Value of 18F-FDG PET/CT radiomics for predicting KRAS gene mutations in non-small cell lung cancer / 中华核医学与分子影像杂志

Objective:To assess the predictive efficacy of 18F-FDG PET/CT-based radiomics models for the mutation status of Kirsten rats sarcoma viral oncogene homolog (KRAS) in patients with non-small cell lung cancer (NSCLC). Methods:From January 2016 to January 2021, the 18F-FDG PET/CT images and KRAS testing of 258 NSCLC patients (180 males, 78 females; age: 33-91 years) in the First Affiliated Hospital of the Air Force Military Medical University were retrospectively analyzed. Patients were randomly divided into training set ( n=180) and validation set ( n=78) in the ratio of 7∶3. Tumor lesions on PET and CT images were drawn respectively, and the radiomics features of PET and CT lesions were extracted. The radiomics features were screened by least absolute shrinkage and selection operator (LASSO). CT radiomics score (RS) model, PET/CT RS model and composite models of PET/CT RS combined with screened clinical information were eventually developed. ROC curves were used to assess the predictive efficacy of these models. Results:The CT RS model included 4 radiomics features and the PET/CT RS model included 4 CT radiomics features and 8 PET radiomics features. The CT RS model and the PET/CT RS model both had significant differences in RS between KRAS mutant and wild-type patients in the training set and validation set ( z values: from -8.30 to -4.10, all P<0.001). In predicting KRAS mutations, the composite model of PET/CT RS combined with age showed AUCs of 0.879 and 0.852 in the training and validation sets respectively, which were higher than those of the CT RS model (0.813 and 0.770) and the PET/CT RS model (0.858 and 0.834). The accuracy of the composite model of PET/CT RS combined with age were 81.67%(147/180) and 79.49%(62/78) in the training set and validation set respectively, which were also higher than those of the CT RS model (75.00%(135/180) and 74.36%(58/78)) and the PET/CT RS model (78.89%(142/180) and 78.21%(61/78)). Conclusion:Models based on radiomics features can predict KRAS gene mutation status, and the composite model combining PET/CT RS and age can improve the prediction performance.

Value of delta radiomic based on contrast enhanced MRI to predict pathological complete response after neoadjuvant therapy for breast cancer / 中华放射学杂志

Objective:To investigate the value of delta radiomics based on longitudinal changes of dynamic contrast enhanced MRI (DCE-MRI) in predicting pathological complete response (pCR) after neoadjuvant therapy (NAT) for breast cancer.Methods:The clinicopathological and imaging data of 117 patients with breast cancer confirmed by surgical pathology from April 2019 to November 2021 at Jiangxi Cancer Hospital were analyzed retrospectively. All patients were female with 23?74 (48±10) years old. The patients were randomly divided into training (81 cases) and test sets (36 cases) at the ratio of 7∶3 according to the number of random seeds in the software. All patients underwent DCE-MRI before and after early NAT (2 courses). The maximum diameter relative regression value of breast tumors before and after early NAT (D%) was calculated and used to construct a conventional imaging model. The delta radiomic features were extracted based on pre-NAT and early-NAT (2 courses) DCE-MRI and selected by redundancy analysis and least absolute shrinkage and selection operator algorithm. A ten-fold cross-validation method was used to construct the delta radiomic model and Radscore was calculated for each patient. All patients were classified into pCR group and non-pCR group according to the surgical pathology after NAT. Significant clinicopathological variables were selected by univariate analysis and stepwise regression method. They were integrated with D% and Radscore to build the combined model and nomogram. The model performance in predicting pCR after NAT in breast cancer was evaluated by the receiver operating characteristic curve and the area under the curve (AUC), and the clinical utility of the models was compared by using clinical decision curves.Results:The combined model had the best diagnostic performance among the three models, with an AUC of 0.90 in the training set and 0.87 in the test set. The Radscore had the highest weight in the nomogram. In the training set, the diagnostic performance of the combined model and delta radiomics model were better than that of the conventional imaging model ( Z=?3.48, P=0.001; Z=2.54, P=0.011). The clinical decision curves showed an overall greater clinical benefit of the combined model compared with the conventional imaging model and delta radiomic model. Conclusions:The addition of significant clinicopathological variables and Radscore of delta radiomic model which represents the longitudinal changes in tumor heterogeneity to the conventional imaging model may improve the predictive ability of pCR. The delta radiomic may serve as a noninvasive biomarker for early prediction of NAT response.

Value of CT radiomic features in differential diagnosis of lung metastases / 国际肿瘤学杂志

Objective:To distinguish lung metastases of different origin by constructing a classification model according to CT radiomics features.Methods:A total of 226 patients with lung metastases of gastric cancer, breast cancer and kidney cancer attending Chongqing Red Cross Hospital from January 2015 to July 2020, with a total of 402 metastases, were randomly divided into a training cohort (training set, 136 patients, 280 metastases) and a validation cohort (validation set, 90 patients, 122 metastases) by the hold-out method. In addition, 68 patients with lung metastases (138 lung metastases in total) attending Chongqing Red Cross Hospital from August 2020 to April 2022 were matched as an external test cohort (test set). Region of interest segmentation was performed by two experienced radiologists independently and manually without clinical information to construct the model by using LASSO screening for the best radiomic features. Support vector machine (SVM) and random forest (RF) were selected to build dichotomous and trichotomous models respectively. The receiver operating characteristic curve was used to evaluate the classification efficiency of both models.Results:There were no statistically significant differences in age ( t=-0.06, P=0.534), gender ( χ2<0.01, P=0.961) and number of lung metastases ( χ2=0.71, P=0.703) between the validation and test sets. A total of 792 radiomic features were extracted, 703 of which had good agreement (intraclass correlation coefficient≥0.75), while 89 features being excluded for having poor agreement (intraclass correlation coefficient<0.75). The dichotomous model (SVM) screened 28 (lung metastases from gastric cancer vs. lung metastases from breast cancer), 25 (lung metastases from gastric cancer vs. lung metastases from kidney cancer) and 34 (lung metastases from kidney cancer vs. lung metastases from breast cancer) features, respectively; the trichotomous model (RF) screened 20 features (three types of lung metastases), in which Short Run Emphasis and Inverse Variance were significantly higher in lung metastases from kidney cancer than in the other two types, correlation was higher in lung metastases from gastric cancer than in the other two types, and there was no significant difference in the sphericity of the three lung metastases. For the dichotomous model, in the validation set, the area under the curve (AUC) of the 28 features selected to distinguish gastric cancer lung metastases from breast cancer lung metastases was 0.81, the AUC of the 25 features distinguishing gastric cancer lung metastases from kidney cancer lung metastases was 0.86, and the AUC of the 34 features distinguishing kidney cancer lung metastases from breast cancer lung metastases was 0.92, and the AUCs of the test set were 0.80, 0.79 and 0.86 respectively. For the trichotomous model, the AUC for predicting lung metastases from gastric cancer, breast cancer and kidney cancer in the validation set were 0.85, 0.82 and 0.91 respectively, and both macroscopic and microscopic AUC were 0.85; In the test set, the AUC for predicting lung metastases from gastric cancer, breast cancer, and kidney cancer were 0.77, 0.86 and 0.84 respectively, and both macroscopic and microscopic AUC were 0.81. Conclusion:The SVM and RF models based on CT radiomic features are helpful in distinguishing lung metastases derived from gastric cancer, breast cancer and kidney cancer.

CT-Based Weighted Radiomic Score Predicts Tumor Response to Immunotherapy in Non-Small Cell Lung Cancer / 中国医学科学院学报

Objective To develop a CT-based weighted radiomic model that predicts tumor response to programmed death-1(PD-1)/PD-ligand 1(PD-L1)immunotherapy in patients with non-small cell lung cancer.Methods The patients with non-small cell lung cancer treated by PD-1/PD-L1 immune checkpoint inhibitors in the Peking Union Medical College Hospital from June 2015 to February 2022 were retrospectively studied and classified as responders(partial or complete response)and non-responders(stable or progressive disease).Original radiomic features were extracted from multiple intrapulmonary lesions in the contrast-enhanced CT scans of the arterial phase,and then weighted and summed by an attention-based multiple instances learning algorithm.Logistic regression was employed to build a weighted radiomic scoring model and the radiomic score was then calculated.The area under the receiver operating characteristic curve(AUC)was used to compare the weighted radiomic scoring model,PD-L1 model,clinical model,weighted radiomic scoring + PD-L1 model,and comprehensive prediction model.Results A total of 237 patients were included in the study and randomized into a training set(n=165)and a test set(n=72),with the mean ages of(64±9)and(62±8)years,respectively.The AUC of the weighted radiomic scoring model reached 0.85 and 0.80 in the training set and test set,respectively,which was higher than that of the PD-L1-1 model(Z=37.30,P<0.001 and Z=5.69,P=0.017),PD-L1-50 model(Z=38.36,P<0.001 and Z=17.99,P<0.001),and clinical model(Z=11.40,P<0.001 and Z=5.76,P=0.016).The AUC of the weighted scoring model was not different from that of the weighted radiomic scoring + PD-L1 model and the comprehensive prediction model(both P>0.05).Conclusion The weighted radiomic scores based on pre-treatment enhanced CT images can predict tumor responses to immunotherapy in patients with non-small cell lung cancer.

Detección de fibrosis en imágenes de cine de resonancia cardíaca mediante el uso de técnicas de inteligencia artificial / Fibrosis detection on MR-Cine images by using artificial intelligence techniques

RESUMEN Introducción: Las técnicas de inteligencia artificial han demostrado tener un gran potencial en el área de la cardiología, especialmente para identificar patrones imperceptibles para el ser humano. En este sentido, dichas técnicas parecen ser las adecuadas para identificar patrones en la textura del miocardio con el objetivo de identificar y cuantificar la fibrosis. Objetivos: Proponer un nuevo método de inteligencia artificial para identificar fibrosis en imágenes cine de resonancia cardíaca. Materiales y métodos: Se realizó un estudio retrospectivo observacional en 75 sujetos del Sanatorio San Carlos de Bariloche. El método propuesto analiza la textura del miocardio en las imágenes cine CMR (resonancia magnética cardíaca) mediante el uso de una red neuronal convolucional que determinar el daño local del tejido miocárdico. Resultados: Se observó una precisión del 89% para cuantificar el daño tisular local en el conjunto de datos de validación y de un 70% para el conjunto de prueba. Además, el análisis cualitativo realizado muestra una alta correlación espacial en la localización de la lesión. Conclusiones: El método propuesto permite identificar espacialmente la fibrosis únicamente utilizando la información de los estudios de cine de resonancia magnética nuclear, mostrando el potencial de la técnica propuesta para cuantificar la viabilidad miocárdica en un futuro o estudiar la etiología de las lesiones.

ABSTRACT Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the etiology of lesions.

Segmentation of lung fields from chest radiographs-a radiomic feature-based approach

Precisely segmented lung fields restrict the region-of-interest from which radiological patterns are searched, and is thus an indispensable prerequisite step in any chest radiographic CADx system. Recently, a number of deep learning-based approaches have been proposed to implement this step. However, deep learning has its own limitations and cannot be used in resource-constrained settings. Medical systems generally have limited RAM, computational power, storage, and no GPUs. They are thus not always suited for running deep learning-based models. Shallow learning-based models with appropriately selected features give comparable performance but with modest resources. The present paper thus proposes a shallow learning-based method that makes use of 40 radiomic features to segment lung fields from chest radiographs. A distance regularized level set evolution (DRLSE) method along with other post-processing steps are used to refine its output. The proposed method is trained and tested using publicly available JSRT dataset. The testing results indicate that the performance of the proposed method is comparable to the state-of-the-art deep learning-based lung field segmentation (LFS) methods and better than other LFS methods.