Her2-Positive and Microsatellite Instability Status in Gastric Cancer—Clinicopathological Implications

Bermúdez, Ana Isabel Sánchez; Arranz-Salas, Isabel; Mercado, Silvia C; López-Villodres, Juan Antonio; Gonzalez, V. Negron; Ríus, Francisca; Ortega, María Victoria; Alba, Carmen; Hierro, Isabel del; Bermúdez, Diego

doi:10.3390/diagnostics11060944

Cited by 18 publications

(13 citation statements)

References 61 publications

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“…Аналогичные результаты были показаны в работе A. Bermúdez и соавт. [25,26]. Нами было продемонстрировано, что наличие МСН ассоциировано с более низкой степенью дифференцировки опухоли.…”

Section: материалы и методыunclassified

“…Нами было продемонстрировано, что наличие МСН ассоциировано с более низкой степенью дифференцировки опухоли. Стоит отметить, что такая взаимосвязь ранее описана в литературе не была, что может быть обусловлено преобладанием в нашей выборке пациентов с диффузным подтипом опухоли по классификации Lauren, который характеризуется более низкой степенью дифференцировки [25,26].…”

Section: материалы и методыunclassified

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Molecular markers as predictors of response to perioperative chemotherapy in locally advanced gastric cancer

et al. 2023

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Introduction. Perioperative FLOT chemotherapy has improved prognosis in patients with locally advanced resectable gastric cancer (GC). However, in 80 % of cases, the tumor is resistant to the therapy, resulting in unnecessary toxicity and delayed surgical treatment.Aim. Evaluation of clinico-morphological patterns of microsatellite instability, HER2 gene amplification, changes in gene copy number and their relationship with the response to perioperative FLOT chemotherapy in patients with locally advanced resectable GC.Materials and methods. The retrospective study included 185 patients. All tumor samples were assessed for HER2 and microsatellite instability status. Among all cases there were 45 patients with locally advanced T2–4N1–2 M0 GC, who underwent a total or subtotal gastrectomy with D2 lymphadenectomy and perioperative chemotherapy with FLOT. Microsatellite instability detection was performed using fragment analysis, HER2 gene amplification testing – fluorescent in situ hybridization. Also 19 patients were tested for copy number changes of the FGFR1, FGFR2, KRAS, MET, EGFR, CCND1, MYC genes using Multiplex ligation-dependent probe amplification. The endpoints were progression-free survival and objective response rate.Results. Microsatellite instability was detected in 4.8 % (9/185) of GC cases. Microsatellite instability was associated with advanced age (p = 0.005), low grade of differentiation (p = 0.011), presence of tumor-infiltrating lymphocytes (p = 0.0004), and high preoperative CA 72–4 levels (p = 0.025). Prevalence of HER2 amplification was 7.5 % (14/185). It was associated with low grade of differentiation (p = 0.048) and metastasis in regional lymph nodes (p = 0.037). PFS in patients with HER2-positive (HER2 – human epidermal growth factor receptor 2) GC treated with perioperative FLOT chemotherapy (4/45) was significantly lower than in patients with HER2-negative GC: the median was 156 and 317 days, respectively (hazard ratio 0.49; 95 % confidence interval 0.16–1.47; p = 0.0006). There was no correlation between the presence of the alteration and ORR (p = 1.0). Progression-free survival in GC patients with KRAS amplification (3/19) was significantly lower comparing with patients without it: the median was 98 and 327 days, respectively (hazard ratio 0.29; 95 % confidence interval 0.07–1.19; p <0.0001). There was no association between an increase in KRAS copy number and objective response rate (p = 1.0). For microsatellite instability and other studied markers no statistically significant correlation with progression-free survival and objective response rate was found (p >0.05).Conclusion. The presence of HER2 and KRAS amplification have been shown as promising predictive markers of the treatment failure in patients treated with perioperative FLOT chemotherapy for locally advanced resectable GC.

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Molecular markers as predictors of response to perioperative chemotherapy in locally advanced gastric cancer

et al. 2023

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“…Among the most frequently affected organs, colon and lung cancers account for the highest number of deaths. Lung cancer accounts for 18.4% of cancer-related deaths, while colon cancer accounts for 9.2% of all cancer-related deaths worldwide [1]- [2]. The rate of simultaneous occurrence of lung and colon cancer is approximately 17%.…”

Section: Introductionmentioning

confidence: 99%

Lung and colon cancer classification using medical imaging: a feature engineering approach

et al. 2022

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Lung and colon cancers are the most common causes of death. Their simultaneous occurrence is uncommon, however, in the absence of early diagnosis, the metastasis of cancer cells is very high between these two organs. Currently, histopathological diagnosis and appropriate treatment are the only possibility to improve the chances of survival and reduce cancer mortality. Using artificial intelligence in the histopathological diagnosis of colon and lung cancer can provide significant help to specialists in identifying cases of colon and lung cancers with less effort, time and cost. The objective of this study is to set up a computer-aided diagnostic system that can accurately classify five types of colon and lung tissues (two classes for colon cancer and three classes for lung cancer) by analyzing their histopathological images. Using machine learning, features engineering and image processing techniques, the five models XGBoost, SVM, RF, LDA and MLP were used to perform the classification of histopathological images of lung and colon cancers that were acquired from the LC25000 dataset. The main advantage of using machine learning models is that they allow for better interpretability of the classification model since they are based on feature engineering; however, deep learning models are black box networks whose working is very difficult to understand due to the complex network design. The acquired experimental results show that machine learning models give satisfactory results and are very precise in identifying classes of lung and colon cancer subtypes. The XGBoost model gave the best performance with an accuracy of 99% and a F1-score of 98.8%. The implementation and the development of this model will help healthcare specialists identify types of colon and lung cancers. The code will be available upon request.

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“…Colon and lung are the most affected organs, with the largest number of deaths. Colon cancer is the major cause of 9.2% of cancer mortality worldwide, while lung cancer is the major cause of 18.4% of all cancer mortality [1,2]. The combined frequency of colon and lung cancer is estimated to be around 17%.…”

Section: Introductionmentioning

confidence: 99%

An Enhancement Technique to Diagnose Colon and Lung Cancer by using Double CLAHE and Deep Learning

Ibrahim¹,

Talaat²

2022

IJACSA

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The most common and deadly cancers are lung and colon cancers. More than a quarter of all cancer cases are caused by them. Early detection of the disease, on the other hand, greatly raises the probability of survival. Image enhancement by Double CLAHE stages and modified neural networks are made to improve classification accuracy and use Deep Learning (DL) algorithms to automate cancer detection. A new Artificial Intelligent classification system is presented in this research to recognize five kinds of colon and lung tissues, three malignant and two benign, with three classes for lung cancer and two classes for colon cancer, based on histological images. The results of the study imply that the suggested system can accurately identify tissues of cancer up to 99.5%. The use of this model will aid medical professionals in the development of an automatic and reliable system for detecting different kinds of colon and lung tumors.

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Her2-Positive and Microsatellite Instability Status in Gastric Cancer—Clinicopathological Implications

Cited by 18 publications

References 61 publications

Molecular markers as predictors of response to perioperative chemotherapy in locally advanced gastric cancer

Molecular markers as predictors of response to perioperative chemotherapy in locally advanced gastric cancer

Lung and colon cancer classification using medical imaging: a feature engineering approach

An Enhancement Technique to Diagnose Colon and Lung Cancer by using Double CLAHE and Deep Learning

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