PDF(Pubmed)
Abstract：
BACKGROUND: Deep learning models (DLMs) using preoperative computed tomography (CT) imaging have shown promise in predicting outcomes following abdominal wall reconstruction (AWR), including component separation, wound complications, and pulmonary failure. This study aimed to apply these methods in predicting hernia recurrence and to evaluate if incorporating additional clinical data would improve the DLM\'s predictive ability.
METHODS: Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence.
RESULTS: The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively.
CONCLUSIONS: The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.
METHODS: Patients were identified from a prospectively maintained single-institution database. Those who underwent AWR with available preoperative CTs were included, and those with < 18 months of follow up were excluded. Patients were separated into a training (80%) set and a testing (20%) set. A DLM was trained on the images only, and another DLM was trained on demographics only: age, sex, BMI, diabetes, and history of tobacco use. A mixed-value DLM incorporated data from both. The DLMs were evaluated by the area under the curve (AUC) in predicting recurrence.
RESULTS: The models evaluated data from 190 AWR patients with a 14.7% recurrence rate after an average follow up of more than 7 years (mean ± SD: 86 ± 39 months; median [Q1, Q3]: 85.4 [56.1, 113.1]). Patients had a mean age of 57.5 ± 12.3 years and were majority (65.8%) female with a BMI of 34.2 ± 7.9 kg/m2. There were 28.9% with diabetes and 16.8% with a history of tobacco use. The AUCs for the imaging DLM, clinical DLM, and combined DLM were 0.500, 0.667, and 0.604, respectively.
CONCLUSIONS: The clinical-only DLM outperformed both the image-only DLM and the mixed-value DLM in predicting recurrence. While all three models were poorly predictive of recurrence, the clinical-only DLM was the most predictive. These findings may indicate that imaging characteristics are not as useful for predicting recurrence as they have been for other AWR outcomes. Further research should focus on understanding the imaging characteristics that are identified by these DLMs and expanding the demographic information incorporated in the clinical-only DLM to further enhance the predictive ability of this model.
摘要：
背景：使用术前计算机断层扫描（CT）成像的深度学习模型（DLM）在预测腹壁重建（AWR）后的结果方面显示出希望，包括组件分离，伤口并发症,和肺衰竭。本研究旨在将这些方法应用于预测疝复发，并评估纳入额外的临床数据是否会提高DLM的预测能力。
方法：从前瞻性维护的单机构数据库中确定患者。那些接受AWR并有术前CTs的患者被包括在内，随访<18个月的患者被排除在外.将患者分成训练(80%)组和测试(20%)组。仅在图像上训练了DLM，另一个DLM只接受了人口统计学方面的培训：年龄，性别,BMI,糖尿病,和烟草使用的历史。混合值DLM合并了来自两者的数据。通过曲线下面积（AUC）评估DLM预测复发。
结果：这些模型评估了190例AWR患者的数据，这些患者平均随访超过7年（平均±SD：86±39个月；中位数[Q1,Q3]：85.4[56.1,113.1]），复发率为14.7%。患者的平均年龄为57.5±12.3岁，大多数为女性（65.8％），BMI为34.2±7.9kg/m2。有28.9%的人患有糖尿病，16.8%的人有烟草使用史。成像DLM的AUC，临床DLM，合并DLM分别为0.500、0.667和0.604。
结论：在预测复发方面，仅临床DLM优于仅图像DLM和混合值DLM。虽然这三个模型对复发的预测都很差，仅临床的DLM最具预测性.这些发现可能表明，成像特征对于预测复发不如其他AWR结果有用。进一步的研究应集中于理解这些DLM识别的成像特征，并扩展仅临床DLM中包含的人口统计信息，以进一步增强该模型的预测能力。
方法：从前瞻性维护的单机构数据库中确定患者。那些接受AWR并有术前CTs的患者被包括在内，随访<18个月的患者被排除在外.将患者分成训练(80%)组和测试(20%)组。仅在图像上训练了DLM，另一个DLM只接受了人口统计学方面的培训：年龄，性别,BMI,糖尿病,和烟草使用的历史。混合值DLM合并了来自两者的数据。通过曲线下面积（AUC）评估DLM预测复发。
结果：这些模型评估了190例AWR患者的数据，这些患者平均随访超过7年（平均±SD：86±39个月；中位数[Q1,Q3]：85.4[56.1,113.1]），复发率为14.7%。患者的平均年龄为57.5±12.3岁，大多数为女性（65.8％），BMI为34.2±7.9kg/m2。有28.9%的人患有糖尿病，16.8%的人有烟草使用史。成像DLM的AUC，临床DLM，合并DLM分别为0.500、0.667和0.604。
结论：在预测复发方面，仅临床DLM优于仅图像DLM和混合值DLM。虽然这三个模型对复发的预测都很差，仅临床的DLM最具预测性.这些发现可能表明，成像特征对于预测复发不如其他AWR结果有用。进一步的研究应集中于理解这些DLM识别的成像特征，并扩展仅临床DLM中包含的人口统计信息，以进一步增强该模型的预测能力。
