Literature DB >> 28653276

Histopathological factors predicting response to neoadjuvant therapy in gastric carcinoma.

M L Sánchez de Molina1, C Díaz Del Arco2, P Vorwald1, D García-Olmo1, L Estrada2, M J Fernández-Aceñero3.   

Abstract

BACKGROUND: Neoadjuvant therapy (NAT) is a useful therapeutic option. However, some patients respond poorly to it and can even show tumor progression. It is important to define factors that can predict response to NAT.
MATERIALS AND METHODS: This is a retrospective cohort study to define histopathological factors predicting response to NAT in gastric tubular carcinoma. This study has enrolled 80 patients receiving chemotherapy for locally advanced gastric carcinoma.
RESULTS: 44.5% of the patients were men; mean age was 64.49 years. Only 5.7% of the patients showed a complete response to therapy, 10% had grade 1, 21.4% grade 2, and 62.9% grade 3 regression. On follow-up, 43.8% of the patients showed recurrence of disease (57.1% distant metastasis) and 33.8% eventually died of it. We found a statistically significant association between response and prognosis. We found a statistically significant association between regression and perineural, vascular, and lymph vessel invasion. Logistic regression model showed that only lymph vessel invasion had independent influence. Lymph vessel invasion not only indicated lack of response to therapy, but also higher incidence of lymph node involvement in the gastrectomy specimen. DISCUSSION: Our study indicates that the presence of vascular or perineural invasion in the endoscopic biopsies and high histopathological grade predict poor response to therapy. This seems peculiar, for undifferentiated tumors are supposed to have better response to therapy.
CONCLUSION: Our study indicates that undifferentiated tumors respond worse to therapy. Furthermore, studies are necessary to define lack of response, to help avoid neoadjuvant therapy in unfavorable cases.

Entities:  

Keywords:  Gastric carcinoma; Histopathological factors; Neoadjuvant therapy; Regression grade

Mesh:

Year:  2017        PMID: 28653276     DOI: 10.1007/s12094-017-1707-1

Source DB:  PubMed          Journal:  Clin Transl Oncol        ISSN: 1699-048X            Impact factor:   3.405


  17 in total

Review 1.  Histopathological regression of gastric adenocarcinoma after neoadjuvant therapy: a critical review.

Authors:  Eduardo Henrique Cunha Neves Filho; Rosane Oliveira de Sant'Ana; Luiz Vianney Saldanha Cidrão Nunes; Adriana Pinheiro Bezerra Pires; Maria do Perpétuo Socorro Saldanha da Cunha
Journal:  APMIS       Date:  2017-01-02       Impact factor: 3.205

Review 2.  Neoadjuvant Combined-Modality Therapy for Locally Advanced Rectal Cancer and Its Future Direction.

Authors:  Mohamed E Salem; Marion Hartley; Keith Unger; John L Marshall
Journal:  Oncology (Williston Park)       Date:  2016-06       Impact factor: 2.990

3.  Nomogram for predicting pathologically complete response after neoadjuvant chemoradiotherapy for oesophageal cancer.

Authors:  Eelke Lucie Anne Toxopeus; Daan Nieboer; Joel Shapiro; Katharina Biermann; Ate van der Gaast; Carolien M van Rij; Ewout Willem Steyerberg; Joseph Jan Baptiste van Lanschot; Bas Peter Louis Wijnhoven
Journal:  Radiother Oncol       Date:  2015-06-23       Impact factor: 6.280

Review 4.  Pathogenesis of Gastric Cancer: Genetics and Molecular Classification.

Authors:  Ceu Figueiredo; M C Camargo; Marina Leite; Ezequiel M Fuentes-Pananá; Charles S Rabkin; José C Machado
Journal:  Curr Top Microbiol Immunol       Date:  2017       Impact factor: 4.291

5.  Predicting Responses to Neoadjuvant Chemotherapy in Breast Cancer: ACRIN 6691 Trial of Diffuse Optical Spectroscopic Imaging.

Authors:  Bruce J Tromberg; Zheng Zhang; Anaïs Leproux; Thomas D O'Sullivan; Albert E Cerussi; Philip M Carpenter; Rita S Mehta; Darren Roblyer; Wei Yang; Keith D Paulsen; Brian W Pogue; Shudong Jiang; Peter A Kaufman; Arjun G Yodh; So Hyun Chung; Mitchell Schnall; Bradley S Snyder; Nola Hylton; David A Boas; Stefan A Carp; Steven J Isakoff; David Mankoff
Journal:  Cancer Res       Date:  2016-08-15       Impact factor: 12.701

Review 6.  Neoadjuvant chemotherapy for resectable non-small-cell lung cancer.

Authors:  Jhanelle Gray; Eric Sommers; Miguel Alvelo-Rivera; Lary Robinson; Gerold Bepler
Journal:  Oncology (Williston Park)       Date:  2009-09       Impact factor: 2.990

Review 7.  Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review.

Authors:  J E Ryan; S K Warrier; A C Lynch; R G Ramsay; W A Phillips; A G Heriot
Journal:  Colorectal Dis       Date:  2016-03       Impact factor: 3.788

8.  Clinical evaluation of CEA, CA19-9, CA72-4 and CA125 in gastric cancer patients with neoadjuvant chemotherapy.

Authors:  Zhipeng Sun; Nengwei Zhang
Journal:  World J Surg Oncol       Date:  2014-12-29       Impact factor: 2.754

9.  What Is the Ideal Tumor Regression Grading System in Rectal Cancer Patients after Preoperative Chemoradiotherapy?

Authors:  Soo Hee Kim; Hee Jin Chang; Dae Yong Kim; Ji Won Park; Ji Yeon Baek; Sun Young Kim; Sung Chan Park; Jae Hwan Oh; Ami Yu; Byung-Ho Nam
Journal:  Cancer Res Treat       Date:  2015-10-22       Impact factor: 4.679

10.  Circulating tumor cells: A promising marker of predicting tumor response in rectal cancer patients receiving neoadjuvant chemo-radiation therapy.

Authors:  Wenjie Sun; Guichao Li; Juefeng Wan; Ji Zhu; Weiqi Shen; Zhen Zhang
Journal:  Oncotarget       Date:  2016-10-25
View more
  4 in total

1.  Nomogram for predicting pathological complete response to neoadjuvant chemotherapy in patients with advanced gastric cancer.

Authors:  Yong-He Chen; Jian Xiao; Xi-Jie Chen; Hua-She Wang; Dan Liu; Jun Xiang; Jun-Sheng Peng
Journal:  World J Gastroenterol       Date:  2020-05-21       Impact factor: 5.742

2.  Down-staging depth score could be a survival predictor for locally advanced gastric cancer patients after preoperative chemoradiotherapy.

Authors:  Ning Li; Xin Wang; Yuan Tang; Dongbin Zhao; Yihebali Chi; Lin Yang; Liming Jiang; Jun Jiang; Jinming Shi; Wenyang Liu; Hua Ren; Hui Fang; Yu Tang; Bo Chen; Ningning Lu; Hao Jing; Shunan Qi; Shulian Wang; Yueping Liu; Yongwen Song; Yexiong Li; Jing Jin
Journal:  Chin J Cancer Res       Date:  2021-08-31       Impact factor: 5.087

3.  A Machine Learning Model for Predicting a Major Response to Neoadjuvant Chemotherapy in Advanced Gastric Cancer.

Authors:  Yonghe Chen; Kaikai Wei; Dan Liu; Jun Xiang; Gang Wang; Xiaochun Meng; Junsheng Peng
Journal:  Front Oncol       Date:  2021-06-01       Impact factor: 6.244

4.  Nomogram for Predicting Survival in Advanced Gastric Cancer after Neoadjuvant Chemotherapy and Radical Surgery.

Authors:  Yonghe Chen; Dan Liu; Jian Xiao; Jun Xiang; Aihong Liu; Shi Chen; Junjie Liu; Xiansheng Hu; Junsheng Peng
Journal:  Gastroenterol Res Pract       Date:  2021-07-28       Impact factor: 2.260

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.