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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

Radiomics approach for preoperative identification of stages Ⅰ-Ⅱand Ⅲ-Ⅳ of esophageal cancer

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Chinese Journal of Cancer Research 2018年第4期30卷 396-405页

作者： Lei WU Cong Wang Xianzheng Tan Zixuan Cheng Ke Zhao Lifen Yan Yanli Liang zaiyi liu Changhong Liang School of Medicine South China University of Technology Guangzhou 510006 China Department of Radiology Guangdong General HospitalGuangdong Academy of Medical Sciences Guangzhou 510080 China School of Automation Science and Engineering South China University of Technology Guangzhou 510641 China

Objective： To predict preoperative staging using a radiomics approach based on computed tomography （CT）images of patients with esophageal squamous cell carcinoma （ESCC）.Methods： This retrospective study included 154 patients （primary cohort： n： t 14; validation cohort： n：40） withpathologically confirmed ESCC. All patients underwent a preoperative CT scan from the neck to abdomen. Highthroughput and quantitative radiomics features were extracted from the CT images for each patient. A radiomicssignature was constructed using the least absolute shrinkage and selection operator （Lasso）. Associations betweenradiomics signature, tumor volume and ESCC staging were explored. Diagnostic performance of radiomicsapproach and tumor volume for discriminating between stages Ⅰ-Ⅱand Ⅲ-Ⅳ was evaluated and compared usingthe receiver operating characteristics （ROC） curves and net reclassification improvement （NRI）.Results= A total of 9,790 radiomics features were extracted. Ten features were selected to build a radiomicssignature after feature dimension reduction. The radiomics signature was significantly associated with ESCCstaging （P〈0.001）, and yielded a better performance for discrimination of early and advanced stage ESCC comparedto tumor volume in both the primary [area under the receiver operating characteristic curve （AUC）： 0.795 vs. 0.694,P=0.003; NRI=0.424）] and validation cohorts （AUC： 0.762 vs. 0.624, P=0.035; NRI=0.834）.Conclusions： The quantitative approach has the potential to identify stage Ⅰ-Ⅱand Ⅲ-Ⅳ ESCC beforetreatment.

关键词： Esophageal cancer tumor staging diagnostic imaging tumor volume

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

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Chinese Journal of Cancer Research 2018年第4期30卷 406-414页

作者： Zhenhui Li Dafu Zhang Youguo Dai Jian Dong Lin Wu Yajun Li Zixuan Cheng Yingying Ding zaiyi liu Department of Radiology the Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Department of Abdominal Surgery the Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Department of Pathology the Third Affiliated Hospital of Kunming Medical University Yunnan Cancer Hospital Department of Radiology Guangdong General Hospital Guangdong Academy of Medical Sciences School of Medicine South China University of Technology

Objective： The standard treatment for patients with locally advanced gastric cancer has relied on perioperativeradio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomicsfor pre-treatment computed tomography （CT） in the prediction of the pathological response of locally advancedgastric cancer with preoperative ***： Thirty consecutive patients with CT-staged Ⅱ/Ⅲ gastric cancer receiving neoadjuvant chemotherapywere enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CTduring the unenhanced, late arterial phase （AP） and portal venous phase （PP） before the administration ofneoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each *** methods were adopted during feature selection and eight methods were used in the process of building theclassifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiveroperating characteristic （ROC） curves were used to evaluate the capability of each combination of feature selectionand classification method to predict a non-good response （non-GR） based on tumor regression grade （TRG）.Results： The mean area under the curve （AUC） ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722in the PP, according to different combinations of feature selection and the classification methods. There was onlyone cross-combination machine-learning method indicating a relatively higher AUC （〉0.600） in the AP, while 12cross-combination machine-learning methods presented relatively higher AUCs （all 〉0.600） in the PP. The featureselection method adopted by a filter based on linear discriminant analysis ＋ classifier of random forest achieved asignificantly prognostic performance in the PP （AUC, 0.722~0.108; accuracy, 0.793; sensitivity, 0.636; specificity,0.889; Z=2.039; P=0.041）.Conclusions： It is possible to predict non-GR after neoadi

关键词： Gastric cancer neoadjuvant chemotherapy radiomics tomography spiral computed

Prognostic value of a modified Immunoscore in patients with stage Ⅰ-Ⅲ resectable colon cancer

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Chinese Journal of Cancer Research 2021年第3期33卷 379-390页

作者： Ke Zhao Xiaomei Wu Zhenhui Li Yingyi Wang Zeyan Xu Yajun Li Lin Wu Su Yao Yanqi Huang Changhong Liang zaiyi liu Department of Radiology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China School of Medicine South China University of TechnologyGuangzhou 510006China Department of Radiology the Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhou 510655China Department of Radiology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of Radiology Zhuhai People's HospitalZhuhai Hospital Affiliated with Jinan UniversityZhuhai 519000China Department of Pathology the Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalYunnan Cancer CenterKunming 650118China Department of Pathology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China the Second School of Clinical Medicine Southern Medical UniversityGuangzhou 510080China

Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon ***, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon ***: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the ***: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation ***: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.

关键词： Immunoscore colon cancer whole-slide image overall survival digital pathology

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

Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features

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Chinese Journal of Cancer Research 2020年第2期32卷 175-185页

作者： Xiaojun Yang Lei Wu Ke Zhao Weitao Ye Weixiao liu Yingyi Wang Jiao Li Hanxiao Li Xiaomei Huang Wen Zhang Yanqi Huang Xin Chen Su Yao zaiyi liu Changhong Liang Department of Radiology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China School of Medicine South China University of TechnologyGuangzhou 510006China Department of Radiology Guangzhou First People's HospitalGuangzhou 510180China Department of Pathology Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China

Objective:To evaluate the human epidermal growth factor receptor 2(HER2)status in patients with breast cancer using multidetector computed tomography(MDCT)-based handcrafted and deep radiomics ***:This retrospective study enrolled 339 female patients(primary cohort,n=177;validation cohort,n=162)with pathologically confirmed invasive breast *** and deep radiomics features were extracted from the MDCT images during the arterial *** the feature selection procedures,handcrafted and deep radiomics signatures and the combined model were built using multivariate logistic regression *** was assessed by measures of discrimination,calibration,and clinical usefulness in the primary cohort and validated in the validation ***:The handcrafted radiomics signature had a discriminative ability with a C-index of 0.739[95%confidence interval(95%CI):0.661-0.818]in the primary cohort and 0.695(95%CI:0.609-0.781)in the validation *** deep radiomics signature also had a discriminative ability with a C-index of 0.760(95%CI:0.690-0.831)in the primary cohort and 0.777(95%CI:0.696-0.857)in the validation *** combined model,which incorporated both the handcrafted and deep radiomics signatures,showed good discriminative ability with a C-index of 0.829(95%CI:0.767-0.890)in the primary cohort and 0.809(95%CI:0.740-0.879)in the validation ***:Handcrafted and deep radiomics features from MDCT images were associated with HER2 status in patients with breast ***,these features could provide complementary aid for the radiological evaluation of HER2 status in breast cancer.

关键词： Breast cancer human epidermal growth factor receptor 2 multidetector computed tomography radiomics deep learning

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

医学影像人工智能发展现状、困境剖析与前景展望

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中华放射学杂志 2024年第11期58卷 1365-1370页

作者：刘再毅梁长虹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广州510080

人工智能技术在医学影像领域已彰显出显著的应用潜能。然而, 实现临床实际应用仍面临诸多挑战。该文聚焦于医学影像数据标准化、模型泛化性、生物学可解释性、医学"数据孤岛"与隐私保护等层面, 对医学影像人工智能的现状及挑战予以探讨... 详细信息

人工智能技术在医学影像领域已彰显出显著的应用潜能。然而, 实现临床实际应用仍面临诸多挑战。该文聚焦于医学影像数据标准化、模型泛化性、生物学可解释性、医学"数据孤岛"与隐私保护等层面, 对医学影像人工智能的现状及挑战予以探讨。未来工作中, 需要构建高效且泛化性优良的医学影像人工智能新方法与技术, 以有力推进在医学影像领域的临床应用。

关键词：医学影像人工智能精准医学