Comput. Biol. Med. - On the relevance of automatically selected single-voxel MRS and multimodal MRI and MRSI features for brain tumour differentiation.

Tópicos

{ featur(3375) classif(2383) classifi(1994) }
{ method(1969) cluster(1462) data(1082) }
{ signal(2180) analysi(812) frequenc(800) }
{ imag(2675) segment(2577) method(1081) }
{ extract(1171) text(1153) clinic(932) }
{ process(1125) use(805) approach(778) }
{ treatment(1704) effect(941) patient(846) }
{ design(1359) user(1324) use(1319) }
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{ howev(809) still(633) remain(590) }
{ studi(1119) effect(1106) posit(819) }
{ result(1111) use(1088) new(759) }
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{ data(1737) use(1416) pattern(1282) }
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{ patient(2315) diseas(1263) diabet(1191) }
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{ spatial(1525) area(1432) region(1030) }
{ record(1888) medic(1808) patient(1693) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

In order to evaluate the relevance of magnetic resonance (MR) features selected by automatic feature selection techniques to build classifiers for differential diagnosis and tissue segmentation two data sets containing MR spectroscopy data from patients with brain tumours were investigated. The automatically selected features were evaluated using literature and clinical experience. It was observed that a significant part of the automatically selected features correspond to what is known from the literature and clinical experience. We conclude that automatic feature selection is a useful tool to obtain relevant and possibly interesting features, but evaluation of the obtained features remains necessary.

Resumo Limpo

order evalu relev magnet reson mr featur select automat featur select techniqu build classifi differenti diagnosi tissu segment two data set contain mr spectroscopi data patient brain tumour investig automat select featur evalu use literatur clinic experi observ signific part automat select featur correspond known literatur clinic experi conclud automat featur select use tool obtain relev possibl interest featur evalu obtain featur remain necessari

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