Mohamad Sehmi Muhammad Nurmahir, Ahmad Fauzi Mohammad Faizal, Wan Ahmad Wan Siti Halimatul Munirah, Wan Ling Chan Elaine
Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, 63100, Malaysia.
International Medical University, Kuala Lumpur, Kuala Lumpur, Malaysia.
F1000Res. 2022 Nov 1;10:1057. doi: 10.12688/f1000research.73161.2. eCollection 2021.
Pancreatic cancer is one of the deadliest forms of cancer. The cancer grades define how aggressively the cancer will spread and give indication for doctors to make proper prognosis and treatment. The current method of pancreatic cancer grading, by means of manual examination of the cancerous tissue following a biopsy, is time consuming and often results in misdiagnosis and thus incorrect treatment. This paper presents an automated grading system for pancreatic cancer from pathology images developed by comparing deep learning models on two different pathological stains. A transfer-learning technique was adopted by testing the method on 14 different ImageNet pre-trained models. The models were fine-tuned to be trained with our dataset. From the experiment, DenseNet models appeared to be the best at classifying the validation set with up to 95.61% accuracy in grading pancreatic cancer despite the small sample set. To the best of our knowledge, this is the first work in grading pancreatic cancer based on pathology images. Previous works have either focused only on detection (benign or malignant), or on radiology images (computerized tomography [CT], magnetic resonance imaging [MRI] etc.). The proposed system can be very useful to pathologists in facilitating an automated or semi-automated cancer grading system, which can address the problems found in manual grading.
胰腺癌是最致命的癌症形式之一。癌症分级定义了癌症扩散的侵袭性，并为医生进行正确的预后和治疗提供依据。目前胰腺癌分级的方法是在活检后通过对癌组织进行人工检查，这种方法耗时且常常导致误诊，进而造成治疗不当。本文提出了一种基于病理图像的胰腺癌自动分级系统，该系统通过比较两种不同病理染色的深度学习模型开发而成。通过在14种不同的ImageNet预训练模型上测试该方法，采用了迁移学习技术。这些模型经过微调后使用我们的数据集进行训练。从实验结果来看，尽管样本集较小，但DenseNet模型在对验证集进行胰腺癌分级时表现最佳，准确率高达95.61%。据我们所知，这是第一项基于病理图像对胰腺癌进行分级的工作。先前的工作要么仅专注于检测（良性或恶性），要么专注于放射学图像（计算机断层扫描[CT]、磁共振成像[MRI]等）。所提出的系统对于病理学家建立自动化或半自动化癌症分级系统非常有用，该系统可以解决人工分级中发现的问题。
