Gebouský Petr, Kárný Miroslav, Krízová Hana, Wald Martin
Department of Adaptive Systems, Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, P.O. Box 18, 182 08 Prague 8, Czech Republic.
Comput Biol Med. 2009 Jan;39(1):1-7. doi: 10.1016/j.compbiomed.2008.10.003. Epub 2008 Nov 29.
Secondary lymphedema of upper limbs, a frequent complication after a breast cancer therapy, can be successfully treated only when diagnosed at an early, ideally latent, stage. Lymphoscintigraphy is a promising candidate to this purpose. A slow lymphatic dynamics of upper limbs allows, however, a routine collection at most three images reflecting it. This makes an exploitation of lymphoscintigraphy to early-stage diagnosis a complex task. Recently, a Bayesian methodology extracting diagnostic information from the available sparse data has been developed. It properly detects lymphedema occurrence but not a desirable disease staging. The present paper proposes Bayesian diagnostic processing of lymphoscintigraphic and routine clinical data. Its staging ability was tested on diagnostic data of 88 women at the age of 39-84 years (60.2+/-10.4) with a suspicion of unilateral secondary lymphedema of upper limbs caused by a breast cancer treatment. Less than 20 of them had simply detectable disease stages. Information about accumulation dynamics of the lymphatic system contained in lymphoscintigraphic images was quantified via estimation of a simplified accumulation model [P. Gebouský, M. Kárný, A. Quinn, Lymphoscintigraphy of upper limbs: a Bayesian framework, in: J.M. Bernardo, M.J. Bayarri, J.O. Berger (Eds.), Bayesian Statistics, vol. 7, University Press, Oxford, 2003, pp. 543-552]. The sole use of this approach, referred as "Bayesian quantitative lymphoscintigraphy", was found insufficient for a finer staging of the disease to typical categories (healthy, latent, reversible, spontaneously irreversible, elephantiasis). For this reason, the results of Bayesian quantitative lymphoscintigraphy were attached to routinely available qualitative lymphoscintigraphic inspection and clinical data. These combined data were modelled by normal probabilistic mixtures. Their Bayesian estimates were used for a computerized disease staging. The resulting model predicts expert's conclusions on the presence of a lymphedema in 95% cases. A finer staging is successful in 85% cases of suspicious limbs. Model cross-validation and a closer look on patients' data indicate that the combined data are still insufficiently informative. It calls for the further improvements of the inspection methods. Even under the current inspection conditions, the proposed processing provides clinicians a reliable quantitative "second" opinion on the disease staging.
上肢继发性淋巴水肿是乳腺癌治疗后常见的并发症，只有在早期（理想情况下是潜伏期）诊断出来才能成功治疗。淋巴闪烁造影术有望用于此目的。然而，上肢淋巴动力学缓慢，常规采集最多只能获得反映其情况的三张图像。这使得利用淋巴闪烁造影术进行早期诊断成为一项复杂的任务。最近，已开发出一种从可用的稀疏数据中提取诊断信息的贝叶斯方法。它能正确检测淋巴水肿的发生，但无法实现理想的疾病分期。本文提出对淋巴闪烁造影和常规临床数据进行贝叶斯诊断处理。对88名年龄在39 - 84岁（平均60.2±10.4岁）、疑似因乳腺癌治疗导致单侧上肢继发性淋巴水肿的女性的诊断数据进行了分期能力测试。其中不到20人处于简单可检测的疾病阶段。通过估计一个简化的蓄积模型（[P. Gebouský, M. Kárný, A. Quinn, 上肢淋巴闪烁造影术：一个贝叶斯框架，载于：J.M. Bernardo, M.J. Bayarri, J.O. Berger（编），《贝叶斯统计学》，第7卷，牛津大学出版社，牛津，2003年，第543 - 552页]）对淋巴闪烁造影图像中包含的淋巴系统蓄积动力学信息进行了量化。仅使用这种方法（称为“贝叶斯定量淋巴闪烁造影术”）发现不足以将疾病更精细地分期为典型类别（健康、潜伏、可逆、自发不可逆、象皮肿）。因此，将贝叶斯定量淋巴闪烁造影术的结果与常规可用的定性淋巴闪烁造影检查和临床数据相结合。这些组合数据通过正态概率混合模型进行建模。其贝叶斯估计用于计算机化疾病分期。所得模型在95%的病例中预测了专家关于淋巴水肿存在的结论。在85%的可疑肢体病例中成功实现了更精细的分期。模型交叉验证以及对患者数据的进一步观察表明，组合数据的信息量仍然不足。这需要进一步改进检查方法。即使在当前的检查条件下，所提出的处理方法也为临床医生提供了关于疾病分期的可靠定量“第二意见”。
