Kia Shabnam, Setayeshi Saeed, Pouladian Majid, Ardehali Seyed Hossein
Department of Medical Radiation Engineering, science and research branch, Islamic Azad University, Tehran, Iran.
Faculty of Energy Engineering and Physics, Amirkabir University of Technology (Tehran Polytechnique), Tehran, Iran.
J Appl Clin Med Phys. 2019 Nov;20(11):153-168. doi: 10.1002/acm2.12671. Epub 2019 Oct 8.
The diagnosis of cancer by modern computer tools, at the very first stages of the incident, is a very important issue that has involved many researchers. In the meantime, skin cancer is a great deal of research because many people are involved with it. The purpose of this paper is to introduce an innovative method based on tissue frequency analyzes to obtain the accurate and real-time evaluation of skin cancers. According to the Biological resonance theory, body cells have natural and unique frequencies based on their biological fluctuations, which, if the structure, profile and cellular status change, its frequency also varies. This concept and theory is considered as the basis for analyzing skin tissue health in the proposed method. Reflected ultrasound waves from tissue have been processed and studied based on frequency analysis as a new method for early detection and diagnosis of accurate location and type of skin diseases. The developed algorithm was approved through 400 patients from CRED; its ability to evaluate benign and malignant skin lesions was shown (AUC = 0.959), with comparable clinical precision; as for the selected threshold, sensitivity, and specificity were 93.8% and 97.3%, respectively. Therefore, this method can detect skin malignancy with an accurate, noninvasive and real-time procedure.
利用现代计算机工具在癌症发病的最初阶段进行诊断，是一个涉及众多研究人员的非常重要的问题。与此同时，皮肤癌受到了大量研究，因为很多人都受其影响。本文的目的是介绍一种基于组织频率分析的创新方法，以实现对皮肤癌的准确实时评估。根据生物共振理论，体细胞基于其生物波动具有自然且独特的频率，若细胞的结构、形态和状态发生变化，其频率也会改变。这一概念和理论被视为所提方法中分析皮肤组织健康状况的基础。作为一种用于早期检测以及准确诊断皮肤疾病位置和类型的新方法，来自组织的反射超声波已基于频率分析进行了处理和研究。所开发的算法在400名来自CRED的患者身上得到了验证；结果显示了其评估良性和恶性皮肤病变的能力（AUC = 0.959），临床精度相当；对于所选阈值，灵敏度和特异性分别为93.8%和97.3%。因此，该方法能够通过准确、无创且实时的程序检测皮肤恶性肿瘤。
