The Effectiveness of Semantic Therapy on Death Anxiety, Pain Catastrophizing, Chronic Pain Acceptance, and Pain Intensity in Patients with Breast Cancer

Authors

  • Hussam Mohammed Wafqan
  • Nada Fadhil Abbas
  • Waleed Khaled Younis Albahadly
  • Mohammed Kadhim Abbas Al-Maeeni
  • Abdullah Shakir
  • Kahtan A. Mohammed
  • Mostafa Hsan Elwan
  • Nathera Hussin Alwan
  • Nazar Abd-Al-Gaffar Rsen
  • Maytham T. Qasim

Abstract

Background: Breast cancer is the most emotionally and psychologically affecting cancer among women, causing the patient to experience profound emotional and psychological difficulties. The present study aimed to examine the effectiveness of semantic therapy on death anxiety, pain catastrophizing, chronic pain acceptance, and pain intensity in patients with breast cancer.

Methods: The current study was a quasi-experimental research with a pre-test, post-test, and follow-up and a control group. All patients with breast cancer referred to King Fahad General Hospital in Jeddah, Saudi Arabia, in 2019, comprised this study's statistical population of 218 individuals. Simple random sampling was used to select the statistical sample. Thus, 60 individuals were selected and divided into two groups. The experimental group was administered the intervention via semantic therapy. Two months after the post-test, the groups underwent a follow-up examination. The repeated measures analysis of variance (ANOVA) was performed using SPSS software.

Results: Semantic therapy positively affected dependent variables, including death anxiety (F = 52.067, P < 0.001), pain catastrophizing (F = 124.569, P < 0.001), chronic pain acceptance (F = 46.034, P < 0.001), and pain intensity (F = 156.413, P < 0.001).

Conclusion: Semantic therapy decreases death anxiety, pain catastrophizing, and pain intensity, while increasing chronic pain acceptance.

References

Akbas, A., Dagmura, H., Daldal, E., Dasiran, F. M., Deveci, H., & Okan, I. (2021). Association between Shoulder Range of Motion and Pain Catastrophizing Scale in Breast Cancer Patients after Surgery. Breast.Care (Basel.), 16(1), 66-71. doi:10.1159/000506922 [doi];brc-0016-0066 [pii]. Retrieved from PM:33716634

Alonso-Calvo, R., Paraiso-Medina, S., Perez-Rey, D., Alonso-Oset, E., van, S. R., Yu, S. et al. (2017). A semantic interoperability approach to support integration of gene expression and clinical data in breast cancer. Comput.Biol Med, 87, 179-186. doi:S0010-4825(17)30169-5 [pii];10.1016/j.compbiomed.2017.06.005 [doi]. Retrieved from PM:28601027

Badve, S., & Nakshatri, H. (2012). Breast-cancer stem cellsbeyond semantics. Lancet Oncol, 13(1), e43-e48.

Baskici, C., Atan, S., & Ercil, Y. (2019). Forecasting of innovation in the light of semantic networks. Procedia Comput Sci 158, 443-449.

Bauer, J., Wehland, M., Infanger, M., Grimm, D., & Gombocz, E. (2018). Semantic analysis of posttranslational modification of proteins accumulated in thyroid cancer cells exposed to simulated microgravity. Int J Mol.Sci, 19(8). doi:ijms19082257 [pii];10.3390/ijms19082257 [doi]. Retrieved from PM:30071661

Cho, S., & Cho, O. H. (2022). Depression and quality of life in older adults with pneumoconiosis: The mediating role of death anxiety. Geriatric Nursing, 44, 215-220.

Daowd, A., Barrett, M., Abidi, S., & Abidi, S. R. (2021 Aug 9-12). A framework to build a causal knowledge graph for chronic diseases and cancers by discovering semantic associations from biomedical literature. Proceedings of the 2021 IEEE 9th International Conference on Healthcare Informatics (ICHI); Victoria, BC, Canada.

Datta, S., Bernstam, E. V., & Roberts, K. (2019). A frame semantic overview of NLP-based information extraction for cancer-related EHR notes. J Biomed Inform., 100, 103301. doi:S1532-0464(19)30221-7 [pii];10.1016/j.jbi.2019.103301 [doi]. Retrieved from PM:31589927

Du, D., Gu, J., Chen, X., Lv, W., Feng, Q., Rahmim, A. et al. (2021). Integration of PET/CT radiomics and semantic features for differentiation between active pulmonary tuberculosis and lung cancer. Mol.Imaging.Biol, 23(2), 287-298. doi:10.1007/s11307-020-01550-4 [doi];10.1007/s11307-020-01550-4 [pii]. Retrieved from PM:33030709

Gonzalez-Hernandez, E., Campos, D., Diego-Pedro, R., Romero, R., Banos, R., Negi, L. T. et al. (2021). Changes in the semantic construction of compassion after the Cognitively-Based Compassion Training (CBCT((R))) in women breast cancer survivors. Span.J Psychol, 24, e34. doi:10.1017/SJP.2021.31 [doi];S1138741621000317 [pii]. Retrieved from PM:34024294

Hoar, D., Lee, P. Q., Guida, A., Patterson, S., Bowen, C. V., Merrimen, J. et al. (2021). Combined transfer learning and test-time augmentation improves convolutional neural network-based semantic segmentation of prostate cancer from multi-parametric mr images. Comput.Methods Programs.Biomed, 210, 106375. doi:S0169-2607(21)00449-1 [pii];10.1016/j.cmpb.2021.106375 [doi]. Retrieved from PM:34500139

Ing, N., Li, J., Salemi, H., Arnold, C., Knudsen, B., Gertych, A. et al. (2018 Mar 6). Semantic segmentation for prostate cancer grading by convolutional neural networks. Proceedings of SPIE 10581, Medical Imaging 2018: Digital Pathology; Houston, TX, US.

Inoue, K. (2019). Semantic segmentation of breast lesion using deep learning. Ultrasound in Medicine & Biology, 45, S52.

Jebadas, D. G., Sivaram, M., M, A., Vidhyasagar, B. S., & Kannan, B. B. (2022). Histogram distance metric learning to diagnose breast cancer using semantic analysis and natural language interpretation methods. In P. Johri, M. J. Div+ín, R. Khanam, M. Marciszack, & A. Will (Eds.), Trends and Advancements of Image Processing and Its Applications (pp. 249-259). Cham, Switzerland: Springer International Publishing.

Jebahi, F., Sharma, S., Bloss, J. E., & Wright, H. H. (2021). Effects of tamoxifen on cognition and language in women with breast cancer: A systematic search and a scoping review. Psychooncology., 30(8), 1262-1277. doi:10.1002/pon.5696 [doi]. Retrieved from PM:33866625

Kerns, R. D., Turk, D. C., & Rudy, T. E. (1985). The West Haven-Yale Multidimensional Pain Inventory (WHYMPI). Pain, 23(4), 345-356. doi:10.1016/0304-3959(85)90004-1 [doi];00006396-198512000-00004 [pii]. Retrieved from PM:4088697

Kim, W. H., Kim, H. J., Lee, S. M., Cho, S. H., Shin, K. M., Lee, S. Y. et al. (2018). Preoperative axillary nodal staging with ultrasound and magnetic resonance imaging: predictive values of quantitative and semantic features. Br.J Radiol, 91(1092), 20180507. doi:10.1259/bjr.20180507 [doi]. Retrieved from PM:30059242

Martial, C., Cassol, H., Charland-Verville, V., Pallavicini, C., Sanz, C., Zamberlan, F. et al. (2019). Neurochemical models of near-death experiences: A large-scale study based on the semantic similarity of written reports. Conscious.Cogn, 69, 52-69. doi:S1053-8100(18)30535-X [pii];10.1016/j.concog.2019.01.011 [doi]. Retrieved from PM:30711788

Menzies, R. E., Zuccala, M., Sharpe, L., & Dar-Nimrod, I. (2018). The effects of psychosocial interventions on death anxiety: A meta-analysis and systematic review of randomised controlled trials. J Anxiety Disord, 59, 64-73. doi:S0887-6185(18)30251-2 [pii];10.1016/j.janxdis.2018.09.004 [doi]. Retrieved from PM:30308474

Mihaylov, I., Nisheva-Pavlova, M., & Vassilev, D. (2019). An approach for semantic data integration in cancer studies. In J. M. F. Rodrigues, P. J. S. Cardoso, J., Monteiro, R. Lam, V. V. Krzhizhanovskaya, M. H. Lees, J. J. Dongarra, & P. M. A. Sloot (Eds.). Computational Science  ICCS 2019 (pp. 60-73) Cham, Switzerland: Springer International Publishing.

Nemoto, T., Futakami, N., Yagi, M., Kunieda, E., Akiba, T., Takeda, A. et al. (2020). Simple low-cost approaches to semantic segmentation in radiation therapy planning for prostate cancer using deep learning with non-contrast planning CT images. Phys Med, 78, 93-100. doi:S1120-1797(20)30220-9 [pii];10.1016/j.ejmp.2020.09.004 [doi]. Retrieved from PM:32950833

Oyelade, O. N., Obiniyi, A. A., Junaidu, S. B., & Adewuyi, S. A. (2018). ST-ONCODIAG: A semantic rule-base approach to diagnosing breast cancer base on Wisconsin datasets. Inform Med Unlocked, 10, 117-125.

Sanchez, E., Toro, C., Artetxe, A., Gra+¦a, M., Sanin, C., Szczerbicki, E. et al. (2013). Bridging challenges of clinical decision support systems with a semantic approach. A case study on breast cancer. Pattern Recognit Lett, 34(14), 1758-1768.

Shahbazi, G., Moraveji, M., Keramati, S., Ghobadi Davod, R., & Noor, M. (2020). Comparison of the effectiveness of cognitive-behavioral and semantic therapy in a group manner on reducing perceived stress and improving the meaning of life in patients with breast cancer. Med J Mashad Univ Med Sci, 63(4), 2633-2642.

Shi, R., Chen, W., Yang, B., Qu, J., Cheng, Y., Zhu, Z. et al. (2020). Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features. Am.J Cancer Res, 10(12), 4513-4526. Retrieved from PM:33415015

Sullivan, M. J. L., Bishop, S. R., & Pivik, J. (1995). The Pain Catastrophizing Scale: Development and validation. Psychol Assess, 7(4), 524-532.

Tang, P. L., Chiou, C. P., Lin, H. S., Wang, C., & Liand, S. L. (2011). Correlates of death anxiety among Taiwanese cancer patients. Cancer Nurs., 34(4), 286-292. doi:10.1097/NCC.0b013e31820254c6 [doi]. Retrieved from PM:21242771

Templer, D. I., Ruff, C. F., & Franks, C. M. (1971). Death anxiety: Age, sex, and parental resemblance in diverse populations. Dev Psychol., 4(1, Pt.1), 108.

Trabelsi Ben Ameur, S., Sellami, D., Wendling, L., & Cloppet, F. (2019). Breast cancer diagnosis system based on semantic analysis and Choquet integral feature selection for high risk subjects. Big Data Cogn.Comput., 3(3), 41.

Vowles, K. E., McCracken, L. M., McLeod, C., & Eccleston, C. (2008). The Chronic Pain Acceptance Questionnaire: Confirmatory factor analysis and identification of patient subgroups. Pain, 140(2), 284-291. doi:00006396-200811300-00006 [pii];10.1016/j.pain.2008.08.012 [doi]. Retrieved from PM:18824301

Wang, C. N., Chang, S., Sheu, C. Y., & Tsai, J. P. (2018 Jan 31-Feb 2). Application of semantic computing in cancer on secondary data analysis. Proceedings of the 2nd IEEE International Conference on Robotic Computing (IRC): Laguna Hills, CA, USA.

Wu, W., Pierce, L. A., Zhang, Y., Pipavath, S. N. J., Randolph, T. W., Lastwika, K. J. et al. (2019). Comparison of prediction models with radiological semantic features and radiomics in lung cancer diagnosis of the pulmonary nodules: a case-control study. Eur Radiol, 29(11), 6100-6108. doi:10.1007/s00330-019-06213-9 [doi];10.1007/s00330-019-06213-9 [pii]. Retrieved from PM:31115618

Xu, X., Ou, M., Xie, C., Cheng, Q., & Chen, Y. (2019). Pain acceptance and its associated factors among cancer patients in Mainland China: A cross-sectional study. Pain Res Manag., 2019, 9458683. doi:10.1155/2019/9458683 [doi]. Retrieved from PM:30906486

Zaza, C., Reyno, L., & Moulin, D. E. (2000). The multidimensional pain inventory profiles in patients with chronic cancer-related pain: an examination of generalizability. Pain, 87(1), 75-82. doi:00006396-200007010-00008 [pii];10.1016/S0304-3959(00)00274-8 [doi]. Retrieved from PM:10863047

Downloads

Published

2022-07-26

How to Cite

Wafqan, H. M. ., Abbas, N. F. ., Albahadly, W. K. Y. ., Al-Maeeni, M. K. A. ., Shakir, A. ., Mohammed, K. A. ., Elwan, M. H. ., Alwan, N. H. ., Rsen, N. A.-A.-G. ., & Qasim, M. T. . (2022). The Effectiveness of Semantic Therapy on Death Anxiety, Pain Catastrophizing, Chronic Pain Acceptance, and Pain Intensity in Patients with Breast Cancer . International Journal of Body, Mind and Culture, 9(sp). Retrieved from https://ijbmc.org/index.php/ijbmc/article/view/418

Issue

Section

Quantitative Study(ies)

Most read articles by the same author(s)