&lt;p&gt;Predicting Recurrence in Endometrial Cancer Based on a Combination of Classical Parameters and Immunohistochemical Markers&lt;/p&gt;

Jiang, Peng; Huang, Jin Long; Deng, Ying; Hu, Jiyi; Huang, Zhen; Jia, Ming; Long, Jiaojiao; Hu, Zhuoying

doi:10.2147/cmar.s263747

Cited by 8 publications

(12 citation statements)

References 30 publications

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“…The reason for the inconsistency with our study may be that the study population was limited to ER/PR+ and HER2- breast cancer patients not receiving NAC, and p53 was simply classified as negative and positive. Additionally, in endometrial cancer, another hormone-dependent malignancy in women, ER and p53 were found to be independent predictors of recurrence in patients after surgery [ 37 ], which is in agreement with our study.…”

Section: Discussionsupporting

confidence: 92%

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

Lan

Chen

et al. 2023

JPM

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Purpose: While a pathologic complete response (pCR) is regarded as a surrogate endpoint for positive outcomes in breast cancer (BC) patients receiving neoadjuvant chemotherapy (NAC), forecasting the prognosis of non-pCR patients is still an open issue. This study aimed to create and evaluate nomogram models for estimating the likelihood of disease-free survival (DFS) for non-pCR patients. Methods: A retrospective analysis of 607 non-pCR BC patients was conducted (2012–2018). After converting continuous variables to categorical variables, variables entering the model were progressively identified by univariate and multivariate Cox regression analyses, and then pre-NAC and post-NAC nomogram models were developed. Regarding their discrimination, accuracy, and clinical value, the performance of the models was evaluated by internal and external validation. Two risk assessments were performed for each patient based on two models; patients were separated into different risk groups based on the calculated cut-off values for each model, including low-risk (assessed by the pre-NAC model) to low-risk (assessed by the post-NAC model), high-risk to low-risk, low-risk to high-risk, and high-risk to high-risk groups. The DFS of different groups was assessed using the Kaplan–Meier method. Results: Both pre-NAC and post-NAC nomogram models were built with clinical nodal (cN) status and estrogen receptor (ER), Ki67, and p53 status (all p < 0.05), showing good discrimination and calibration in both internal and external validation. We also assessed the performance of the two models in four subtypes, with the triple-negative subtype showing the best prediction. Patients in the high-risk to high-risk subgroup have significantly poorer survival rates (p < 0.0001). Conclusion: Two robust and effective nomograms were developed to personalize the prediction of DFS in non-pCR BC patients treated with NAC.

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Section: Discussionsupporting

confidence: 92%

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

Lan

Chen

et al. 2023

JPM

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“…were initially judged by primary physicians and reviewed by superior physicians. 2 Pathological type I of EC was defined as G1 and G2 endometrioid adenocarcinoma, type II was defined as G3 endometrioid adenocarcinoma and non-endometrioid adenocarcinoma including serous carcinoma, clear cell carcinoma, and other histotypes. 21 Interpretation of immunohistochemical results refers to the following steps: 5 high-power fields were randomly observed in "hottest spot" area of tumor, and 100 tumor cells were assessed per field, tumor cells with strong nuclear immunostaining were defined as positive cells, then average percentages (range 0-100%) of positive cells of the five fields for each molecular maker (Ki67, ER, PR, and P53) were calculated.…”

Section: Pathology and Immunohistochemistrymentioning

confidence: 99%

“…25 Recurrence was confirmed by more than two gynecological oncologists through physical examination, biochemical indicators, imaging examination and pathological biopsy. 2 According to the recurrence site, recurrence was divided into local recurrence and distant metastasis. Local recurrence included vaginal stump recurrence and central pelvic region recurrence.…”

Section: Pathology and Immunohistochemistrymentioning

confidence: 99%

“…1 The overall survival rate of patients is relatively high, but the prognosis of patients with recurrence is extremely poor, and the recurrence of tumor is one of the main causes of death in patients. 2 Predicting the recurrence of EC as accurately as possible and applying risk stratification of patients can not only ensure that patients receive personalized prognostic management, reduce tumor recurrence rates, but also prevent patients from being over-treated, which leads to waste of medical resources. 3 Traditionally, prognostic indicators used to predict the recurrence of EC are mostly based on classical predictors, including clinicopathological parameters and molecular prognostic markers.…”

Section: Introductionmentioning

confidence: 99%

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A Nomogram Model for Predicting Recurrence of Stage I–III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors

Jiang¹,

Wang²,

Gong³

et al. 2022

JIR

Self Cite

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The purpose of this study was to investigate the prognostic value of the inflammation-immunity-nutrition score (IINS) in patients with stage I-III endometrial cancer (EC) and establish a nomogram model to predict the recurrence of EC by combining IINS and traditional classical predictors. Methods: Seven hundred and seventy-five patients with stage I-III EC who underwent initial surgical treatment at the First Affiliated Hospital of Chongqing Medical University were included in this study as the training cohort. In the training cohort, IINS (0-3) was constructed based on preoperative C-reactive protein (CRP), lymphocytes (LYM), and albumin (ALB). Univariate and multivariate Cox regression analysis were used to screen independent predictors associated with recurrence of EC for developing the nomogram model. Internal validation of the model was performed in the training cohort by using the C-index and calibration curve, while external validation of the model was performed in another cohort (validation cohort) of 491 patients from the Second Affiliated Hospital of Chongqing Medical University. Results: IINS was successfully constructed, and survival analysis showed that patients with high IINS had a worse prognosis. Multivariate analysis showed that IINS, age, FIGO stage, pathological type, myometrial invasion, lymphatic vessel space invasion (LVSI), Ki67 expression, estrogen receptor (ER) expression, and P53 expression were significantly associated with shorter recurrencefree survival, and then a nomogram model for predicting the recurrence of EC was successfully established. The internal and external calibration curves of the model showed that the model fit well, and the C-index (0.887 in training cohort and 0.883 in validation cohort) showed that the model proposed in this study had better prediction accuracy than other prediction models. Conclusion: IINS may be a strong predictor of prognosis in patients with EC. The nomogram model incorporated into the IINS can better predict the recurrence of EC than the traditional models.

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“…Various predictive models have indicated the influence of molecular subtypes on prognostic relevance within HR-EC. [35,44,[55][56][57][58]. In one study, favorable prognosis was reported in POLE-mt and MSI tumors in HR patients with 5 year recurrence-free survival (RFS) of 93% and 95%, compared to 42% and 52% for the CNH and the CNL group, respectively (p < 0.001) [39].…”

Section: Prognostic Impact Within High-risk Ec (Hr-ec)mentioning

confidence: 99%

Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

Baiden-Amissah

Annibali

Tuyaerts

et al. 2021

Cancers

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Endometrial carcinomas (EC) are the sixth most common cancer in women worldwide and the most prevalent in the developed world. ECs have been historically sub-classified in two major groups, type I and type II, based primarily on histopathological characteristics. Notwithstanding the usefulness of such classification in the clinics, until now it failed to adequately stratify patients preoperatively into low- or high-risk groups. Pieces of evidence point to the fact that molecular features could also serve as a base for better patients’ risk stratification and treatment decision-making. The Cancer Genome Atlas (TCGA), back in 2013, redefined EC into four main molecular subgroups. Despite the high hopes that welcomed the possibility to incorporate molecular features into practice, currently they have not been systematically applied in the clinics. Here, we outline how the emerging molecular patterns can be used as prognostic factors together with tumor histopathology and grade, and how they can help to identify high-risk EC subpopulations for better risk stratification and treatment strategy improvement. Considering the importance of the use of preclinical models in translational research, we also discuss how the new patient-derived models can help in identifying novel potential targets and help in treatment decisions.

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<p>Predicting Recurrence in Endometrial Cancer Based on a Combination of Classical Parameters and Immunohistochemical Markers</p>

Cited by 8 publications

References 30 publications

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

Nomograms for Predicting Disease-Free Survival Based on Core Needle Biopsy and Surgical Specimens in Female Breast Cancer Patients with Non-Pathological Complete Response to Neoadjuvant Chemotherapy

A Nomogram Model for Predicting Recurrence of Stage I–III Endometrial Cancer Based on Inflammation-Immunity-Nutrition Score (IINS) and Traditional Classical Predictors

Endometrial Cancer Molecular Characterization: The Key to Identifying High-Risk Patients and Defining Guidelines for Clinical Decision-Making?

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