PDF(Pubmed)
Abstract：
Desmoid tumors (DTs) are non-metastasizing and locally aggressive soft-tissue mesenchymal neoplasms. Those that become enlarged often become locally invasive and cause significant morbidity. DTs have a varied pattern of clinical presentation, with up to 50-60% not growing after diagnosis and 20-30% shrinking or even disappearing after initial progression. Enlarging tumors are considered unstable and progressive. The management of symptomatic and enlarging DTs is challenging, and primarily consists of chemotherapy. Despite wide surgical resection, DTs carry a rate of local recurrence as high as 50%. There is a consensus that contrast-enhanced magnetic resonance imaging (MRI) or, alternatively, computerized tomography (CT) is the preferred modality for monitoring DTs. Each uses Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1), which measures the largest diameter on axial, sagittal, or coronal series. This approach, however, reportedly lacks accuracy in detecting response to therapy and fails to detect tumor progression, thus calling for more sophisticated methods. The objective of this study was to detect unique features identified by deep learning that correlate with the future clinical course of the disease. Between 2006 and 2019, 51 patients (mean age 41.22 ± 15.5 years) who had a tissue diagnosis of DT were included in this retrospective single-center study. Each had undergone at least three MRI examinations (including a pretreatment baseline study), and each was followed by orthopedic oncology specialists for a median of 38.83 months (IQR 44.38). Tumor segmentations were performed on a T2 fat-suppressed treatment-naive MRI sequence, after which the segmented lesion was extracted to a three-dimensional file together with its DICOM file and run through deep learning software. The results of the algorithm were then compared to clinical data collected from the patients\' medical files. There were 28 males (13 stable) and 23 females (15 stable) whose ages ranged from 19.07 to 83.33 years. The model was able to independently predict clinical progression as measured from the baseline MRI with an overall accuracy of 93% (93 ± 0.04) and ROC of 0.89 ± 0.08. Artificial intelligence may contribute to risk stratification and clinical decision-making in patients with DT by predicting which patients are likely to progress.
摘要：
纤维瘤（DTs）是非转移性和局部侵袭性软组织间充质肿瘤。那些扩大的通常会成为局部侵入性的，并导致严重的发病率。DTs有不同的临床表现模式，高达50-60％的人在诊断后不生长，20-30％的人在最初进展后萎缩甚至消失。增大的肿瘤被认为是不稳定的和进行性的。有症状和扩大的DTs的管理是具有挑战性的，主要包括化疗。尽管进行了广泛的手术切除，DTs的局部复发率高达50％。有一个共识，对比增强磁共振成像（MRI）或，或者，计算机断层扫描（CT）是监测DTs的首选方式。每个人都使用实体瘤1.1版（RECIST1.1）中的反应评估标准，测量轴向最大直径，矢状,或日冕系列。这种方法,然而,据报道，在检测对治疗的反应方面缺乏准确性，并且无法检测肿瘤进展，因此需要更复杂的方法。这项研究的目的是检测通过深度学习识别的与疾病未来临床过程相关的独特特征。在2006年至2019年之间，该回顾性单中心研究纳入了51例组织诊断为DT的患者（平均年龄41.22±15.5岁）。每个人都接受了至少三次MRI检查（包括预处理基线研究），每位患者均由骨科肿瘤专科医生随访,中位随访时间为38.83个月(IQR44.38).在T2脂肪抑制治疗初治MRI序列上进行肿瘤分割，之后,将分割的病变与其DICOM文件一起提取到三维文件中,并通过深度学习软件运行.然后将算法的结果与从患者医疗档案中收集的临床数据进行比较。男性28例（稳定13例）和女性23例（稳定15例），年龄在19.07至83.33岁之间。该模型能够独立地预测从基线MRI测量的临床进展,总体准确度为93%(93±0.04),ROC为0.89±0.08。通过预测哪些患者可能会进展，人工智能可能有助于DT患者的风险分层和临床决策。
