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Wu W, He X, Li S, Jin M, Ni Y. Pain nursing for gynecologic cancer patients. Front Oncol 2023; 13:1205553. [PMID: 37564934 PMCID: PMC10410261 DOI: 10.3389/fonc.2023.1205553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 07/11/2023] [Indexed: 08/12/2023] Open
Abstract
Gynecological malignancy remains a prevalent cause of mortality among women. Chronic cancer pain, as a severe complication of malignancy and its therapies, accounts for a substantial burden of physical and psychological distress in affected patients. Accordingly, early identification, assessment, and standardized management of such pain are crucial in the prevention or delay of its progression. In the present review, we provide a comprehensive overview of the pathological factors that contribute to pain in patients with gynecological malignancy while highlighting the underlying mechanisms of pain in this population. In addition, we summarize several treatment modalities targeting pain management in gynecologic cancer patients, including surgery, radiotherapy, and chemotherapy. These interventions are crucial for tumor elimination and patient survival. Chronic cancer pain exerts a significant impact on wellbeing and quality of life for patients with gynecologic cancer. Therefore, our review emphasizes the importance of addressing this pain and its psychological sequelae and advocates for a multidisciplinary approach that encompasses nursing and psychological support. In summary, this review offers valuable insights into the pathological factors underlying pain, reviews pain management modalities, and stresses the critical role of early intervention and comprehensive care in enhancing the quality of life of these patients.
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Affiliation(s)
| | - Xiaodan He
- Department of Gynecology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, Liaoning, China
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Ni H, Zhou G, Chen X, Ren J, Yang M, Zhang Y, Zhang Q, Zhang L, Mao C, Li X. Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram. Bioengineering (Basel) 2023; 10:828. [PMID: 37508855 PMCID: PMC10376503 DOI: 10.3390/bioengineering10070828] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 07/30/2023] Open
Abstract
This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.
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Affiliation(s)
- Haixu Ni
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Gonghai Zhou
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Xinlong Chen
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
| | - Jing Ren
- The Reproductive Medicine Hospital of the First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Minqiang Yang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Yuhong Zhang
- School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
| | - Qiyu Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Lei Zhang
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Chengsheng Mao
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Xun Li
- First Clinical Medical College, Lanzhou University, Lanzhou 730000, China
- Department of General Surgery, First Hospital of Lanzhou University, Lanzhou 730000, China
- Key Laboratory of Biotherapy and Regenerative Medicine of Gansu Province, Lanzhou 730000, China
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Gurova K. Can aggressive cancers be identified by the "aggressiveness" of their chromatin? Bioessays 2022; 44:e2100212. [PMID: 35452144 DOI: 10.1002/bies.202100212] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 04/08/2022] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
Abstract
Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.
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Affiliation(s)
- Katerina Gurova
- Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center, Buffalo, New York, USA
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Liu S, Wu M, Wang F. Research Progress in Prognostic Factors and Biomarkers of Ovarian Cancer. J Cancer 2021; 12:3976-3996. [PMID: 34093804 PMCID: PMC8176232 DOI: 10.7150/jca.47695] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Accepted: 04/22/2021] [Indexed: 12/14/2022] Open
Abstract
Ovarian cancer is a serious threat to women's health; its early diagnosis rate is low and prone to metastasis and recurrence. The current conventional treatment for ovarian cancer is a combination of platinum and paclitaxel chemotherapy based on surgery. The recurrence and progression of ovarian cancer with poor prognosis is a major challenge in treatment. With rapid advances in technology, understanding of the molecular pathways involved in ovarian cancer recurrence and progression has increased, biomarker-guided treatment options can greatly improve the prognosis of patients. This review systematically discusses and summarizes existing and new information on prognostic factors and biomarkers of ovarian cancer, which is expected to improve the clinical management of patients and lead to effective personalized treatment.
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Affiliation(s)
- Shuna Liu
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 210029
- National Key Clinical Department of Laboratory Medicine, Nanjing, China, 210029
| | - Ming Wu
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 210029
- National Key Clinical Department of Laboratory Medicine, Nanjing, China, 210029
| | - Fang Wang
- Department of Laboratory Medicine, the First Affiliated Hospital of Nanjing Medical University, Nanjing, China, 210029
- National Key Clinical Department of Laboratory Medicine, Nanjing, China, 210029
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Kleppe A, Albregtsen F, Trovik J, Kristensen GB, Danielsen HE. Prognostic Value of the Diversity of Nuclear Chromatin Compartments in Gynaecological Carcinomas. Cancers (Basel) 2020; 12:E3838. [PMID: 33352679 PMCID: PMC7766595 DOI: 10.3390/cancers12123838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/15/2020] [Accepted: 12/16/2020] [Indexed: 11/16/2022] Open
Abstract
Statistical texture analysis of cancer cell nuclei stained for DNA has recently been used to develop a pan-cancer prognostic marker of chromatin heterogeneity. In this study, we instead analysed chromatin organisation by automatically quantifying the diversity of chromatin compartments in cancer cell nuclei. The aim was to investigate the prognostic value of such an assessment in relation to chromatin heterogeneity and as a potential supplement to pathological risk classifications in gynaecological carcinomas. The diversity was quantified by calculating the entropy of both chromatin compartment sizes and optical densities within compartments. We analysed a median of 281 nuclei (interquartile range (IQR), 273 to 289) from 246 ovarian carcinoma patients and a median of 997 nuclei (IQR, 502 to 1452) from 791 endometrial carcinoma patients. The prognostic value of the entropies and chromatin heterogeneity was moderately strongly correlated (r ranged from 0.68 to 0.73), but the novel marker was observed to provide additional prognostic information. In multivariable analysis with clinical and pathological markers, the hazard ratio associated with the novel marker was 2.1 (95% CI, 1.3 to 3.5) in ovarian carcinoma and 2.4 (95% CI, 1.5 to 3.9) in endometrial carcinoma. Integration with pathological risk classifications gave three risk groups with distinctly different prognoses. This suggests that the novel marker of diversity of chromatin compartments might possibly contribute to the selection of high-risk stage I ovarian carcinoma patients for adjuvant chemotherapy and low-risk endometrial carcinoma patients for less extensive surgery.
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Affiliation(s)
- Andreas Kleppe
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway; (A.K.); (F.A.); (G.B.K.)
- Department of Informatics, University of Oslo, NO-0316 Oslo, Norway
| | - Fritz Albregtsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway; (A.K.); (F.A.); (G.B.K.)
- Department of Informatics, University of Oslo, NO-0316 Oslo, Norway
| | - Jone Trovik
- Department of Obstetrics and Gynecology, Haukeland University Hospital, NO-5020 Bergen, Norway;
- Department of Clinical Science, University of Bergen, NO-5020 Bergen, Norway
| | - Gunnar B. Kristensen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway; (A.K.); (F.A.); (G.B.K.)
- Department of Gynecologic Oncology, Oslo University Hospital, NO-0424 Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, NO-0318 Oslo, Norway
| | - Håvard E. Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, NO-0424 Oslo, Norway; (A.K.); (F.A.); (G.B.K.)
- Department of Informatics, University of Oslo, NO-0316 Oslo, Norway
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford OX3 9DU, UK
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