Development of an artificial intelligence-based precision medicine decision support system for radiogenomics data sets

Abdulvahap Pınar; Ahmet Kadir Arslan; Emek Güldoğan

doi:10.7197/cmj.1713462

EN TR

Development of an artificial intelligence-based precision medicine decision support system for radiogenomics data sets

Abstract

Aim: This study aims to apply deep learning algorithms for superpixel segmentation, herbaceous thresholding, and disease reference position estimation from DICOM images and clinical data of Non-Small Cell Lung Cancer (NSCLC) patients. Quantitative imaging data was integrated with clinical information. Various machine learning algorithms were employed to identify biomarkers and evaluate classification performance based on clinical data, imaging data, and their combination, assessing the model improvement rates. Materials and Methods: The clinical dataset included 43 patients with and 168 without an Epidermal Growth Factor Receptor (EGFR) mutation, and 38 with and 173 without a Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS) mutation, totaling 211 NSCLC cases. A total of 2,231 images were analyzed. Using the VGG16 deep learning model, 25,088 features were extracted from each image. XGBoost, CatBoost, Random Forest, and Support Vector Machine (SVM) classification algorithms were used to predict mutation status. Findings: Clinical data revealed significant differences in mutation status among NSCLC patients. The Random Forest algorithm was employed for feature selection, identifying the 50 most important variables for model training. XGBoost and CatBoost achieved the highest classification performance, with results for accuracy, balanced accuracy, precision, sensitivity, F1-score, and ROC-AUC as follows: 0.965 ± 0.015, 0.954 ± 0.021, 0.953 ± 0.024, 0.994 ± 0.007, 0.973 ± 0.011, and 0.990 ± 0.005, respectively. Result: The study’s findings demonstrate that XGBoost and CatBoost models were highly effective in predicting KRAS mutation status from imaging data. CatBoost also performed best in determining EGFR mutation status, outperforming other machine learning methods.

Keywords

Radyogenomik veri setleri için yapay zeka tabanlı bir hassas tıp karar destek sisteminin geliştirilmesi

Öz

Amaç: Bu çalışma, küçük hücreli dışı akciğer kanseri (KHDAK) hastalarına ait DICOM görüntüleri ve klinik verilerden süperpiksel segmentasyonu, otsu eşikleme ve hastalık referans pozisyonu tahmini için derin öğrenme algoritmalarını uygulamayı amaçlamaktadır. Nicel görüntüleme verileri, klinik bilgilerle entegre edilmiştir. Klinik veriler, görüntüleme verileri ve bunların kombinasyonuna dayalı olarak biyobelirteçleri tanımlamak ve sınıflandırma performansını değerlendirmek için çeşitli makine öğrenmesi algoritmaları kullanılmış; model iyileşme oranları değerlendirilmiştir. Gereç ve Yöntem: Klinik veri seti, Epidermal Büyüme Faktörü Reseptör (EGFR) mutasyonu olan 43 ve olmayan 168, Kirsten Rat Sarkom Viral Onkogen Homoloğu (KRAS) mutasyonu olan 38 ve olmayan 173 hasta olmak üzere toplam 211 KHDAK vakasını içermektedir. Toplam 2.231 görüntü analiz edilmiştir. VGG16 derin öğrenme modeli kullanılarak her bir görüntüden 25.088 özellik çıkarılmıştır. Mutasyon durumunu tahmin etmek için XGBoost, CatBoost, Random Forest ve Destek Vektör Makineleri (SVM) sınıflandırma algoritmaları kullanılmıştır. Bulgular: Klinik veriler, KHDAK hastaları arasında mutasyon durumlarına göre anlamlı farklılıklar olduğunu ortaya koymuştur. Model eğitimi için en önemli 50 değişkeni belirlemek amacıyla Random Forest algoritması ile özellik seçimi yapılmıştır. XGBoost ve CatBoost, en yüksek sınıflandırma performansını elde etmiştir. Elde edilen doğruluk, dengelenmiş doğruluk, kesinlik, duyarlılık, F1 skoru ve ROC-AUC değerleri sırasıyla şu şekildedir: 0.965 ± 0.015, 0.954 ± 0.021, 0.953 ± 0.024, 0.994 ± 0.007, 0.973 ± 0.011 ve 0.990 ± 0.005. Sonuç: Çalışmanın bulguları, XGBoost ve CatBoost modellerinin görüntüleme verilerinden KRAS mutasyon durumunu tahmin etmede son derece etkili olduğunu göstermektedir. Ayrıca CatBoost, EGFR mutasyon durumunun belirlenmesinde de diğer makine öğrenmesi yöntemlerinden daha iyi performans göstermiştir.

Anahtar Kelimeler

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Sağlık Bilişimi ve Bilişim Sistemleri

Bölüm

Araştırma Makalesi

Yazarlar

Abdulvahap Pınar ^*
0000-0002-3662-2579
Türkiye

Ahmet Kadir Arslan
0000-0001-8626-9542
Türkiye

Emek Güldoğan
0000-0002-5436-8164
Türkiye

Yayımlanma Tarihi

21 Haziran 2025

Gönderilme Tarihi

3 Haziran 2025

Kabul Tarihi

11 Haziran 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 47 Sayı: 2

DOI

https://doi.org/10.7197/cmj.1713462

IZ

https://izlik.org/JA97FP27TJ

Kaynak Göster

RIS / Bibtex

AMA

1.Pınar A, Arslan AK, Güldoğan E. Development of an artificial intelligence-based precision medicine decision support system for radiogenomics data sets. CMJ. 2025;47(2):35-44. doi:10.7197/cmj.1713462