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Simple Summary: Cancer stands as a leading cause of death globally and remains a significant threat to public health in developed nations. With an anticipated rise in cancer cases, the number of survivors is also expected to increase, many of whom grapple with distorted body image perception. Addressing the common poor quality of life among cancer patients and survivors necessitates monitoring emotions and body perception to mitigate psychological burdens. Given the strain on healthcare resources, exploring automated screening methods for cancer patients is crucial. This article proposes leveraging Natural Language Processing for monitoring body image perception in enterally fed patients with head and neck or upper gastrointestinal tract cancers, offering potential advancements in care delivery and patient support within oncology settings. Background: Psycho-oncology care has emerged as a significant concern in contemporary oncology practice, given its profound impact on patient psychological well-being. Patients undergoing treatment for head–neck or upper gastrointestinal tract cancers often experience complex emotional and psychological challenges, necessitating specialized support and intervention. Traditional approaches to psycho-oncological care may be limited in their ability to comprehensively assess and address patients' needs. Therefore, exploring innovative methodologies, such as leveraging natural language processing (NLP) elements, is crucial to enhancing the effectiveness of psycho-oncological interventions. Methods: In this study, we developed a method utilizing natural language processing (NLP) elements to augment psycho-oncological care for patients with head–neck or upper gastrointestinal tract cancers. The method aimed to facilitate vocabulary, sentiment, and intensity analysis of five basic emotions (happiness, sadness, anger, disgust, and fear), as well as to explore potential areas of difficulty such as body image, pain, and self-esteem. We conducted research involving 50 patients across three treatment stages. Results: Our method facilitated the identification of characteristic features at each treatment stage, aiding in the tailoring of appropriate therapies to individual patient needs. The results offer insights valuable to psychologists and psychiatrists for expedited diagnosis and intervention, potentially influencing therapy outcomes. Additionally, the data may inform treatment decisions by addressing patient-specific concerns. Furthermore, our method holds promise for optimizing the allocation of psychological care resources, particularly at the initial stages of patient contact. Limitations: The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary. Conclusion: In conclusion, our study demonstrates the potential utility of integrating natural language processing (NLP) elements into psycho-oncological care for patients with head–neck or upper gastrointestinal tract cancers. The developed method offers a novel approach to comprehensively assessing patients' emotional states and areas of difficulty, thereby facilitating tailored interventions and treatment planning. These findings underscore the importance of continued research and innovation in psycho-oncology to enhance patient care and outcomes. [ABSTRACT FROM AUTHOR]

Simple Summary: Psychological assessment of a cancer patient is a challenge due to the difficulty of the issues raised and, additionally, the lack of a sufficient number of psycho-oncologists. The article proposes a minimally invasive automatic method of this assessment using a note prepared by the patient about his body image. The proposed method allows you to determine the general attitude, the intensity of the five basic emotions (happiness, fear, sadness, anger, disgust), and potential areas of difficulty in terms of: body image, self-esteem, and acceptance of the environment. The combination of the attributes of patient's condition determined by the method can indicate the direction of support for the patient, relatives, direct medical personnel, and psycho-oncologists. Background: Head and neck cancers (H&NCs) constitute a significant part of all cancer cases. H&NC patients experience unintentional weight loss, poor nutritional status, or speech disorders. Medical interventions affect appearance and interfere with patients' self-perception of their bodies. Psychological consultations are not affordable due to limited time. Methods: We used NLP to analyze the basic emotion intensity, sentiment about one's body, characteristic vocabulary, and potential areas of difficulty in free notes. The emotion intensity research uses the extended NAWL dictionary developed using word embedding. The sentiment analysis used a hybrid approach: a sentiment dictionary and a deep recursive network. The part-of-speech tagging and domain rules defined by a psycho-oncologist determine the distinct language traits. Potential areas of difficulty were analyzed using the dictionaries method with word polarity to define a given area and the presentation of a note using bag-of-words. Here, we applied the LSA method using SVD to reduce dimensionality. A total of 50 cancer patients requiring enteral nutrition participated in the study. Results: The results confirmed the complexity of emotions in patients with H&NC in relation to their body image. A negative attitude towards body image was detected in most of the patients. The method presented in the study appeared to be effective in assessing body image perception disturbances, but it cannot be used as the sole indicator of body image perception issues. Limitations: The main problem in the research was the fairly wide age range of participants, which explains the potential diversity of vocabulary. Conclusions: The combination of the attributes of a patient's condition, possible to determine using the method for a specific patient, can indicate the direction of support for the patient, relatives, direct medical personnel, and psycho-oncologists. [ABSTRACT FROM AUTHOR]