ABSTRACT
Correction for 'Body composition analysis via spatially resolved NIR spectroscopy with multifrequency bioimpedance precision' by Evgeny Shirshin et al., Anal. Methods, 2024, 16, 175-178, https://doi.org/10.1039/D3AY01901B.
ABSTRACT
Near-infrared spectroscopy (NIRS) is often criticized due to its insufficient accuracy in determining body composition compared to the gold standard methods. In this work, we show that the use of multiple source-detector distances, as well as the simultaneous use of physiological and optical features, can significantly improve the accuracy of determination of fat and lean mass percentage in the human body using NIR spectroscopy. The study performed on the n = 292 cohort revealed the mean absolute errors of 3.5% for fat content and 3.3% for soft lean mass percentage prediction (r = 0.93) using the multifrequency bioimpedance analysis (BIA) as a reference. Hence, NIRS can serve as an independent reliable method for body composition analysis with precision close to that of advanced multifrequency BIA.
Subject(s)
Body Composition , Spectroscopy, Near-Infrared , Humans , Electric Impedance , Body Composition/physiologyABSTRACT
Introduction: Adrenocortical cancer (ACC) is a rare malignant tumor that originates in the adrenal cortex. Despite extensive molecular-genetic, pathomorphological, and clinical research, assessing the malignant potential of adrenal neoplasms in clinical practice remains a daunting task in histological diagnosis. Although the Weiss score is the most prevalent method for diagnosing ACC, its limitations necessitate additional algorithms for specific histological variants. Unequal diagnostic value, subjectivity in evaluation, and interpretation challenges contribute to a gray zone where the reliable assessment of a tumor's malignant potential is unattainable. In this study, we introduce a universal mathematical model for the differential diagnosis of all morphological types of ACC in adults. Methods: This model was developed by analyzing a retrospective sample of data from 143 patients who underwent histological and immunohistochemical examinations of surgically removed adrenal neoplasms. Statistical analysis was carried out on Python 3.1 in the Google Colab environment. The cutting point was chosen according to Youden's index. Scikit-learn 1.0.2 was used for building the multidimensional model for Python. Logistical regression analysis was executed with L1-regularization, which is an effective method for extracting the most significant features of the model. Results: The new system we have developed is a diagnostically meaningful set of indicators that takes into account a smaller number of criteria from the currently used Weiss scale. To validate the obtained model, we divided the initial sample set into training and test sets in a 9:1 ratio, respectively. The diagnostic algorithm is highly accurate [overall accuracy 100% (95% CI: 96%-100%)]. Discussion: Our method involves determining eight diagnostically significant indicators that enable the calculation of ACC development probability using specified formulas. This approach may potentially enhance diagnostic precision and facilitate improved clinical outcomes in ACC management.
Subject(s)
Adrenal Cortex Neoplasms , Adrenocortical Adenoma , Adrenocortical Carcinoma , Adult , Humans , Adrenocortical Adenoma/pathology , Adrenocortical Carcinoma/diagnosis , Adrenocortical Carcinoma/pathology , Retrospective Studies , Adrenal Cortex Neoplasms/diagnosis , Adrenal Cortex Neoplasms/surgery , Adrenal Cortex Neoplasms/pathology , Regression AnalysisABSTRACT
The analysis of the tumor microenvironment, especially tumor-infiltrated immune cells, is essential for predicting tumor prognosis, clinical outcomes, and therapy strategies. Adrenocortical cancer is a rare nonimmunogenic malignancy in which the importance of the presence of immune cells is not well understood. In our study, we made the first attempt to understand the interplay between the histology of adrenocortical cancer and its immune landscape using cases from The Cancer Genome Atlas database and the Endocrinology Research Centre collection (Moscow, Russia). We showed that the oncocytic variant of adrenocortical cancer is characterized by intensive immune infiltration and better survival, and it is crucial to analyze the effect of immune infiltration independently for each histological variant.
