Machine learning techniques for metastatic tumor prediction

Abstract

Accurate prediction of tumor metastasis is crucial for cancer prognosis and treatment. This study explores the application of artificial intelligence techniques, specif ically neural networks and Principal Component Analysis (PCA), for predicting tumor metastasis based on histopathological images. The aim is to identify an effective approach that balances accuracy and computational efficiency. The study begins with a comparative analysis between PCA and a Convolutional Neural Network (CNN) for image classification, using the ISIC dataset and MNIST dataset. To further investigate potential benefits, a combined model using PCA and CNN is developed. PCA is employed as a dimensionality reduction technique to enhance computational efficiency. The combined model demonstrates promising results, reducing computational time while maintaining accuracy. In the second part of the study, metastatic prediction is addressed using Whole Slide Images (WSI) obtained from Sahlgrenska University Hospital. Preprocessing involves dividing WSIs into smaller tiles and extracting relevant features such as breslow depth and metastatic status. Two models are utilized: a modified ResNet18 and a shallower network (ConvNet2/ConvNet3) with varying inputs. Training and evaluation are performed using a 4-fold cross-validation approach and mini-batches. It was observed that the PCA algorithm performed similar to the neural networks but was outperformed in terms of computational time. The combined model showcased the potential in reducing computational time while maintaining accuracy. The combined model showcased the potential in reducing computational time while maintaining accuracy when the there are a large amount of available data with high complexity. Lastly, the study showed that when the data are limited, the best approach was to utilize a neural network based on image data and clinical data to enhance metastatic prediction. Overall, this thesis emphasizes the potential of neural networks based on image and clinical data for enhancing metastatic prediction, providing valuable insights for improved cancer prognosis and treatment decision-making.