The Intersection of Traditional and Modern Medicine in Cancer Care: Evaluating Integrative Oncology Models

Cristina Bosinelli^*
*Correspondence: Cristina Bosinelli, Department of Integrative Oncology, University of Pennsylvania, Philadelphia, PA 19104, USA, Email:

Introduction

Cancer care has long been dominated by traditional medical approaches that focus primarily on standard treatments such as surgery, chemotherapy, and radiation. However, the growing interest in holistic and patient-centered care has led to the emergence of integrative oncology, a model that seeks to bridge the gap between conventional medicine and complementary therapies. Integrative oncology combines the rigorous, evidence-based methods of modern medicine with traditional and alternative practices to create a more comprehensive approach to cancer treatment. This intersection not only aims to enhance the efficacy of treatment but also to improve the overall quality of life for patients. Evaluating integrative oncology models provides insight into how these blended approaches can address the complex needs of cancer patients, offering a more nuanced and personalized care experience [1,2].

Description

Integrative oncology is an evolving field that synthesizes conventional cancer treatments with complementary therapies to offer a holistic approach to cancer care. This model recognizes that while modern medicine provides crucial and often life-saving interventions, integrating traditional practices can support and enhance overall patient well-being. Conventional cancer treatments such as chemotherapy, radiation therapy, and targeted therapies are grounded in scientific research and clinical trials. These methods focus on directly attacking cancer cells, aiming to reduce tumor size, eradicate cancer, and prevent recurrence. The effectiveness of these treatments is well-documented, and they remain the cornerstone of cancer management [3]. Complementary therapies encompass a wide range of practices, including acupuncture, herbal medicine, nutritional counselling and mind-body techniques such as meditation and yoga. These therapies are often used alongside conventional treatments to manage symptoms, reduce side effects, and improve emotional and psychological well-being. While they may not replace traditional treatments, they offer additional support that can enhance the patient’s overall experience. Integrative oncology models aim to combine these approaches in a way that maximizes benefits and minimizes potential conflicts. For instance, a patient undergoing chemotherapy might also participate in acupuncture sessions to manage nausea or receive nutritional advice to maintain strength and improve immunity. By carefully coordinating these diverse treatment modalities, integrative oncology seeks to provide a more balanced and patient-centered care plan [4].

Evaluating integrative oncology models

One of the key aspects of evaluating integrative oncology models is assessing the scientific evidence supporting various complementary therapies. Research into how these practices impact treatment outcomes, symptom management, and overall quality of life is crucial. While some complementary therapies have robust evidence supporting their efficacy, others require further investigation to establish their benefits. Patient feedback is an essential component in evaluating integrative models. Surveys and interviews can reveal how patients perceive the integration of traditional and complementary therapies, their satisfaction with the overall care, and the impact on their emotional and psychological well-being. Understanding patient experiences helps to refine and improve integrative approaches. The integration of complementary therapies into cancer care can have implications for cost and accessibility. Evaluating the financial impact of these approaches and their availability to patients across different healthcare settings is important. Ensuring that integrative oncology remains accessible and affordable is crucial for its widespread adoption and effectiveness. Successful integrative oncology requires effective collaboration between oncologists, complementary therapists, and other healthcare professionals. Evaluating how well these teams work together and communicate can provide insights into the effectiveness of integrative models. Strong interdisciplinary collaboration ensures that all aspects of a patient’s care are coordinated and that treatment plans are comprehensive and cohesive [5].

Conclusion

The intersection of traditional and modern medicine in cancer care through integrative oncology represents a promising advancement in the treatment landscape. By combining evidence-based conventional therapies with complementary practices, integrative models offer a more holistic approach that addresses not only the disease but also the patient’s overall well-being. Evaluating these models involves examining their efficacy, patient experiences, cost implications, and the effectiveness of interdisciplinary collaboration. As the field continues to evolve, ongoing research and evaluation will be essential in refining integrative oncology practices and ensuring that they provide the best possible outcomes for cancer patients. Ultimately, the goal is to create a care paradigm that respects and incorporates the strengths of both traditional and modern approaches, delivering comprehensive and compassionate care to those affected by cancer.

Acknowledgement

None.

Conflict of Interest

Authors declare no conflict of interest.

References

1. Baez-Navarro, Ximena, Mieke R. van Bockstal and Diënna Nawawi, et al. "Interobserver variation in the assessment of immunohistochemistry expression levels in HER2-negative breast cancer: Can we improve the identification of low levels of HER2 expression by adjusting the criteria? An international interobserver study."Mod Pathol36 (2023): 100009.

   Google Scholar, Crossref, Indexed at

2. Robert, Marie E., Josef Rüschoff, Bharat Jasani and Rondell P. Graham, et al. "High interobserver variability among pathologists using combined positive score to evaluate PD-L1 expression in gastric, gastroesophageal junction, and esophageal adenocarcinoma."Mod Pathol36 (2023): 100154.

   Google Scholar, Crossref, Indexed at

3. Butter, Rogier, Liesbeth M. Hondelink, Lisette van Elswijk and Johannes LG Blaauwgeers, et al. "The impact of a pathologist’s personality on the interobserver variability and diagnostic accuracy of predictive PD-L1 immunohistochemistry in lung cancer."Lung Cancer166 (2022): 143-149.

   Google Scholar, Crossref, Indexed at

4. Bankhead, Peter, Maurice B. Loughrey, José A. Fernández and Yvonne Dombrowski, et al. "QuPath: Open source software for digital pathology image analysis." Sci Rep7 (2017): 1-7.

   Google Scholar, Crossref, Indexed at

5. Yan, Rui, Yijun Shen, Xueyuan Zhang and Peihang Xu, et al. "Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning." Med Image Anal87 (2023): 102824.

   Google Scholar, Crossref, Indexed at