Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions.

BACKGROUND: Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics nomogram for distinguishing between benign and malignant pulmonary lesions based on robust features derived from diffusion images. MATERIAL AND METHODS: The study was conducted in two phases. In the first phase, we prospectively collected 30 patients with pulmonary nodule/mass who underwent twice EPI-DWI scans. The robustness of features between the two scans was evaluated using the concordance correlation coefficient (CCC) and dynamic range (DR). In the second phase, 139 patients who underwent pulmonary DWI were randomly divided into training and test sets in a 7:3 ratio. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator, and logistic regression were used for feature selection and construction of radiomics signatures. Nomograms were established incorporating clinical features, radiomics signatures, and ADC(0, 800). The diagnostic efficiency of different models was evaluated using the area under the curve (AUC) and decision curve analysis. RESULTS: Among the features extracted from DWI and ADC images, 42.7% and 37.4% were stable (both CCC and DR ≥ 0.85). The AUCs for distinguishing pulmonary lesions in the test set for clinical model, ADC, ADC radiomics signatures, and DWI radiomics signatures were 0.694, 0.802, 0.885, and 0.767, respectively. The nomogram exhibited the best differentiation performance (AUC = 0.923). The decision curve showed that the nomogram consistently outperformed ADC value and clinical model in lesion differentiation. CONCLUSION: Our study demonstrates the robustness of radiomics features derived from lung DWI. The ADC radiomics nomogram shows superior clinical net benefits compared to conventional clinical models or ADC values alone in distinguishing solitary pulmonary lesions, offering a promising tool for noninvasive, precision diagnosis in lung cancer.

The value of diffusion kurtosis imaging, diffusion weighted imaging and 18F-FDG PET for differentiating benign and malignant solitary pulmonary lesions and predicting pathological grading.

Objective: To explore the value of PET/MRI, including diffusion kurtosis imaging (DKI), diffusion weighted imaging (DWI) and positron emission tomography (PET), for distinguishing between benign and malignant solitary pulmonary lesions (SPLs) and predicting the histopathological grading of malignant SPLs. Material and methods: Chest PET, DKI and DWI scans of 73 patients with SPL were performed by PET/MRI. The apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), maximum standard uptake value (SUVmax), metabolic total volume (MTV) and total lesion glycolysis (TLG) were calculated. Student's t test or the Mann-Whitney U test was used to analyze the differences in parameters between groups. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy. Logistic regression analysis was used to evaluate independent predictors. Results: The MK and SUVmax were significantly higher, and the MD and ADC were significantly lower in the malignant group (0.59 ± 0.13, 10.25 ± 4.20, 2.27 ± 0.51[×10-3 mm2/s] and 1.35 ± 0.33 [×10-3 mm2/s]) compared to the benign group (0.47 ± 0.08, 5.49 ± 4.05, 2.85 ± 0.60 [×10-3 mm2/s] and 1.67 ± 0.33 [×10-3 mm2/s]). The MD and ADC were significantly lower, and the MTV and TLG were significantly higher in the high-grade malignant SPLs group (2.11 ± 0.51 [×10-3 mm2/s], 1.35 ± 0.33 [×10-3 mm2/s], 35.87 ± 42.24 and 119.58 ± 163.65) than in the non-high-grade malignant SPLs group (2.46 ± 0.46 [×10-3 mm2/s], 1.67 ± 0.33[×10-3 mm2/s], 20.17 ± 32.34 and 114.20 ± 178.68). In the identification of benign and malignant SPLs, the SUVmax and MK were independent predictors, the AUCs of the combination of SUVmax and MK, SUVmax, MK, MD, and ADC were 0.875, 0.787, 0.848, 0.769, and 0.822, respectively. In the identification of high-grade and non-high-grade malignant SPLs, the AUCs of MD, ADC, MTV, and TLG were 0.729, 0.680, 0.693, and 0.711, respectively. Conclusion: DWI, DKI, and PET in PET/MRI are all effective methods to distinguish benign from malignant SPLs, and are also helpful in evaluating the pathological grading of malignant SPLs.

Differential value of diffusion kurtosis imaging and intravoxel incoherent motion in benign and malignant solitary pulmonary lesions.

Objective: To investigate the diagnostic value of diffusion kurtosis imaging (DKI) and intravoxel incoherent motion (IVIM) whole-lesion histogram parameters in differentiating benign and malignant solitary pulmonary lesions (SPLs). Materials and Methods: Patients with SPLs detected by chest CT examination and with further routine MRI, DKI and IVIM-DWI functional sequence scanning data were recruited. According to the pathological results, SPLs were divided into a benign group and a malignant group. Independent samples t tests (normal distribution) or Mann‒Whitney U tests (nonnormal distribution) were used to compare the differences in DKI (Dk, K), IVIM (D, D*, f) and ADC whole-lesion histogram parameters between the benign and malignant SPL groups. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of the histogram parameters and determine the optimal threshold. The area under the curve (AUC) of each histogram parameter was compared by the DeLong method. Spearman rank correlation was used to analyze the correlation between histogram parameters and malignant SPLs. Results: Most of the histogram parameters for diffusion-related values (Dk, D, ADC) of malignant SPLs were significantly lower than those of benign SPLs, while most of the histogram parameters for the K value of malignant SPLs were significantly higher than those of benign SPLs. DKI (Dk, K), IVIM (D) and ADC were effective in differentiating benign and malignant SPLs and combined with multiple parameters of the whole-lesion histogram for the D value, had the highest diagnostic efficiency, with an AUC of 0.967, a sensitivity of 90.00% and a specificity of 94.03%. Most of the histogram parameters for the Dk, D and ADC values were negatively correlated with malignant SPLs, while most of the histogram parameters for the K value were positively correlated with malignant SPLs. Conclusions: DKI (Dk, K) and IVIM (D) whole-lesion histogram parameters can noninvasively distinguish benign and malignant SPLs, and the diagnostic performance is better than that of DWI. Moreover, they can provide additional information on SPL microstructure, which has important significance for guiding clinical individualized precision diagnosis and treatment and has potential clinical application value.

Non-Small Cell Carcinoma-Not Otherwise Specified on Cytology Specimens in Patients with Solitary Pulmonary Lesion: Primary Lung Cancer or Metastatic Cancer?

CONTEXT: Subtyping of solitary pulmonary lesion (SPL) in small amount of cytology specimen using a limited panel of immunohistochemistry (IHC) markers is very important to the correct choice of treatment. This study was performed to categorize non-small cell carcinoma-not otherwise specified (NSCC-NOS) on cytology in patients with SPL, especially with regard to the incidence of metastatic cancer. MATERIALS AND METHODS: We reviewed 91 cases, in which a precise morphology-based, lineage-specific IHC-aided subtyping was not possible, that qualified as NSCC-NOS on cytology. A stepwise clinical approach and IHC of organ-specific markers was performed on each cell block (CB) to exclude metastasis from extrapulmonary malignancies. RESULTS: Of the 91 evaluated cases, 65 (71.4%) were diagnosed as non-small cell lung carcinoma (NSCLC)-NOS, 24 (26.4%) were metastatic cancer, and the remaining 2 (2.2%) had undetermined diagnoses. The most frequent primary tumor site was the colorectum (41.7%), followed by breast (20.8%), kidney (8.3%), and then stomach, duodenum, liver, pancreas, gallbladder, prostate, and skin (4.2% each, 1 of 24). Moreover, we found that 7 of the 24 patients with metastatic cancer had a history of extrapulmonary malignancy that was unknown at the time of cytology-based diagnosis. CONCLUSIONS: These results underscored the need for accurate and stepwise clinical correlation to rule out the possibility of pulmonary metastasis from other sites and appropriate but judicious IHC (i.e., CDX2) on CB for SPL to increase refinement of the cytology diagnosis of NSCC-NOS.

Use of radiomics based on 18F-FDG PET/CT and machine learning methods to aid clinical decision-making in the classification of solitary pulmonary lesions: an innovative approach.

PURPOSE: This study was designed and performed to assess the ability of 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomics features combined with machine learning methods to differentiate between primary and metastatic lung lesions and to classify histological subtypes. Moreover, we identified the optimal machine learning method. METHODS: A total of 769 patients pathologically diagnosed with primary or metastatic lung cancers were enrolled. We used the LIFEx package to extract radiological features from semiautomatically segmented PET and CT images within the same volume of interest. Patients were randomly distributed in training and validation sets. Through the evaluation of five feature selection methods and nine classification methods, discriminant models were established. The robustness of the procedure was controlled by tenfold cross-validation. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: Based on the radiomics features extracted from PET and CT images, forty-five discriminative models were established. Combined with appropriate feature selection methods, most classifiers showed excellent discriminative ability with AUCs greater than 0.75. In the differentiation between primary and metastatic lung lesions, the feature selection method gradient boosting decision tree (GBDT) combined with the classifier GBDT achieved the highest classification AUC of 0.983 in the PET dataset. In contrast, the feature selection method eXtreme gradient boosting combined with the classifier random forest (RF) achieved the highest AUC of 0.828 in the CT dataset. In the discrimination between squamous cell carcinoma and adenocarcinoma, the combination of GBDT feature selection method with GBDT classification had the highest AUC of 0.897 in the PET dataset. In contrast, the combination of the GBDT feature selection method with the RF classification had the highest AUC of 0.839 in the CT dataset. Most of the decision tree (DT)-based models were overfitted, suggesting that the classification method was not appropriate for practical application. CONCLUSION: 18F-FDG PET/CT radiomics features combined with machine learning methods can distinguish between primary and metastatic lung lesions and identify histological subtypes in lung cancer. GBDT and RF were considered optimal classification methods for the PET and CT datasets, respectively, and GBDT was considered the optimal feature selection method in our analysis.

18 Fluorodeoxyglucose-positron emission tomography/computed tomography features of suspected solitary pulmonary lesions in breast cancer patients following previous curative treatment.

BACKGROUND: Differentiating pulmonary metastasis from primary lung cancer can be challenging in patients with breast malignancy. This study aimed to characterize the imaging features of 18 fluorodeoxyglucose-positron emission tomography/computed tomography (18 F-FDG-PET/CT) for distinguishing between these diseases. METHODS: We enrolled 52 patients who received curative treatment for breast cancer but later presented with suspected solitary pulmonary lesions (SPLs) and subsequently underwent 18 F-FDG-PET/CT to investigate. RESULTS: Subsolid lesions, ill-defined borders, lung lesions with negative maximum standardized uptake value, and lesions without 18 F-FDG-PET/CT-diagnosed hilar and/or mediastinal lymph nodes and pleural metastases were more likely to be associated with primary lung cancer. CONCLUSIONS: CT border, FDG uptake, hilar and/or mediastinal lymph node metastasis, and pleural metastasis are potential markers for diagnosis.

The application of four-dimensional CT technique in determining the planning target volume of the solitary pulmonary lesion / 中华放射肿瘤学杂志

ObjectiveTo measure the displacement of solitary pulmonary lesion (SPL) using fourdimensional CT (4DCT), and to compare the planning target volume using 4D maximum intensity projection (MIPMIP) ( PTV4DMIP ) with the empirical PTV3D.Methods Data were acquired from 24 consecutive patients with SPL. For each patient, respiration-synchronized 4DCT images and standard axial CT scans were obtained during free breathing.In lung window setting,the 4D technique was used to measure the displacement of SPL in three dimensions. We compared an PTV created using the MIP (PTV4DMIP) to the PTV created from the gross tumor volume (GTV) enlarged isotropically for each spatial direction by 1.0 cm and 1. 5 cm in the PTV3D1.0cm and PTV3D1.5cm. Results The SPL located in the lower lobe showed significant difference with the upper and middle lobe in y axis (0. 44 cm,0. 92 cm, t =2. 87, P =0. 000),but there was no difference in both x and z axis (0. 27 cm,0. 39 cm,t =1.44 ,P =0. 116 and 0. 29 cm,0. 40 cm,t =1.51, P =0. 227). SPL showed significantly greater displacement in y axis than in both x and z axis [0.60 cm and0. 31 cm (t =4.23,P=0.000) ,0.60 cm and 0.32 cm (t =4.65,P=0. 000)], but there was no significant difference between x and z axis (0. 31 cm,0. 32 cm,t =0. 33 ,P =0. 741 ). There was no statistically difference between the peripheral lung cancer and the pulmonary metastasis tumor in three directions ( x axis : 0. 37 cm,0. 32 cm, t =0. 52, P =0. 223 ; y axis : 0. 54 cm, 0. 95 cm, t =- 1.38, P =0.061;z axis:0.42 cm,0.37 cm, t=0.29, P=0.859).Both PTV3D1.0cm and PTV3D1.5cm showed significantly greater volume than PTV4DMIP(46. 73 cm3 ,86. 52 cm3 and 30. 02 cm3 ,t =- 11.35, - 12. 09,P =0. 000,0. 000). ConclusionsThe displacement of SPL in y axis is much greater than x and z axis. The empirical PTV3D is much bigger than PTV4DMIP, which suggests that 4DMIP provide adequate coverage of the moving target and minimize dose to normal tissues.