Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of...Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.展开更多
目的应用数据挖掘技术,研究针灸治疗股骨头缺血性坏死(avascular necrosis of femoral head,ANFH)的干预方法及取穴规律。方法通过中国期刊全文数据库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(重庆维普)1990-04-01—2...目的应用数据挖掘技术,研究针灸治疗股骨头缺血性坏死(avascular necrosis of femoral head,ANFH)的干预方法及取穴规律。方法通过中国期刊全文数据库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(重庆维普)1990-04-01—2022-05-01有关针灸治疗股骨头缺血性坏死的文献作为数据来源,将其干预方法和针灸处方等建立数据库。在统计软件中运用频数统计、关联规则分析、聚类分析等方法进行数据挖掘,探求其取穴规律及干预方法的特点。结果共筛选出文献117条,纳入处方77个。(1)干预方法统计发现:以普通针刺(36.89%)为主,其次为电针(20.39%)和温针灸(19.42%)。另外电针多选用疏密波和连续波。(2)高频穴位前10位依次为:环跳、阳陵泉、足三里、肾俞、阿是穴、三阴交、悬钟、居髎、血海、承扶(占总频次的54.52%)。穴位归经前3位依次是:胆经(26.97%)、膀胱经(23.45%)、胃经(14.57%)。(3)在支持度个数为10,置信度为90%前提下,穴位组合频率前3位是:“环跳,阳陵泉”“阳陵泉,足三里”“环跳,足三里”;穴位组合关联规则分布前3位是:“阳陵泉,足三里,三阴交→环跳”“环跳,足三里,三阴交→阳陵泉”“阳陵泉,肾俞,三阴交→环跳”。(4)运用Kmeans算法+回归模拟获得4个核心新处方,对高频穴位进行系统聚类获得5个聚类方。(5)对辨证取穴情况进行统计,获得不同证型常用穴位。结论针灸治疗股骨头缺血性坏死具有独特的优势和疗效,在中医针灸理论的指导下借助数据挖掘技术分析其干预方法和取穴规律,可为股骨头缺血性坏死的治疗提供科学、合理的治疗方案。展开更多
To explore the points selection pattern of acupuncture for sleep apnea syndromes by data mining technique. Methods： Clinical literature about acupuncture therapy for sleep apnea syndromes was derived from China Natio...To explore the points selection pattern of acupuncture for sleep apnea syndromes by data mining technique. Methods： Clinical literature about acupuncture therapy for sleep apnea syndromes was derived from China National Knowledge Infrastructure （CNKI）, Wanfang Academic Journal Full-text Database （Wanfang）, Chongqing VIP Database （CQVIP）, PubMed and Science Direct between the time that databases were created and March 25th,2017. Relevant excel database was established and descriptive studies and association rules were analyzed. Results： The most frequently used point was Lianquan （CV 23） and the most frequently used meridian was the Stomach Meridian. The analysis of association rules showed that the clinical choice of acupuncture points was highly correlated, among which the combination of the highest degree of confidence and the highest degree of support was Shenmen （HT 7） and Sishencong （EX-HN 1）; Lieque （LU 7）, lianquan （CV 23） and Zhaohai （KI 6）. Conclusion： Acupuncture treatment of sleep apnea syndromes has specific selection rules of points, providing certain references for clinical and scientific research.展开更多
基金supported in part by the National Natural Science Foundation of China(82072019)the Shenzhen Basic Research Program(JCYJ20210324130209023)+5 种基金the Shenzhen-Hong Kong-Macao S&T Program(Category C)(SGDX20201103095002019)the Mainland-Hong Kong Joint Funding Scheme(MHKJFS)(MHP/005/20),the Project of Strategic Importance Fund(P0035421)the Projects of RISA(P0043001)from the Hong Kong Polytechnic University,the Natural Science Foundation of Jiangsu Province(BK20201441)the Provincial and Ministry Co-constructed Project of Henan Province Medical Science and Technology Research(SBGJ202103038,SBGJ202102056)the Henan Province Key R&D and Promotion Project(Science and Technology Research)(222102310015)the Natural Science Foundation of Henan Province(222300420575),and the Henan Province Science and Technology Research(222102310322).
文摘Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.
文摘目的应用数据挖掘技术,研究针灸治疗股骨头缺血性坏死(avascular necrosis of femoral head,ANFH)的干预方法及取穴规律。方法通过中国期刊全文数据库(CNKI)、中国学术期刊数据库(万方数据)、中文科技期刊数据库(重庆维普)1990-04-01—2022-05-01有关针灸治疗股骨头缺血性坏死的文献作为数据来源,将其干预方法和针灸处方等建立数据库。在统计软件中运用频数统计、关联规则分析、聚类分析等方法进行数据挖掘,探求其取穴规律及干预方法的特点。结果共筛选出文献117条,纳入处方77个。(1)干预方法统计发现:以普通针刺(36.89%)为主,其次为电针(20.39%)和温针灸(19.42%)。另外电针多选用疏密波和连续波。(2)高频穴位前10位依次为:环跳、阳陵泉、足三里、肾俞、阿是穴、三阴交、悬钟、居髎、血海、承扶(占总频次的54.52%)。穴位归经前3位依次是:胆经(26.97%)、膀胱经(23.45%)、胃经(14.57%)。(3)在支持度个数为10,置信度为90%前提下,穴位组合频率前3位是:“环跳,阳陵泉”“阳陵泉,足三里”“环跳,足三里”;穴位组合关联规则分布前3位是:“阳陵泉,足三里,三阴交→环跳”“环跳,足三里,三阴交→阳陵泉”“阳陵泉,肾俞,三阴交→环跳”。(4)运用Kmeans算法+回归模拟获得4个核心新处方,对高频穴位进行系统聚类获得5个聚类方。(5)对辨证取穴情况进行统计,获得不同证型常用穴位。结论针灸治疗股骨头缺血性坏死具有独特的优势和疗效,在中医针灸理论的指导下借助数据挖掘技术分析其干预方法和取穴规律,可为股骨头缺血性坏死的治疗提供科学、合理的治疗方案。
基金supported by Hunan Provincial Innovation Foundation for Postgraduate,No.CX2017B427~~
文摘To explore the points selection pattern of acupuncture for sleep apnea syndromes by data mining technique. Methods： Clinical literature about acupuncture therapy for sleep apnea syndromes was derived from China National Knowledge Infrastructure （CNKI）, Wanfang Academic Journal Full-text Database （Wanfang）, Chongqing VIP Database （CQVIP）, PubMed and Science Direct between the time that databases were created and March 25th,2017. Relevant excel database was established and descriptive studies and association rules were analyzed. Results： The most frequently used point was Lianquan （CV 23） and the most frequently used meridian was the Stomach Meridian. The analysis of association rules showed that the clinical choice of acupuncture points was highly correlated, among which the combination of the highest degree of confidence and the highest degree of support was Shenmen （HT 7） and Sishencong （EX-HN 1）; Lieque （LU 7）, lianquan （CV 23） and Zhaohai （KI 6）. Conclusion： Acupuncture treatment of sleep apnea syndromes has specific selection rules of points, providing certain references for clinical and scientific research.
