The transformational potential of molecular radiomics

Currie, Geoffrey; Hawk, K Elizabeth; Rohren, Eric M.

doi:10.1002/jmrs.626

Cited by 3 publications

(3 citation statements)

References 39 publications

(109 reference statements)

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“…In recent years, radiomics and deep learning have helped identify early signs of metastasis and predict the likelihood of cancer recurrence ( Ferreira-Junior et al, 2020 , Jalalifar et al, 2022 ; Bang et al, 2023 ). Radiomics involves the extraction of quantitative features from medical images to identify subtle patterns that are not visible to the naked eye ( Guiot et al, 2022 ; Currie & Rohren, 2022 ; Currie, Hawk & Rohren, 2023 ). Deep learning refers to training deep neural networks, such as conventional convolutional neural network (CNN) on large datasets, and fine-tuning them on smaller datasets to improve the predictive ability ( Hosny et al, 2018 ; Liu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

Han,

Wang,

et al. 2024

PeerJ

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Background Occult lymph node metastasis (OLNM) is an essential prognostic factor for early-stage tongue cancer (cT1-2N0M0) and a determinant of treatment decisions. Therefore, accurate prediction of OLNM can significantly impact the clinical management and outcomes of patients with tongue cancer. The aim of this study was to develop and validate a multiomics-based model to predict OLNM in patients with early-stage tongue cancer. Methods The data of 125 patients diagnosed with early-stage tongue cancer (cT1-2N0M0) who underwent primary surgical treatment and elective neck dissection were retrospectively analyzed. A total of 100 patients were randomly assigned to the training set and 25 to the test set. The preoperative contrast-enhanced computed tomography (CT) and clinical data on these patients were collected. Radiomics features were extracted from the primary tumor as the region of interest (ROI) on CT images, and correlation analysis and the least absolute shrinkage and selection operator (LASSO) method were used to identify the most relevant features. A support vector machine (SVM) classifier was constructed and compared with other machine learning algorithms. With the same method, a clinical model was built and the peri-tumoral and intra-tumoral images were selected as the input for the deep learning model. The stacking ensemble technique was used to combine the multiple models. The predictive performance of the integrated model was evaluated for accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC), and compared with expert assessment. Internal validation was performed using a stratified five-fold cross-validation approach. Results Of the 125 patients, 41 (32.8%) showed OLNM on postoperative pathological examination. The integrated model achieved higher predictive performance compared with the individual models, with an accuracy of 84%, a sensitivity of 100%, a specificity of 76.5%, and an AUC-ROC of 0.949 (95% CI [0.870–1.000]). In addition, the performance of the integrated model surpassed that of younger doctors and was comparable to the evaluation of experienced doctors. Conclusions The multiomics-based model can accurately predict OLNM in patients with early-stage tongue cancer, and may serve as a valuable decision-making tool to determine the appropriate treatment and avoid unnecessary neck surgery in patients without OLNM.

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Section: Introductionmentioning

confidence: 99%

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

Han,

Wang,

et al. 2024

PeerJ

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“…15,16 With the rise of radiomics, computer-aided diagnosis technology based on machine learning for medical image has played an important role in clinical practice. [17][18][19] Automatic diagnostic techniques greatly reduce the pressure on physicians to read films, provide them with information that cannot be observed by the naked eye, and assist them in making correct decisions based on medical images. 20,21 To the best of our knowledge, most current studies on radiomics in bone metastasis of lung cancer only considers the diagnostic value of radiomics features or clinical features without investigating their added value.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, faced with a large number of medical images in the clinic, physicians may have differing interpretations of medical images due to fatigue and personal experience, leading to inefficiencies and inconsistencies in manual diagnoses 15,16 . With the rise of radiomics, computer‐aided diagnosis technology based on machine learning for medical image has played an important role in clinical practice 17–19 . Automatic diagnostic techniques greatly reduce the pressure on physicians to read films, provide them with information that cannot be observed by the naked eye, and assist them in making correct decisions based on medical images 20,21 …”

Section: Introductionmentioning

confidence: 99%

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Wang,

Chen,

Qiu

et al. 2023

Medical Physics

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BackgroundBone metastasis is a common event in lung cancer progression. Early diagnosis of lung malignant tumor with bone metastasis is crucial for selecting effective treatment strategies. However, 14.3% of patients are still difficult to diagnose after SPECT/CT examination.PurposeMachine learning analysis of [99mTc]‐methylene diphosphate (99mTc‐MDP) SPECT/CT scans to distinguish bone metastases from benign bone lesions in patients with lung cancer.MethodsOne hundred forty‐one patients (69 with bone metastases and 72 with benign bone lesions) were randomly assigned to the training group or testing group in a 7:3 ratio. Lesions were manually delineated using ITK‐SNAP, and 944 radiomics features were extracted from SPECT and CT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the radiomics features in the training set, and the single/bimodal radiomics models were established based on support vector machine (SVM). To further optimize the model, the best bimodal radiomics features were combined with clinical features to establish an integrated Radiomics‐clinical model. The diagnostic performance of models was evaluated using receiver operating characteristic (ROC) curve and confusion matrix, and performance differences between models were evaluated using the Delong test.ResultsThe optimal radiomics model comprised of structural modality (CT) and metabolic modality (SPECT), with an area under curve (AUC) of 0.919 and 0.907 for the training and testing set, respectively. The integrated model, which combined SPECT, CT, and two clinical features, exhibited satisfactory differentiation in the training and testing set, with AUC of 0.939 and 0.925, respectively.ConclusionsThe machine learning can effectively differentiate between bone metastases and benign bone lesions. The Radiomics‐clinical integrated model demonstrated the best performance.

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Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models

Song,

Wang,

Cai

et al. 2024

Br J Cancer

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The transformational potential of molecular radiomics

Cited by 3 publications

References 39 publications

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

A CT-based integrated model for preoperative prediction of occult lymph node metastasis in early tongue cancer

Machine learning based on SPECT/CT to differentiate bone metastasis and benign bone lesions in lung malignancy patients

Stratifying ICIs-responsive tumor microenvironment in HCC: from parsing out immune-hypoxic crosstalk to clinically applicable MRI-radiomics models

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