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
A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0...A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0.1 mm. Electrical properties and detection performances including full depletion bias voltage, reverse leakage current, rise time, energy resolution and cross talk have been studied. At a bias of 80 V, leakage current in each strip is less than 15 nA, and rise time for alpha particle at 5157 keV is approximately 15 ns on both sides.Good energy resolutions have been achieved with0.65-0.80% for the junction strips and 0.85-1.00% for the ohmic strips. The cross talk is found to be negligible on both sides. The overall good performance of DSSD indicates its readiness for various nuclear physics experiments.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.
基金supported by the National Natural Science Foundation of China(Nos.U1432246,U1632136,U1432127,11375268,11635015,and 11475263)the National Basic Research Program of China(No.2013CB834404)
文摘A double-sided silicon strip detector(DSSD)with active area of 48 mm x 48 mm and thickness of300μm has been developed. Each side of DSSD consists of48 strips, each with width of 0.9 mm and inter-strip separation of 0.1 mm. Electrical properties and detection performances including full depletion bias voltage, reverse leakage current, rise time, energy resolution and cross talk have been studied. At a bias of 80 V, leakage current in each strip is less than 15 nA, and rise time for alpha particle at 5157 keV is approximately 15 ns on both sides.Good energy resolutions have been achieved with0.65-0.80% for the junction strips and 0.85-1.00% for the ohmic strips. The cross talk is found to be negligible on both sides. The overall good performance of DSSD indicates its readiness for various nuclear physics experiments.
