ANN SOFTWARE FOR EARLY DETECTION OF OVARIAN CANCER

用于早期检测卵巢癌的 ANN 软件

• 批准号: 6211886
• 负责人: HONG ZHANG
• 金额: $ 58.56万
• 依托单位: HORUS GLOBAL HEALTHNET, INC.
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-05-07 至 2002-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6211886
• 关键词: artificial intelligence; biomarker; computer assisted diagnosis; computer program /software; computer system design /evaluation; diagnosis design /evaluation; early diagnosis; human data; neoplasm /cancer diagnosis; ovary neoplasms

Ovarian cancer is the leading cause of death from gynecologic cancer in the US. For most patients, the disease is first diagnosed at an advanced stage, and the 5-year survival rate is low (< 30%). Despite incremental improvement in chemotherapy, the cure rate has not improved significantly in the past decades, The dramatic difference in long-term survival between patients with local disease (80-90%) and those with distant metastases (5-20%) suggests the need for a non-invasive, yet effective test applicable to at-risk population groups to detect ovarian cancer in early stages. Building upon prior research in differentiating malignant from benign ovarian masses, this project seeks to apply artificial neural network (ANN) technology to the problem of screening for early-stage ovarian cancer based on a variety of serum markers and other clinical inputs. The recently completed Phase I pilot project validated the feasibility by (1) assembling existing data from collaborating Organizations, (2) analyzing the predictive value of relevant biomarkers, (3) developing a preliminary ANN, and (4) validating the ANN using independent test data. The successful completion of Phase I now facilitates the commencement of Phase II activities to develop a production version of the screening system and to initiate broad-scale validation through multiple clinical studies. PROPOSED COMMERCIAL APPLICATIONS: ANN software capable of detecting early-stage ovarian cancer with sufficient improvement in specificity, sensitivity, and predictive value over alternative techniques would have clear commercial value in screening high-risk populations. Horns presently offers as a commercial product an Internet-based clinical information processing service, called ProstAsure, developed using ANN technology, for the detection of prostate cancer.

卵巢癌是美国妇科癌症死亡的主要原因。对于大多数患者，该疾病在晚期首次诊断，5年生存率低（< 30%）。尽管化疗的进展，治愈率在过去的几十年中并没有显着提高，局部疾病患者（80-90%）和远处转移患者（5-20%）之间的长期生存率的显着差异表明，需要一种适用于高危人群的非侵入性，但有效的测试来检测早期卵巢癌。基于先前在区分恶性和良性卵巢肿块方面的研究，该项目旨在将人工神经网络（ANN）技术应用于基于各种血清标志物和其他临床输入的早期卵巢癌筛查问题。最近完成的第一阶段试点项目通过以下方式验证了可行性：（1）收集来自合作组织的现有数据，（2）分析相关生物标志物的预测价值，（3）开发初步ANN，以及（4）使用独立测试数据验证ANN。第一阶段的成功完成现在有利于第二阶段活动的开始，以开发筛选系统的生产版本，并通过多项临床研究启动大规模验证。拟议的商业应用：人工神经网络软件能够检测早期卵巢癌的特异性，灵敏度和预测价值的替代技术有足够的改善，将有明确的商业价值，在筛选高危人群。Horns目前提供一种基于互联网的临床信息处理服务，称为ProstAsure，使用ANN技术开发，用于检测前列腺癌。