Prospective Randomized Control Study for Exclusion of Negative Appendicitis; Deep Learning Model, Information of Appendix (IA) Versus Non-radiologists
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 568
- 试验地点
- 1
- 主要终点
- Negative appendectomy rate
研究概览
简要总结
the investigators's study group has developed a fully automated 3D convolutional neural network (CNN)-based diagnostic framework using information of appendix (IA) model to identify non-appendicitis and simple and complicated appendicitis on CT scan images based on the two-stage binary classification algorithm, as a clinician does for deciding treatment. The dataset was built from a large population of patients visiting emergency departments who underwent intravenous contrast-enhanced abdominopelvic CT examinations to evaluate abdominal pain in the right or lower quadrant area as the chief complaint. Recently, the IA model was externally validated using a dataset of multicenter institutions through data exfiltration. In this study, the investigators hypothesized that the IA model would show a comparable negative appendicitis rate of <10% non-inferior margins compared to non-radiologists with a shorter interpretation time in a prospectively randomized dataset.
详细描述
Development of information of appendix model
- This study used a pretrained information of appendix (IA) model based on a fully automated diagnostic framework to predict three classes with probability and feature mapping: non-appendicitis, simple appendicitis, and complicated appendicitis.
The pipeline of IA model embedding parameters learned from the 3D CT image of 7,147 patients consisted of a two-stage binary algorithm connected to transfer learning with three 3D CNN models: DenseNet, EfficientNet, and ResNet.
- A two-stage binary algorithm for transfer learning was applied to learn inherent patterns from the 3D images of three true classes, as way a clinician does when deciding treatment for appendicitis. In the first step of the pipeline, a Stage 1 classification model was developed to identify non-appendicitis vs. appendicitis. In the second step of the pipeline, the Stage 2 classification model identified simple vs. complicated appendicitis from the data transferred with the trainable parameters learned in Stage 1.
Currently, the final IA model has been externally validated using a never-before-seen dataset from an outside institution with broad eligibility criteria for patients who visited the emergency room with abdominal pain and underwent abdominopelvic CT due to clinical suspicion of acute appendicitis.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Diagnostic
- 盲法
- Single (Participant)
盲法说明
All image data were anonymized with the deletion of Dicom header information such as sex, age, and CT protocol. Participants were given CT slices that were identical to the range of the VOI for appendicitis generated automatically through the extraction pipeline of the IA model. A total of 20 axial CT slice images of a 5-mm cut were masked with blinded truth labeling and a blackened background on the outside of the body surface.
入排标准
- 年龄范围
- 12 Years 至 —（Child, Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Inclusion criteria for broad eligibility were applied to reflect that the CT utilization rate in the emergency room has rapidly expanded, presumably in many other institutions where physicians maintain a reasonably sensitive standpoint in raising a clinical suspicion of appendicitis as a cause of abdominal pain and then use CT as an imaging test to confirm or rule out appendicitis.
- •When the imaging protocol parameters were as follows: abdomen or pelvis (intravenous contrast, 2 mg/kg, maximum 160 mL), scan timing (portal venous phase), range (from 4 cm above the liver dome to 1 cm below the ischial tuberosity), radiation dose (tube potential, KVP from 100 to 120), pitch 1.75:1, and reconstruction (5 mm, cut slice for adults; 3 mm, cut slice for children under 12 years old), anonymized CT images of patients were referred to a randomized dataset.
排除标准
- •Patients who did not fulfill the CT imaging protocol were excluded in detail as follows:
- •i) Failure to meet the CT protocol criteria of this study: liver CT, biliary CT, etc. (if contrast phase was different); ureter CT, etc. (if contrast media was not used and the reconstruction method was different); non-enhanced CT (when contrast media was not used); and appendix CT or low-dose CT (when radiation dose was low).
- •ii) When the quality of the CT image is significantly reduced, as follows: when blurring occurs (motion artifact) or metal artifact (when internal fixation is performed due to spinal surgery).
- •iii) when it was evident from the medical record review that clinical information suggested that APCT was performed due to the suspicion of a condition other than appendicitis, as follows: suspected acute cholecystitis due to RUQ tenderness and Murphy's sign; suspected urolithiasis due to flank pain and gross hematuria; suspected pancreatitis due to a history of pancreatitis; alcohol abuse; and suspected gynecological diseases due to vaginal discharge. Suspected panperitonitis due to whole abdominal tenderness, rebound tenderness, and unstable vital signs. Patients with acute cholecystitis, ureteral stones, pancreatitis, or acute peritonitis due to small bowel or colon perforation were also excluded.
- •iv) Patients younger than 10 years were excluded. Adolescent patients from 11 to 18 years old were included in the study if the exclusion criteria were not applicable.
- •v) diagnosed by ultrasound sonography vi) Patients who were transferred to the emergency department after a diagnosis of appendicitis at an outside hospital or ambulatory care were excluded.
- •vii) Patients with appendicitis who did not undergo surgical treatment because of the enrollment protocol of other ongoing studies.
- •viii) patients who had undergone an appendectomy
研究组 & 干预措施
IA model
A fully automated diagnostic framework based on 3D-CNN model to predict non-appendicitis, simple and complicated appendicitis
干预措施: Information of Appendix (IA) model (Device)
Non-radiologist
Ten non-radiologists participated in this study. CT image same to IA model was allocated to radiologist randomly.
结局指标
主要结局
Negative appendectomy rate
时间窗: Outcome measurement for four test items of non-radiologists will be assessed for an average of one year through study completion.
Description: False positive rate = FP /FP +TN FP: false positive, TN true negative Assessment of outcomes: To evaluate diagnostic performance in humans, four test items for non-radiologists were set up as follows: (1) Appendix visualization: Can you find the location of the appendix? ① Yes ② No (2) Exclusion of appendicitis: Can appendicitis be excluded from the CT images? ① Yes (non-appendicitis) ② No (simple or complicated appendicitis) (3) If your choice is "no (simple or complicated appendicitis)," is appendicitis accompanied by complication? ① Yes (complicated appendicitis) ② No (simple appendicitis) (4) Finally, what is the radiologic diagnosis based on CT images in patients presenting with acute right or lower abdominal pain in the ER? ① Non-appendicitis ② simple appendicitis ③ complicated appendicitis The NAR was calculated as the primary endpoint using the outcomes of (2) the answer sheet of the non-radiologist and the Stage 1 IA model-yielding class.
次要结局
未报告次要终点
研究者
Iltae Son
Principal Investigator
Hallym University Medical Center