重离子束治疗皮肤恶性肿瘤的初步临床结果；Clinical trial of carbon ion radiotherapy for malignant neoplasm of skin

目的:评价重离子束对皮肤恶性肿瘤放射治疗的近期疗效和毒副反应。方法:29例皮肤恶性肿瘤患者分6批接受重离子束放射治疗,其中恶性黑色素瘤13例,皮肤鳞癌及Bowen’s病各6例,基底细胞癌2例,其他皮肤恶性肿瘤2例。照射总剂量(50～70)GyE/(6～12)d,单次剂量5.5～11.67GyE,1f/d,连续治疗。采用RTOG标准和WHO近期疗效标准分别评价毒副反应和近期疗效。结果:截止2009-05,中位随访时间为13.5个月(1～25个月),随访率为100%。29例患者中完全缓解(CR)24例(82.8%),部分缓解(PR)5例(17.2%),有效率(RR)为100%,中位生存时间为22.8个月(95%CI:20.6～24.9)。皮肤反应0度11例(37.9%),Ⅰ度9例(31.0%),Ⅱ度6例(20.7%),Ⅲ度2例(6.9%),Ⅳ度1例(3.4%);血液毒副反应治疗前后无明显改变。结论:重离子束(12C6+)放射治疗皮肤恶性肿瘤近期疗效好,并发症轻,远期疗效、晚期副反应等尚需进一步长期全面的观察和更多的研究提供依据。

Application of probabilistic neural network in the clinical diagnosis of cancers based on clinical chemistry data

As a recently developed and powerful classification tool, probabilistic neural network was used to distinguish cancer patients from healthy persons according to the levels of nucleosides in human urine. Two datasets (containing 32 and 50 patterns, respectively) were investigated and the total consistency rate obtained was 100% for dataset 1 and 94% for dataset 2. To evaluate the performance of probabilistic neural network, linear discriminant analysis and learning vector quantization network, were also applied to the classification problem. The results showed that the predictive ability of the probabilistic neural network is stronger than the others in this study. Moreover, the recognition rate for dataset 2 can achieve to 100% if combining, these three methods together, which indicated the promising potential of clinical diagnosis by combining different methods. (C) 2002 Elsevier Science B.V. All rights reserved.

Artificial neural network classification based on high-performance liquid chromatography of urinary and serum nucleosides for the clinical diagnosis of cancer

Nucleosides in human urine and serum have frequently been studied as a possible biomedical marker for cancer, acquired immune deficiency syndrome (AIDS) and the whole-body turnover of RNAs. Fifteen normal and modified nucleosides were determined in 69 urine and 42 serum samples using high-performance liquid chromatography (HPLC). Artificial neural networks have been used as a powerful pattern recognition tool to distinguish cancer patients from healthy persons. The recognition rate for the training set reached 100%. In the validating set, 95.8 and 92.9% of people were correctly classified into cancer patients and healthy persons when urine and serum were used as the sample for measuring the nucleosides. The results show that the artificial neural network technique is better than principal component analysis for the classification of healthy persons and cancer patients based on nucleoside data. (C) 2002 Elsevier Science B.V. All rights reserved.

Clinical significance and prognostic value of urinary nucleosides in breast cancer patients

Objective: Thirteen urinary nucleosides, primarily degradation products of tRNA, were evaluated as potential tumor markers for breast cancer patients.