AI in Oncology

Cancer Survival Prediction

Cox Proportional Hazards

Deep Survival Analysis

import torch.nn as nn

class DeepSurvivalModel(nn.Module):
    def __init__(self, input_dim, hidden_dims=[256, 128, 64]):
        super().__init__()
        layers = []
        prev = input_dim
        for dim in hidden_dims:
            layers.extend([nn.Linear(prev, dim), nn.BatchNorm1d(dim),
                          nn.ReLU(), nn.Dropout(0.3)])
            prev = dim
        layers.append(nn.Linear(prev, 1))
        self.network = nn.Sequential(*layers)

    def forward(self, x):
        return self.network(x)

Treatment Response Modeling

Radiomics Features

Tumor Evolution Tracking

Clonal Evolution

Evaluation

• C-index:
• AUROC for response prediction
• Clinical utility via decision curve analysis