Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score
Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhong, Jia-Wei [verfasserIn] |
|---|
| Format: |
E-Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
|---|
| Anmerkung: |
© The Author(s) 2023 |
|---|
| Übergeordnetes Werk: |
Enthalten in: Hormones and cancer - New York, NY [u.a.] : Springer, 2010, 14(2023), 1 vom: 17. Okt. |
|---|---|
| Übergeordnetes Werk: |
volume:14 ; year:2023 ; number:1 ; day:17 ; month:10 |
| Links: |
|---|
| DOI / URN: |
10.1007/s12672-023-00803-2 |
|---|
| Katalog-ID: |
SPR053437942 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR053437942 | ||
| 003 | DE-627 | ||
| 005 | 20231119064753.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231018s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s12672-023-00803-2 |2 doi | |
| 035 | |a (DE-627)SPR053437942 | ||
| 035 | |a (SPR)s12672-023-00803-2-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zhong, Jia-Wei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a © The Author(s) 2023 | ||
| 520 | |a Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. | ||
| 650 | 4 | |a Hepatocellular carcinoma |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Transarterial chemoembolization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a First response |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Individual prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Nie, Dan-Dan |4 aut | |
| 700 | 1 | |a Huang, Ji-Lan |4 aut | |
| 700 | 1 | |a Luo, Rong-Guang |4 aut | |
| 700 | 1 | |a Cheng, Qing-He |4 aut | |
| 700 | 1 | |a Du, Qiao-Ting |4 aut | |
| 700 | 1 | |a Guo, Gui-Hai |4 aut | |
| 700 | 1 | |a Bai, Liang-Liang |4 aut | |
| 700 | 1 | |a Guo, Xue-Yun |4 aut | |
| 700 | 1 | |a Chen, Yan |4 aut | |
| 700 | 1 | |a Chen, Si-Hai |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Hormones and cancer |d New York, NY [u.a.] : Springer, 2010 |g 14(2023), 1 vom: 17. Okt. |w (DE-627)621547379 |w (DE-600)2543318-0 |x 1868-8500 |7 nnns |
| 773 | 1 | 8 | |g volume:14 |g year:2023 |g number:1 |g day:17 |g month:10 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s12672-023-00803-2 |z kostenfrei |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a GBV_ILN_120 | ||
| 951 | |a AR | ||
| 952 | |d 14 |j 2023 |e 1 |b 17 |c 10 | ||
| author_variant |
j w z jwz d d n ddn j l h jlh r g l rgl q h c qhc q t d qtd g h g ghg l l b llb x y g xyg y c yc s h c shc |
|---|---|
| matchkey_str |
article:18688500:2023----::rdcinoeonrsosbfrteistasreilhmeblztofrea |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
10.1007/s12672-023-00803-2 doi (DE-627)SPR053437942 (SPR)s12672-023-00803-2-e DE-627 ger DE-627 rakwb eng Zhong, Jia-Wei verfasserin aut Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 Nie, Dan-Dan aut Huang, Ji-Lan aut Luo, Rong-Guang aut Cheng, Qing-He aut Du, Qiao-Ting aut Guo, Gui-Hai aut Bai, Liang-Liang aut Guo, Xue-Yun aut Chen, Yan aut Chen, Si-Hai aut Enthalten in Hormones and cancer New York, NY [u.a.] : Springer, 2010 14(2023), 1 vom: 17. Okt. (DE-627)621547379 (DE-600)2543318-0 1868-8500 nnns volume:14 year:2023 number:1 day:17 month:10 https://dx.doi.org/10.1007/s12672-023-00803-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 AR 14 2023 1 17 10 |
| spelling |
10.1007/s12672-023-00803-2 doi (DE-627)SPR053437942 (SPR)s12672-023-00803-2-e DE-627 ger DE-627 rakwb eng Zhong, Jia-Wei verfasserin aut Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 Nie, Dan-Dan aut Huang, Ji-Lan aut Luo, Rong-Guang aut Cheng, Qing-He aut Du, Qiao-Ting aut Guo, Gui-Hai aut Bai, Liang-Liang aut Guo, Xue-Yun aut Chen, Yan aut Chen, Si-Hai aut Enthalten in Hormones and cancer New York, NY [u.a.] : Springer, 2010 14(2023), 1 vom: 17. Okt. (DE-627)621547379 (DE-600)2543318-0 1868-8500 nnns volume:14 year:2023 number:1 day:17 month:10 https://dx.doi.org/10.1007/s12672-023-00803-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 AR 14 2023 1 17 10 |
| allfields_unstemmed |
10.1007/s12672-023-00803-2 doi (DE-627)SPR053437942 (SPR)s12672-023-00803-2-e DE-627 ger DE-627 rakwb eng Zhong, Jia-Wei verfasserin aut Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 Nie, Dan-Dan aut Huang, Ji-Lan aut Luo, Rong-Guang aut Cheng, Qing-He aut Du, Qiao-Ting aut Guo, Gui-Hai aut Bai, Liang-Liang aut Guo, Xue-Yun aut Chen, Yan aut Chen, Si-Hai aut Enthalten in Hormones and cancer New York, NY [u.a.] : Springer, 2010 14(2023), 1 vom: 17. Okt. (DE-627)621547379 (DE-600)2543318-0 1868-8500 nnns volume:14 year:2023 number:1 day:17 month:10 https://dx.doi.org/10.1007/s12672-023-00803-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 AR 14 2023 1 17 10 |
| allfieldsGer |
10.1007/s12672-023-00803-2 doi (DE-627)SPR053437942 (SPR)s12672-023-00803-2-e DE-627 ger DE-627 rakwb eng Zhong, Jia-Wei verfasserin aut Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 Nie, Dan-Dan aut Huang, Ji-Lan aut Luo, Rong-Guang aut Cheng, Qing-He aut Du, Qiao-Ting aut Guo, Gui-Hai aut Bai, Liang-Liang aut Guo, Xue-Yun aut Chen, Yan aut Chen, Si-Hai aut Enthalten in Hormones and cancer New York, NY [u.a.] : Springer, 2010 14(2023), 1 vom: 17. Okt. (DE-627)621547379 (DE-600)2543318-0 1868-8500 nnns volume:14 year:2023 number:1 day:17 month:10 https://dx.doi.org/10.1007/s12672-023-00803-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 AR 14 2023 1 17 10 |
| allfieldsSound |
10.1007/s12672-023-00803-2 doi (DE-627)SPR053437942 (SPR)s12672-023-00803-2-e DE-627 ger DE-627 rakwb eng Zhong, Jia-Wei verfasserin aut Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 Nie, Dan-Dan aut Huang, Ji-Lan aut Luo, Rong-Guang aut Cheng, Qing-He aut Du, Qiao-Ting aut Guo, Gui-Hai aut Bai, Liang-Liang aut Guo, Xue-Yun aut Chen, Yan aut Chen, Si-Hai aut Enthalten in Hormones and cancer New York, NY [u.a.] : Springer, 2010 14(2023), 1 vom: 17. Okt. (DE-627)621547379 (DE-600)2543318-0 1868-8500 nnns volume:14 year:2023 number:1 day:17 month:10 https://dx.doi.org/10.1007/s12672-023-00803-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 AR 14 2023 1 17 10 |
| language |
English |
| source |
Enthalten in Hormones and cancer 14(2023), 1 vom: 17. Okt. volume:14 year:2023 number:1 day:17 month:10 |
| sourceStr |
Enthalten in Hormones and cancer 14(2023), 1 vom: 17. Okt. volume:14 year:2023 number:1 day:17 month:10 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Hepatocellular carcinoma Transarterial chemoembolization First response Individual prediction |
| isfreeaccess_bool |
true |
| container_title |
Hormones and cancer |
| authorswithroles_txt_mv |
Zhong, Jia-Wei @@aut@@ Nie, Dan-Dan @@aut@@ Huang, Ji-Lan @@aut@@ Luo, Rong-Guang @@aut@@ Cheng, Qing-He @@aut@@ Du, Qiao-Ting @@aut@@ Guo, Gui-Hai @@aut@@ Bai, Liang-Liang @@aut@@ Guo, Xue-Yun @@aut@@ Chen, Yan @@aut@@ Chen, Si-Hai @@aut@@ |
| publishDateDaySort_date |
2023-10-17T00:00:00Z |
| hierarchy_top_id |
621547379 |
| id |
SPR053437942 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR053437942</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231119064753.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231018s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12672-023-00803-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053437942</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12672-023-00803-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhong, Jia-Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hepatocellular carcinoma</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transarterial chemoembolization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">First response</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Individual prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Dan-Dan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Ji-Lan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Rong-Guang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cheng, Qing-He</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Qiao-Ting</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Gui-Hai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bai, Liang-Liang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Xue-Yun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Si-Hai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Hormones and cancer</subfield><subfield code="d">New York, NY [u.a.] : Springer, 2010</subfield><subfield code="g">14(2023), 1 vom: 17. Okt.</subfield><subfield code="w">(DE-627)621547379</subfield><subfield code="w">(DE-600)2543318-0</subfield><subfield code="x">1868-8500</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s12672-023-00803-2</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">10</subfield></datafield></record></collection>
|
| author |
Zhong, Jia-Wei |
| spellingShingle |
Zhong, Jia-Wei misc Hepatocellular carcinoma misc Transarterial chemoembolization misc First response misc Individual prediction Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| authorStr |
Zhong, Jia-Wei |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)621547379 |
| format |
electronic Article |
| delete_txt_mv |
keep |
| author_role |
aut aut aut aut aut aut aut aut aut aut aut |
| collection |
springer |
| remote_str |
true |
| illustrated |
Not Illustrated |
| issn |
1868-8500 |
| topic_title |
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score Hepatocellular carcinoma (dpeaa)DE-He213 Transarterial chemoembolization (dpeaa)DE-He213 First response (dpeaa)DE-He213 Individual prediction (dpeaa)DE-He213 |
| topic |
misc Hepatocellular carcinoma misc Transarterial chemoembolization misc First response misc Individual prediction |
| topic_unstemmed |
misc Hepatocellular carcinoma misc Transarterial chemoembolization misc First response misc Individual prediction |
| topic_browse |
misc Hepatocellular carcinoma misc Transarterial chemoembolization misc First response misc Individual prediction |
| format_facet |
Elektronische Aufsätze Aufsätze Elektronische Ressource |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
cr |
| hierarchy_parent_title |
Hormones and cancer |
| hierarchy_parent_id |
621547379 |
| hierarchy_top_title |
Hormones and cancer |
| isfreeaccess_txt |
true |
| familylinks_str_mv |
(DE-627)621547379 (DE-600)2543318-0 |
| title |
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| ctrlnum |
(DE-627)SPR053437942 (SPR)s12672-023-00803-2-e |
| title_full |
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| author_sort |
Zhong, Jia-Wei |
| journal |
Hormones and cancer |
| journalStr |
Hormones and cancer |
| lang_code |
eng |
| isOA_bool |
true |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| author_browse |
Zhong, Jia-Wei Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai |
| container_volume |
14 |
| format_se |
Elektronische Aufsätze |
| author-letter |
Zhong, Jia-Wei |
| doi_str_mv |
10.1007/s12672-023-00803-2 |
| title_sort |
prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: tacf score |
| title_auth |
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| abstract |
Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. © The Author(s) 2023 |
| abstractGer |
Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE. © The Author(s) 2023 |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_120 |
| container_issue |
1 |
| title_short |
Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score |
| url |
https://dx.doi.org/10.1007/s12672-023-00803-2 |
| remote_bool |
true |
| author2 |
Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai |
| author2Str |
Nie, Dan-Dan Huang, Ji-Lan Luo, Rong-Guang Cheng, Qing-He Du, Qiao-Ting Guo, Gui-Hai Bai, Liang-Liang Guo, Xue-Yun Chen, Yan Chen, Si-Hai |
| ppnlink |
621547379 |
| mediatype_str_mv |
c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s12672-023-00803-2 |
| up_date |
2024-07-03T19:32:00.271Z |
| _version_ |
1803587546275381248 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR053437942</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231119064753.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231018s2023 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12672-023-00803-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053437942</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12672-023-00803-2-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Zhong, Jia-Wei</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Prediction model of no-response before the first transarterial chemoembolization for hepatocellular carcinoma: TACF score</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Previous clinic models for patients with hepatocellular carcinoma (HCC) receiving transarterial chemoembolization (TACE) mainly focused on the overall survival, whereas a simple-to-use tool for predicting the response to the first TACE and the management of risk classification before TACE are lacking. Our aim was to develop a scoring system calculated manually for these patients. A total of 437 patients with hepatocellular carcinoma (HCC) who underwent TACE treatment were carefully selected for analysis. They were then randomly divided into two groups: a training group comprising 350 patients and a validation group comprising 77 patients. Furthermore, 45 HCC patients who had recently undergone TACE treatment been included in the study to validate the model’s efficacy and applicability. The factors selected for the predictive model were comprehensively based on the results of the LASSO, univariate and multivariate logistic regression analyses. The discrimination, calibration ability and clinic utility of models were evaluated in both the training and validation groups. A prediction model incorporated 3 objective imaging characteristics and 2 indicators of liver function. The model showed good discrimination, with AUROCs of 0.735, 0.706 and 0.884 and in the training group and validation groups, and good calibration. The model classified the patients into three groups based on the calculated score, including low risk, median risk and high-risk groups, with rates of no response to TACE of 26.3%, 40.2% and 76.8%, respectively. We derived and validated a model for predicting the response of patients with HCC before receiving the first TACE that had adequate performance and utility. This model may be a useful and layered management tool for patients with HCC undergoing TACE.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hepatocellular carcinoma</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Transarterial chemoembolization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">First response</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Individual prediction</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Nie, Dan-Dan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Huang, Ji-Lan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Luo, Rong-Guang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Cheng, Qing-He</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Du, Qiao-Ting</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Gui-Hai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bai, Liang-Liang</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Guo, Xue-Yun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Yan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chen, Si-Hai</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Hormones and cancer</subfield><subfield code="d">New York, NY [u.a.] : Springer, 2010</subfield><subfield code="g">14(2023), 1 vom: 17. Okt.</subfield><subfield code="w">(DE-627)621547379</subfield><subfield code="w">(DE-600)2543318-0</subfield><subfield code="x">1868-8500</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:14</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:1</subfield><subfield code="g">day:17</subfield><subfield code="g">month:10</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://dx.doi.org/10.1007/s12672-023-00803-2</subfield><subfield code="z">kostenfrei</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_SPRINGER</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">14</subfield><subfield code="j">2023</subfield><subfield code="e">1</subfield><subfield code="b">17</subfield><subfield code="c">10</subfield></datafield></record></collection>
|
| score |
7.3996468 |