Pattanayak Satyabrata, Singh Tripty, Kumar Rishabh
Department of Computer Sciences and Engineering, Amrita School of Computing, Amrita Vishwavidyapeetham, Bengaluru, Bengaluru, Karnataka, 560067, India.
Radiation Oncology, Amrita Hospital, Faridabad, India.
Sci Rep. 2025 Jul 30;15(1):27844. doi: 10.1038/s41598-025-08565-3.
Neoadjuvant therapy plays a pivotal role in breast cancer treatment, particularly for patients aiming to conserve their breast by reducing tumor size pre-surgery. The ultimate goal of this treatment is achieving a pathologic complete response (pCR), which signifies the complete eradication of cancer cells, thereby lowering the likelihood of recurrence. This study introduces a novel predictive approach to identify patients likely to achieve pCR using radiomic features extracted from MR images, enhanced by the InceptionV3 model and cutting-edge validation methodologies.
In our study, we gathered data from 255 unique Patient IDs sourced from the -SPY 2 MRI database with the goal of classifying pCR (pathological complete response). Our research introduced two key areas of novelty.Firstly, we explored the extraction of advanced features from the dcom series such as Area, Perimeter, Entropy, Intensity of the places where the intensity is more than the average intensity of the image. These features provided deeper insights into the characteristics of the MRI data and enhanced the discriminative power of our classification model.Secondly, we applied these extracted features along with combine pixel array of the dcom series of each patient to the numerous deep learning model along with InceptionV3 (GoogleNet) model which provides the best accuracy. To optimize the model's performance, we experimented with different combinations of loss functions, optimizer functions, and activation functions. Lastly, our classification results were subjected to validation using accuracy, AUC, Sensitivity, Specificity and F1 Score. These evaluation metrics provided a robust assessment of the model's performance and ensured the reliability of our findings.
The successful combination of advanced feature extraction, utilization of the InceptionV3 model with tailored hyperparameters, and thorough validation using cutting-edge techniques significantly enhanced the accuracy and reliability of our pCR classification study. By adopting a collaborative approach that involved both radiologists and the computer-aided system, we achieved superior predictive performance for pCR, as evidenced by the impressive values obtained for the area under the curve (AUC) at 0.91 having an accuracy of .92.
Overall, the combination of advanced feature extraction, leveraging the InceptionV3 model with customized hyperparameters, and rigorous validation using state-of-the-art techniques contributed to the accuracy and credibility of our pCR classification study.
新辅助治疗在乳腺癌治疗中起着关键作用，特别是对于那些旨在通过在手术前缩小肿瘤大小来保留乳房的患者。这种治疗的最终目标是实现病理完全缓解（pCR），这意味着癌细胞被完全根除，从而降低复发的可能性。本研究引入了一种新颖的预测方法，使用从磁共振图像中提取的放射组学特征来识别可能实现pCR的患者，并通过InceptionV3模型和前沿验证方法进行强化。
在我们的研究中，我们从-SPY 2磁共振成像数据库收集了255个唯一患者ID的数据，目的是对pCR（病理完全缓解）进行分类。我们的研究引入了两个关键的创新领域。首先，我们探索了从dcom系列中提取高级特征，如面积、周长、熵、强度大于图像平均强度的区域的强度。这些特征为磁共振成像数据的特征提供了更深入的见解，并增强了我们分类模型的判别能力。其次，我们将这些提取的特征以及每个患者dcom系列的组合像素阵列应用于众多深度学习模型以及具有最佳准确率的InceptionV3（谷歌网络）模型。为了优化模型性能，我们试验了损失函数、优化器函数和激活函数的不同组合。最后，我们的分类结果使用准确率、AUC、灵敏度、特异性和F1分数进行验证。这些评估指标对模型性能进行了有力评估，并确保了我们研究结果的可靠性。
先进特征提取、使用具有定制超参数的InceptionV3模型以及使用前沿技术进行全面验证的成功结合，显著提高了我们pCR分类研究的准确性和可靠性。通过采用放射科医生和计算机辅助系统相结合的方法，我们实现了对pCR的卓越预测性能，曲线下面积（AUC）达到0.91，准确率为0.92，这一令人印象深刻的值证明了这一点。
总体而言，先进特征提取、利用具有定制超参数的InceptionV3模型以及使用先进技术进行严格验证的结合，提高了我们pCR分类研究的准确性和可信度。
