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Number of Negative Lymph Nodes Can Predict Survival after Postmastectomy Radiotherapy According to Different Breast Cancer Subtypes

Identifieur interne : 001A40 ( Main/Exploration ); précédent : 001A39; suivant : 001A41

Number of Negative Lymph Nodes Can Predict Survival after Postmastectomy Radiotherapy According to Different Breast Cancer Subtypes

Auteurs : San-Gang Wu [République populaire de Chine] ; Fang Peng [République populaire de Chine] ; Juan Zhou [République populaire de Chine] ; Jia-Yuan Sun [République populaire de Chine] ; Feng-Yan Li [République populaire de Chine] ; Qin Lin [République populaire de Chine] ; Huan-Xin Lin [République populaire de Chine] ; Yong Bao [République populaire de Chine] ; Zhen-Yu He [République populaire de Chine]

Source :

RBID : PMC:4317762

Abstract

Purpose: To assess the prognostic value of the number of negative lymph nodes (NLNs) in breast cancer patients with positive axillary lymph nodes after mastectomy and its predictive value for radiotherapy efficacy of different breast cancer subtypes (BCS).

Methods: The records of 1,260 breast cancer patients with positive axillary lymph nodes who received mastectomy between January 1998 and December 2007 were reviewed. The prognostic impact and predictive value of the number of NLNs with respect to locoregional recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS) were analyzed.

Results: The median follow-up time was 58 months, and 444 patients (35.2%) received postmastectomy radiotherapy (PMRT). Univariate and multivariate Cox survival analysis indicated the number of NLNs was an independent prognostic factor of LRFS, DFS, and OS. Patients with a higher number of NLNs had better survival. PMRT improved the LRFS of patients with ≤ 8 NLNs ( p < 0.001), while failing to improve the LRFS of patients with > 8 NLNs (p = 0.075). In patients with luminal A subtype, PMRT improved the LRFS, DFS, and OS of patients with ≤ 8 NLNs, but in patients with > 8 NLNs only the LRFS was improved. For patients with luminal B subtype, PMRT only improved the LRFS of patients with ≤ 8 NLNs. The number of NLNs had no predictive value for the efficacy with PMRT in Her2+ and triple-negative subtypes.

Conclusions: The number of NLNs is a prognostic indicator in patients with node-positive breast cancer, and it can predict the efficacy of PMRT according to different BCS.


Url:
DOI: 10.7150/jca.10402
PubMed: 25663944
PubMed Central: 4317762


Affiliations:

Links toward previous steps (curation, corpus...)

Le document en format XML

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<wicri:regionArea>2. Department of Radiation Oncology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510060</wicri:regionArea>
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<name sortKey="Zhou, Juan" sort="Zhou, Juan" uniqKey="Zhou J" first="Juan" last="Zhou">Juan Zhou</name>
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<nlm:aff id="A3">3. Xiamen Cancer Center, Department of Obstetrics and Gynecology, the First Affiliated Hospital of Xiamen University, Xiamen 361000, China</nlm:aff>
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<wicri:noRegion>Xiamen 361000</wicri:noRegion>
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<name sortKey="Sun, Jia Yuan" sort="Sun, Jia Yuan" uniqKey="Sun J" first="Jia-Yuan" last="Sun">Jia-Yuan Sun</name>
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<wicri:regionArea>1. Xiamen Cancer Center, Department of Radiation Oncology, the First Affiliated Hospital of Xiamen University, Xiamen 361000</wicri:regionArea>
<wicri:noRegion>Xiamen 361000</wicri:noRegion>
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<name sortKey="Lin, Huan Xin" sort="Lin, Huan Xin" uniqKey="Lin H" first="Huan-Xin" last="Lin">Huan-Xin Lin</name>
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<country xml:lang="fr">République populaire de Chine</country>
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<name sortKey="Bao, Yong" sort="Bao, Yong" uniqKey="Bao Y" first="Yong" last="Bao">Yong Bao</name>
<affiliation wicri:level="1">
<nlm:aff id="A4">4. Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer Medicine, Guangzhou 510080, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>4. Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer Medicine, Guangzhou 510080</wicri:regionArea>
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<settlement type="city">Jiangmen</settlement>
<region type="province">Guangdong</region>
</placeName>
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<name sortKey="He, Zhen Yu" sort="He, Zhen Yu" uniqKey="He Z" first="Zhen-Yu" last="He">Zhen-Yu He</name>
<affiliation wicri:level="1">
<nlm:aff id="A4">4. Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer Medicine, Guangzhou 510080, China</nlm:aff>
<country xml:lang="fr">République populaire de Chine</country>
<wicri:regionArea>4. Department of Radiation Oncology, Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center of Cancer Medicine, Guangzhou 510080</wicri:regionArea>
<placeName>
<settlement type="city">Jiangmen</settlement>
<region type="province">Guangdong</region>
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<title level="j">Journal of Cancer</title>
<idno type="eISSN">1837-9664</idno>
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<div type="abstract" xml:lang="en">
<p>
<bold>Purpose:</bold>
To assess the prognostic value of the number of negative lymph nodes (NLNs) in breast cancer patients with positive axillary lymph nodes after mastectomy and its predictive value for radiotherapy efficacy of different breast cancer subtypes (BCS).</p>
<p>
<bold>Methods:</bold>
The records of 1,260 breast cancer patients with positive axillary lymph nodes who received mastectomy between January 1998 and December 2007 were reviewed. The prognostic impact and predictive value of the number of NLNs with respect to locoregional recurrence-free survival (LRFS), disease-free survival (DFS), and overall survival (OS) were analyzed.</p>
<p>
<bold>Results:</bold>
The median follow-up time was 58 months, and 444 patients (35.2%) received postmastectomy radiotherapy (PMRT). Univariate and multivariate Cox survival analysis indicated the number of NLNs was an independent prognostic factor of LRFS, DFS, and OS. Patients with a higher number of NLNs had better survival. PMRT improved the LRFS of patients with ≤ 8 NLNs (
<italic> p</italic>
< 0.001), while failing to improve the LRFS of patients with > 8 NLNs (
<italic>p</italic>
= 0.075). In patients with luminal A subtype, PMRT improved the LRFS, DFS, and OS of patients with ≤ 8 NLNs, but in patients with > 8 NLNs only the LRFS was improved. For patients with luminal B subtype, PMRT only improved the LRFS of patients with ≤ 8 NLNs. The number of NLNs had no predictive value for the efficacy with PMRT in Her2+ and triple-negative subtypes.</p>
<p>
<bold>Conclusions:</bold>
The number of NLNs is a prognostic indicator in patients with node-positive breast cancer, and it can predict the efficacy of PMRT according to different BCS.</p>
</div>
</front>
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<author>
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<author>
<name sortKey="Tian, J" uniqKey="Tian J">J Tian</name>
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<author>
<name sortKey="Hao, Q" uniqKey="Hao Q">Q Hao</name>
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<name sortKey="Eiermann, W" uniqKey="Eiermann W">W Eiermann</name>
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<affiliations>
<list>
<country>
<li>République populaire de Chine</li>
</country>
<region>
<li>Guangdong</li>
</region>
<settlement>
<li>Jiangmen</li>
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<name sortKey="Wu, San Gang" sort="Wu, San Gang" uniqKey="Wu S" first="San-Gang" last="Wu">San-Gang Wu</name>
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<name sortKey="Bao, Yong" sort="Bao, Yong" uniqKey="Bao Y" first="Yong" last="Bao">Yong Bao</name>
<name sortKey="He, Zhen Yu" sort="He, Zhen Yu" uniqKey="He Z" first="Zhen-Yu" last="He">Zhen-Yu He</name>
<name sortKey="Li, Feng Yan" sort="Li, Feng Yan" uniqKey="Li F" first="Feng-Yan" last="Li">Feng-Yan Li</name>
<name sortKey="Lin, Huan Xin" sort="Lin, Huan Xin" uniqKey="Lin H" first="Huan-Xin" last="Lin">Huan-Xin Lin</name>
<name sortKey="Lin, Qin" sort="Lin, Qin" uniqKey="Lin Q" first="Qin" last="Lin">Qin Lin</name>
<name sortKey="Peng, Fang" sort="Peng, Fang" uniqKey="Peng F" first="Fang" last="Peng">Fang Peng</name>
<name sortKey="Sun, Jia Yuan" sort="Sun, Jia Yuan" uniqKey="Sun J" first="Jia-Yuan" last="Sun">Jia-Yuan Sun</name>
<name sortKey="Zhou, Juan" sort="Zhou, Juan" uniqKey="Zhou J" first="Juan" last="Zhou">Juan Zhou</name>
</country>
</tree>
</affiliations>
</record>

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