人类健康的重大公共卫生问题。近年来，随着基于大数据的生物信息学的高速发展，各种组学研究方法在肿瘤诊治方面展现了重要潜能。其中，影像组学的兴起扩大了医学影像在肿瘤精准诊疗领域的应用范畴。然而，由于肿瘤的发生和发展涉及多层次的多种因素，单一组学信息仍难以满足推进肿瘤精准诊疗策略发展的需求，多组学研究成为了新兴趋势。其中，将影像组学与其他组学整合的研究思路为肿瘤个体化诊疗提供了全新的视角，但同时也面临着诸多挑战，仍有必要引入更多新技术、新理论，加速肿瘤精准诊疗的进程。

关键词：影像组学多组学研究人工智能肿瘤精准诊疗

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家族性腺瘤性息肉病的临床特征分析

中国现代医生 2024年第2期62卷 5-9页

作者：黄艳齐陈柯豫张玲利郑州大学第一附属医院消化内科河南郑州450052

目的探究家族性腺瘤性息肉病(familial adenomatous polyposis,FAP)临床特征。方法回顾分析郑州大学第一附属医院2011年1月至2021年12月100例被确诊为家族性腺瘤性息肉病患者的临床资料。结果患者主要临床表现依次为血便(44.0%),腹痛... 详细信息

目的探究家族性腺瘤性息肉病(familial adenomatous polyposis,FAP)临床特征。方法回顾分析郑州大学第一附属医院2011年1月至2021年12月100例被确诊为家族性腺瘤性息肉病患者的临床资料。结果患者主要临床表现依次为血便(44.0%),腹痛(40.0%),大便性状改变(25.0%),腹胀(18.0%)和腹泻(17.0%)等;腺瘤类型以绒毛管状腺瘤(44.9%)和管状腺瘤(32.1%)为主;肠外表现包括硬纤维瘤3例,骨瘤2例,脂肪瘤1例;伴发胃息肉47例,病理类型多为胃底腺息肉(34.0%);伴发十二指肠息肉18例,十二指肠降部多发(61.1%);21例患者发生癌变,癌变中位年龄为39.6岁,直肠部位多发;将癌变与未癌变患者性别、有无家族史、发病年龄、腺瘤数目、腺瘤直径和腺瘤病理类型进行统计学比较,发现两组患者的性别、平均发病年龄、不同年龄阶段、腺瘤数目、腺瘤直径和腺瘤病理类型差异有统计学意义(P<0.05)。结论家族性腺瘤性息肉病主要表现为血便及腹痛等,癌变率高,危险因素包括患者发病年龄、腺瘤数目直径及病理类型。病理类型以绒毛管状腺瘤和管状腺瘤为主。目前预防性结直肠切除术仍是治疗FAP最有效的方式;无论患者选择何种术式,均需严格内镜下随访。

关键词：家族性腺瘤性息肉病临床特征病理类型癌变治疗方式

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage Ⅰ-Ⅲ colorectal cancer

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Chinese Journal of Cancer Research 2022年第1期34卷 40-52页

作者： yanqi huang Lan He Zhenhui Li Xin Chen Chu Han Ke Zhao Yuan Zhang Jinrong Qu Yun Mao Changhong Liang Zaiyi Liu The Second School of Clinical Medicine Southern Medical UniversityGuangzhou 510515China Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Department of Radiology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of Radiology Guangzhou First People’s HospitalSchool of MedicineSouth China University of TechnologyGuangzhou 510180China Department of Radiology the Affiliated Cancer Hospital of Zhengzhou University&Henan Cancer HospitalZhengzhou 450003China Department of Radiology the First Affiliated Hospital of Chongqing Medical UniversityChongqing 400042China

Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem ***: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS ***: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical ***: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively pred

关键词： Radiomics tumor ecosystem spatial heterogeneity survival prediction colorectal cancer

Radiomics-based predictive risk score: A scoring system for preoperatively predicting risk of lymph node metastasis in patients with resectable non-small cell lung cancer

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Chinese Journal of Cancer Research 2019年第4期31卷 641-652页

作者： Lan He yanqi huang Lixu Yan Junhui Zheng Changhong Liang Zaiyi Liu Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Department of Pathology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China

Objective: To develop and validate a radiomics-based predictive risk score(RPRS) for preoperative prediction of lymph node(LN) metastasis in patients with resectable non-small cell lung cancer(NSCLC).Methods: We retrospectively analyzed 717 who underwent surgical resection for primary NSCLC with systematic mediastinal lymphadenectomy from October 2007 to July 2016. By using the method of radiomics analysis, 591 computed tomography(CT)-based radiomics features were extracted, and the radiomics-based classifier was constructed. Then, using multivariable logistic regression analysis, a weighted score RPRS was derived to identify LN metastasis. Apparent prediction performance of RPRS was assessed with its calibration,discrimination, and clinical ***: The radiomics-based classifier was constructed, which consisted of 13 selected radiomics *** models demonstrated that radiomics-based classifier, age group, tumor diameter, tumor location, and CT-based LN status were independent predictors. When we assigned the corresponding score to each variable,patients with RPRSs of 0-3, 4-5, 6, 7-8, and 9 had distinctly very low(0%-20%), low(21%-40%), intermediate(41%-60%), high(61%-80%), and very high(81%-100%) risks of LN involvement, respectively. The developed RPRS showed good discrimination and satisfactory calibration (C-index: 0.785, 95% confidence interval(95% CI):0.780-0.790)Additionally, RPRS outperformed the clinicopathologic-based characteristics model with net reclassification index(NRI) of 0.711(95% CI: 0.555-0.867).Conclusions: The novel clinical scoring system developed as RPRS can serve as an easy-to-use tool to facilitate the preoperatively individualized prediction of LN metastasis in patients with resectable NSCLC. This stratification of patients according to their LN status may provide a basis for individualized treatment.

关键词： Lymph node radiomics risk score CT non-small cell lung cancer

A radiomics prognostic scoring system for predicting progression-free survival in patients with stageⅣnon-small cell lung cancer treated with platinum-based chemotherapy

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Chinese Journal of Cancer Research 2021年第5期33卷 592-605页

作者： Lan He Zhenhui Li Xin Chen yanqi huang Lixu Yan Changhong Liang Zaiyi Liu Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Department of Radiology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of Radiology Guangzhou First People’s HospitalSchool of MedicineSouth China University of TechnologyGuangzhou 510120China The Second School of Clinical Medicine Southern Medical UniversityGuangzhou 510515China Department of Pathology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China

Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based ***:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each ***,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression ***,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS ***:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of *** RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds

关键词： Non-small cell lung cancer radiomics prognostic scoring system progression-free survival platinum-based chemotherapy

Unexpected divergence in magnetoreceptor MagR from robin and pigeon linked to two sequence variations

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Zoological Research 2024年第1期45卷 69-78页

作者： Shun Wang Peng Zhang Fan Fei Tianyang Tong Xiujuan Zhou Yajie Zhou Jing Zhang Mengke Wei yanqi Zhang Lei Zhang Yulong huang Lin Zhang Xin Zhang Tiantian Cai Can Xie Institutes of Physical Science and Information Technology Anhui UniversityHefeiAnhui 230039China High Magnetic Field Laboratory Hefei Institutes of Physical ScienceChinese Academy of SciencesScience IslandHefeiAnhui 230031China Science Island Branch of Graduate School University of Science and Technology of ChinaHefeiAnhui 230036China Department of Anatomy Anhui Medical UniversityHefeiAnhui 230032China Department of Mechanical and Aerospace Engineering University at BuffaloState University of New YorkBuffaloNY 14260 USA International Magnetobiology Frontier Research Center Science IslandHefeiAnhui 230031China

Birds exhibit extraordinary mobility and remarkable navigational skills,obtaining guidance cues from the Earth’s magnetic field for orientation and long-distance *** species also show tremendous diversity in navigation strategies,with considerable differences even within the same taxa and among individuals from the same *** highly conserved iron and iron-sulfur cluster binding magnetoreceptor(MagR)protein is suggested to enable animals,including birds,to detect the geomagnetic field and navigate ***,MagR is also implicated in other functions,such as electron transfer and biogenesis of iron-sulfur clusters,raising the question of whether variability exists in its biochemical and biophysical features among species,particularly *** the current study,we conducted a comparative analysis of MagR from two different bird species,including the migratory European robin(Erithacus rubecula)and the homing pigeon(Columba livia).Sequence alignment revealed an extremely high degree of similarity between the MagRs of these species,with only three sequence ***,two of these variations underpinned significant differences in metal binding capacity,oligomeric state,and magnetic *** findings offer compelling evidence for the marked differences in MagR between the two avian species,potentially explaining how a highly conserved protein can mediate such diverse functions.

关键词： Homing and migration Animal navigation Magnetoreceptor(MagR) Diverse navigation pattern Conserved protein

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer

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Chinese Journal of Cancer Research 2020年第1期32卷 62-71页

作者： Yexing Li Zixuan Cheng Olivier Gevaert Lan He yanqi huang Xin Chen Xiaomei huang Xiaomei Wu Wen Zhang Mengyi Dong Jia huang Yucun huang Ting Xia Changhong Liang Zaiyi Liu Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Graduate College Shantou University Medical CollegeShantou 515041China School of Medicine South China University of TechnologyGuangzhou 510006China Stanford Center for Biomedical Informatics Research Department of Medicineand Department of Biomedical Data ScienceStanford UniversityCalifornia 94305USA Graduate College Southern Medical UniversityGuangzhou 510515China Department of Radiology Guangzhou First People’s HospitalGuangzhou 510180China Department of Radiology Zhuhai Hospital of Traditional Chinese and Western MedicineZhuhai 519000China Department of Radiology The Fifth People’s Hospital of ZhuhaiZhuhai 519055China

Objective: To develop and validate a computed tomography(CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2(HER2) status in patients with gastric ***: This retrospective study included 134 patients with gastric cancer(HER2-negative: n=87;HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training(n=94) and validation(n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator(LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation ***: The radiomics signature was significantly associated with HER2 status in both training(P<0.001) and validation(P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen(CEA) level demonstrated good discriminative performance for HER2 status prediction,with an area under the curve(AUC) of 0.799 [95% confidence interval(95% CI): 0.704-0.894] in the training cohort and 0.771(95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was ***: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.

关键词： Gastric cancer human epidermal growth factor receptor 2 radiomics X ray computed tomography

Individualized prediction of perineural invasion in colorectal cancer: development and validation of a radiomics prediction model

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Chinese Journal of Cancer Research 2018年第1期30卷 40-50页

作者： yanqi huang Lan He Di Dong Caiyun Yang Cuishan Liang Xin Chen Zelan Ma Xiaomei huang Su Yao Changhong Liang Jie Tian Zaiyi Liu Department of Radiology Guangdong General Hospital Guangdong Academy of Medical Sciences School of Medicine South China University of Technology Key Laboratory of Molecular Imaging Chinese Academy of Sciences Department of Radiology the Affiliated Guangzhou First People's Hospital Guangzhou Medical University Department of Pathology Guangdong General Hospital Guangdong Academy of Medical Sciences

Objective： To develop and validate a radiomics prediction model for individualized prediction of perineural invasion（PNI） in colorectal cancer（CRC）.Methods： After computed tomography（CT） radiomics features extraction, a radiomics signature was constructed in derivation cohort（346 CRC patients）. A prediction model was developed to integrate the radiomics signature and clinical candidate predictors [age, sex, tumor location, and carcinoembryonic antigen（CEA） level]. Apparent prediction performance was assessed. After internal validation, independent temporal validation（separate from the cohort used to build the model） was then conducted in 217 CRC patients. The final model was converted to an easy-to-use ***： The developed radiomics nomogram that integrated the radiomics signature and CEA level showed good calibration and discrimination performance [Harrell＇s concordance index（c-index）： 0.817; 95% confidence interval（95% CI）： 0.811–0.823]. Application of the nomogram in validation cohort gave a comparable calibration and discrimination（c-index： 0.803; 95% CI： 0.794–0.812）.Conclusions： Integrating the radiomics signature and CEA level into a radiomics prediction model enables easy and effective risk assessment of PNI in CRC. This stratification of patients according to their PNI status may provide a basis for individualized auxiliary treatment.

关键词： Colorectal cancer perineural invasion prediction model radiomics nomogram