Unravelling Quality of Life for Head and Neck Cancer Patients after VMAT Radiation Therapy: Insights from Toxicity, Dosimetry and Symptoms Correlation.

(1) Background: Head and neck cancer treatment, including advanced techniques like Volumetric Modulated Arc Therapy (VMAT), presents challenges for maintaining patient quality of life (QoL). Thus, thoroughly investigating how radiation therapy (RT) affects patients has been proved essential. Derived by that, this study aims to understand the complex interactions between not only RT and QoL but also symptom severity, and treatment-related toxicities in three distinct time points of patient's treatment; (2) Methods: To achieve that, EORTC-QLQ-C30 and EORTC QLQ-H&N35 questionnaires were used in combination with EORTC_RTOG scoring criteria and Spearman's rho statistical analysis for 74 patients with cancer undergoing VMAT radiation therapy; (3) Results: The results revealed a significant improvement in the Overall Health Index post-treatment, indicating a temporary decline during therapy followed by subsequent recovery, often surpassing pre-treatment QoL levels. Concurrently a reduction in symptomatology was observed, notably in pain, swallowing difficulties, and dry mouth, aligning with prior research indicating decreased symptom burden post-treatment. However, Spearman's correlation coefficient analysis at two distinct time points during therapy uncovered varying degrees of correlation between dosimetric data at Organs at Risk (OARs) and reported symptoms, highlighting potential limitations in using QoL questionnaires as sole indicators of treatment efficacy. Our investigation into the correlation between dosimetric data, toxicity, and symptoms focused on the relationship between radiation doses and oral mucositis levels, a common toxicity in head and neck cancer patients. Significant associations were identified between toxicity levels and dosimetric parameters, particularly with OARs such as the parotid glands, oral cavity, and swallowing muscles, underlining the utility of the EORTC method as a reliable toxicity assessment tool; (4) Conclusions: To summarize, current research attempts to underscore the importance of refining QoL assessments for enhanced patient care. The integration of dosimetric data, symptom severity, and treatment-related toxicities in the QoL outcomes of head and neck cancer patients undergoing VMAT radiation therapy, can lead towards the optimization of treatment strategies and the improvement of patient outcomes in future patient-centered radiation therapy practices.

Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis.

Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.

Extended Analysis of Raman Spectra Using Artificial Intelligence Techniques for Colorectal Abnormality Classification.

Raman spectroscopy (RS) techniques are attracting attention in the medical field as a promising tool for real-time biochemical analyses. The integration of artificial intelligence (AI) algorithms with RS has greatly enhanced its ability to accurately classify spectral data in vivo. This combination has opened up new possibilities for precise and efficient analysis in medical applications. In this study, healthy and cancerous specimens from 22 patients who underwent open colorectal surgery were collected. By using these spectral data, we investigate an optimal preprocessing pipeline for statistical analysis using AI techniques. This exploration entails proposing preprocessing methods and algorithms to enhance classification outcomes. The research encompasses a thorough ablation study comparing machine learning and deep learning algorithms toward the advancement of the clinical applicability of RS. The results indicate substantial accuracy improvements using techniques like baseline correction, L2 normalization, filtering, and PCA, yielding an overall accuracy enhancement of 15.8%. In comparing various algorithms, machine learning models, such as XGBoost and Random Forest, demonstrate effectiveness in classifying both normal and abnormal tissues. Similarly, deep learning models, such as 1D-Resnet and particularly the 1D-CNN model, exhibit superior performance in classifying abnormal cases. This research contributes valuable insights into the integration of AI in medical diagnostics and expands the potential of RS methods for achieving accurate malignancy classification.

Nanoparticle-Mediated Radiotherapy: Unraveling Dose Enhancement and Apoptotic Responses in Cancer and Normal Cell Lines.

Cervical cancer remains a pressing global health concern, necessitating advanced therapeutic strategies. Radiotherapy, a fundamental treatment modality, has faced challenges such as targeted dose deposition and radiation exposure to healthy tissues, limiting optimal outcomes. To address these hurdles, nanomaterials, specifically gold nanoparticles (AuNPs), have emerged as a promising avenue. This study delves into the realm of cervical cancer radiotherapy through the meticulous exploration of AuNPs' impact. Utilizing ex vivo experiments involving cell lines, this research dissected intricate radiobiological interactions. Detailed scrutiny of cell survival curves, dose enhancement factors (DEFs), and apoptosis in both cancer and normal cervical cells revealed profound insights. The outcomes showcased the substantial enhancement of radiation responses in cancer cells following AuNP treatment, resulting in heightened cell death and apoptotic levels. Significantly, the most pronounced effects were observed 24 h post-irradiation, emphasizing the pivotal role of timing in AuNPs' efficacy. Importantly, AuNPs exhibited targeted precision, selectively impacting cancer cells while preserving normal cells. This study illuminates the potential of AuNPs as potent radiosensitizers in cervical cancer therapy, offering a tailored and efficient approach. Through meticulous ex vivo experimentation, this research expands our comprehension of the complex dynamics between AuNPs and cells, laying the foundation for their optimized clinical utilization.

Raman Spectroscopy: A Personalized Decision-Making Tool on Clinicians' Hands for In Situ Cancer Diagnosis and Surgery Guidance.

Accurate in situ diagnosis and optimal surgical removal of a malignancy constitute key elements in reducing cancer-related morbidity and mortality. In surgical oncology, the accurate discrimination between healthy and cancerous tissues is critical for the postoperative care of the patient. Conventional imaging techniques have attempted to serve as adjuvant tools for in situ biopsy and surgery guidance. However, no single imaging modality has been proven sufficient in terms of specificity, sensitivity, multiplexing capacity, spatial and temporal resolution. Moreover, most techniques are unable to provide information regarding the molecular tissue composition. In this review, we highlight the potential of Raman spectroscopy as a spectroscopic technique with high detection sensitivity and spatial resolution for distinguishing healthy from malignant margins in microscopic scale and in real time. A Raman spectrum constitutes an intrinsic "molecular finger-print" of the tissue and any biochemical alteration related to inflammatory or cancerous tissue state is reflected on its Raman spectral fingerprint. Nowadays, advanced Raman systems coupled with modern instrumentation devices and machine learning methods are entering the clinical arena as adjunct tools towards personalized and optimized efficacy in surgical oncology.