Histopathological factors predicting response to neoadjuvant therapy in gastric carcinoma

Molina, Manuel; Arco, Cristina Díaz del; Vorwald, Peter; García-Olmo, Damián; Muñoz, Lourdes Estrada; Fernández-Aceñero, María Jesús

doi:10.1007/s12094-017-1707-1

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…pre-intervention parameter, only differentiation was included. A higher differentiation grade was associated with a poorer response to chemotherapy, which is consistent with previous reports (25,26).…”

Section: Discussionsupporting

confidence: 92%

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

et al. 2021

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AimsTo develop and validate a model for predicting major pathological response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on a machine learning algorithm.MethodA total of 221 patients who underwent NAC and radical gastrectomy between February 2013 and September 2020 were enrolled in this study. A total of 144 patients were assigned to the training cohort for model building, and 77 patients were assigned to the validation cohort. A major pathological response was defined as primary tumor regressing to ypT0 or T1. Radiomic features extracted from venous-phase computed tomography (CT) images were selected by machine learning algorithms to calculate a radscore. Together with other clinical variables selected by univariate analysis, the radscores were included in a binary logistic regression analysis to construct an integrated prediction model. The data obtained for the validation cohort were used to test the predictive accuracy of the model.ResultA total of 27.6% (61/221) patients achieved a major pathological response. Five features of 572 radiomic features were selected to calculate the radscores. The final established model incorporates adenocarcinoma differentiation and radscores. The model showed satisfactory predictive accuracy with a C-index of 0.763 and good fitting between the validation data and the model in the calibration curve.ConclusionA prediction model incorporating adenocarcinoma differentiation and radscores was developed and validated. The model helps stratify patients according to their potential sensitivity to NAC and could serve as an individualized treatment strategy-making tool for AGC patients.

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Section: Discussionsupporting

confidence: 92%

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

et al. 2021

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“…Previous studies [11][12][13][14][15][16] in this treatment setting have proposed small tumor size (but not T stage), differentiated tumor and Laurén intestinal subtype as predictors for histopathologic response to chemotherapy, but there are conflicting results regarding Laurén subtype [17]. We found no other factors than TI and DI to be associated with histopathologic response, though we did not have data on tumor size.…”

Section: Discussioncontrasting

confidence: 58%

Impact of dose reduction and treatment delay of neoadjuvant chemotherapy in gastric and esophageal adenocarcinoma

Borg

Hedner

Jirström

et al. 2019

Preprint

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Background Neoadjuvant chemotherapy in resectable gastric and esophageal adenocarcinoma is often hampered by toxicities and fragile patients resulting in dose reductions and/or treatment delays. The aim of this study was to assess how these treatment modifications affect outcome. Methods A series of 63 consecutive patients treated 2008-2014 with neoadjuvant EOX (epirubicin, oxaliplatin and capecitabine) and surgical resection, with or without adjuvant treatment, were reviewed. Chemotherapy dose index (DI), i.e. the ratio of actual to planned cumulative dose, and time index (TI), i.e. the ratio of planned to actual total duration, were calculated. Associations of neoadjuvant EOX DI and TI with histopathologic response were analysed with binary logistic regression. Time to recurrence (TTR) and overall survival (OS) were estimated using Kaplan-Meier analyses. Results Statistically significant associations were found between neoadjuvant EOX TI >= 0.95 and a major histopathologic response (0-10% residual cancer cells) and between neoadjuvant EOX DI >= 0.95 and a response with 0-50% residual cancer cells. Significantly improved TTR and OS were seen in patients with a major histopathologic response. Conclusions Our results suggest that treatment delays of neoadjuvant chemotherapy in gastric or esophageal adenocarcinoma should be avoided in order to achieve a major response.

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“…However, pCR is uncommon, and our data showed that only 13% of patients achieve pCR, similar to previous reports (8.4–17.4%) [ 32 – 35 ]. A few studies have been devoted to identifying the predictive factors of pCR; possible positive factors include good differentiation of the tumor cells, higher carcinoma embryonic antigen levels and lymphocyte ratios, and lower monocyte counts [ 36 , 37 ]. Patients with these clinical features tend to benefit more from NAC.…”

Section: Discussionmentioning

confidence: 99%

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

Chen

Liú

Xiao

et al. 2021

Gastroenterology Research and Practice

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Background. Neoadjuvant chemotherapy (NAC) with subsequent radical surgery has become a popular treatment modality for advanced gastric cancer (AGC) worldwide. However, the survival benefit is still controversial, and prognostic factors remain undetermined. Aim. To identify clinical parameters that are associated with the survival of AGC patients after NAC and radical surgery and to establish a nomogram integrating multiple factors to predict survival. Methods. We reviewed the medical profiles of 215 AGC patients who received NAC and radical resection, and clinical parameters concerning NAC, surgery, pathological findings, and adjuvant chemotherapy were analyzed using a Cox regression model to determine their impact on survival. Based on these factors, a nomogram was developed and validated. Results. The overall 1-year and 3-year survival rates were 85.8% and 55.6%, respectively. Younger age (<60 years old), increased examined lymph nodes (exLNs), successful R0 resection, the achievement of pathological complete response (pCR), and acceptance of adjuvant chemotherapy were positive predictors of survival. The C-index of the established nomogram was 0.785. The area under receiver operating curve (ROC) at 1/3 years of prediction was 0.694/0.736, respectively. The model showed an ideal calibration following internal bootstrap validation. Conclusion. A nomogram predicting survival after NAC and surgery was established. Since this nomogram exhibited satisfactory and stable predictive power, it can be inferred that this is a practical tool for predicting AGC patient survival after NAC and radical surgery.

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Histopathological factors predicting response to neoadjuvant therapy in gastric carcinoma

Abstract: 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.

Cited by 12 publications

References 15 publications

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

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

Impact of dose reduction and treatment delay of neoadjuvant chemotherapy in gastric and esophageal adenocarcinoma

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

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