Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Long-term prognosis analysis of surgical therapy for bilateral synchronous multiple primary lung cancer: a follow-up of 293 cases.

Background: Bilateral synchronous multiple primary lung cancer (BSMPLC) presents significant clinical challenges due to its unique characteristics and prognosis. Understanding the risk factors that influence overall survival (OS) and recurrence-free survival (RFS) is crucial for optimizing therapeutic strategies for BSMPLC patients. Methods: We retrospectively analyzed clinical characteristics and treatment outcomes of 293 patients with BSMPLC who underwent surgical treatment between January 2010 and July 2017. Results: The 10-year OS and RFS rates were 96.1% and 92.8%, respectively. Preoperative forced expiratory volume in 1 second (FEV1) ≥70% [hazard ratio (HR), 0.214; 95% confidence interval (CI): 0.053 to 0.857], identical pathology types (HR, 9.726; 95% CI: 1.886 to 50.151), largest pT1 (HR, 7.123; 95% CI: 2.663 to 19.055), and absence of lymphovascular invasion (LVI; HR, 7.021; 95% CI: 1.448 to 34.032) emerged as independent predictors of improved OS. Moreover, the sum of tumor sizes less than or equal to 3 cm (HR, 6.229; 95% CI: 1.411 to 27.502) and absence of pleural invasion (HR, 3.442; 95% CI: 1.352 to 8.759) were identified as independent predictors of enhanced RFS. The presence or absence of residual nodules after bilateral surgery did not influence patients' OS (P=0.987) and RFS (P=0.054). Conclusions: Patients with BSMPLC who underwent surgery generally had a favorable prognosis. Whether or not to remove all nodules bilaterally does not affect the patient's long-term prognosis, suggesting the need for an individualized surgical approach.

Combined Pulmonary Fibrosis and Emphysema: A Narrative Review.

Combined pulmonary fibrosis and emphysema (CPFE) syndrome refers to co-occurrence of two disease processes in the lung that can be difficult to diagnose but is associated with high morbidity and mortality burden. Diagnosis of CPFE is challenging because the two diseases can counterbalance respective impairments resulting in deceivingly normal-appearing chest radiography and spirometry in a dyspneic patient. Although an international committee published the terminology and definitions of CPFE in 2022, consensus on exact diagnostic criteria and optimal management strategy is yet to be determined. Herein, we provide a narrative review summarizing the literature on CPFE from 1990 to 2022, including historical background, epidemiology, pathogenesis, clinical features, imaging and pulmonary function findings, diagnosis, prognosis, complications, and treatment. Although CPFE was initially conceived as a variant presentation of idiopathic pulmonary fibrosis, it has been recognized to occur in patients with a wide variety of interstitial lung diseases, including connective tissue disease-associated interstitial lung diseases, and hypersensitivity pneumonitis. The affected patients have a heightened risk for pulmonary hypertension and lung cancer. Clinicians need to recognize the characteristic presenting features of CPFE along with prognostic implications of this entity.

The dynamic contrast enhanced-magnetic resonance imaging and diffusion-weighted imaging features of alveolar soft part sarcoma.

Background: Alveolar soft part sarcoma (ASPS) is a rare type of soft tissue sarcoma that predominantly affects adolescents and young adults. Early diagnosis of ASPS is crucial for optimal therapeutic planning and improving prognosis, but its diagnostic features are not well delineated. This study aimed to retrospectively analyze the imaging features of ASPS with an emphasis on the dynamic contrast-enhanced-magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) findings to identify imaging findings that might suggest the diagnosis to radiologists. Methods: The imaging features of 34 patients with pathologically proven limb ASPS were retrospectively analyzed. A total of 23 underwent DCE-MRI, and 12 underwent DWI. Results: Among the 34 cases of ASPS, 31 tumors were in the lower extremities, and 3 were in the upper extremities. The maximum tumor diameters ranged from 3.0 to 19.4 cm (mean, 8.7±3.96 cm). A total of 28 cases had well-defined borders. The masses demonstrated heterogeneous high signal intensity on T2-weighted imaging (T2WI) and the fat-suppressed (FS) T2WI sequence and slight hyperintensity on T1-weighted imaging (T1WI). A total of 25 lesions had thin hypointense bands on T1WI and T2WI. Intra- and peri-tumoral tubular areas of flow void were exhibited on both T1WI and T2WI in all cases. A total of 12 cases showed a high signal on DWI, and the mean apparent diffusion coefficient (ADC) value was (0.86±0.07)×10-3 mm2/s [range, (0.6-1.4)×10-3 mm2/s]. Persistent remarkable enhancement of the lesion was displayed on contrast-enhanced scans. The time-intensity curves (TICs) of 23 masses showed early arterial enhancement and slow washout of contrast. Conclusions: ASPS most commonly presents in the lower extremities of adolescents or young adults. Hyperintense T1WI, T2WI, and DWI signals, low ADC, flow voids, early arterial enhancement are frequent MRI features.

DIPNECH: pragmatic approach, uncertainties, notable associations, and a proposal for an improved definition.

Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) is a rare, but increasingly recognized entity that primarily affects middle-aged and elderly women. It is characterized by abnormal proliferation of pulmonary neuroendocrine cells (PNECs) and is considered a preinvasive lesion for carcinoid tumorlets/tumors. Sometimes, DIPNECH is accompanied by constrictive bronchiolitis which usually manifests as chronic cough and/or dyspnea, along with airflow limitation on spirometry. The telltale imaging sign of DIPNECH is the presence of multiple noncalcified pulmonary nodules and mosaic attenuation on CT. However, these clinico-radiologic features of DIPNECH are characteristic but nonspecific; thus, histopathologic confirmation is usually necessary. DIPNECH has an indolent course and only rarely leads to respiratory failure or death; progression to overt neuroendocrine tumor (carcinoid) of the lung occurs in a minority of patients. Of available therapies, somatostatin analogs and mechanistic target of rapamycin inhibitors are the most promising. In this review, we provide an update regarding the diagnosis and management of DIPNECH and describe critical gaps in our understanding of this entity, including the central terms 'diffuse' and 'idiopathic.' We also summarize the inconsistencies in definitions employed by recent studies and discuss the pitfalls of the DIPNECH definitions proposed by the World Health Organization in 2021. In this context, we propose an objective and reproducible radio-pathologic case definition intended for implementation in the research realm and seeks to enhance homogeneity across cohorts. Furthermore, we discuss aspects of PNECs biology which suggest that PNEC hyperplasia may contribute to the pathogenesis of phenotypes of lung disease aside from constrictive bronchiolitis and carcinoid tumorlets/tumors. Finally, we steer attention to some of the most pressing and impactful research questions awaiting to be unraveled.

Recent Advancements in Computed Tomography Assessment of Fibrotic Interstitial Lung Diseases.

Interstitial lung disease (ILD) is a heterogeneous group of disorders with complex and varied imaging manifestations and prognosis. High-resolution computed tomography (HRCT) is the current standard-of-care imaging tool for ILD assessment. However, visual evaluation of HRCT is limited by interobserver variation and poor sensitivity for subtle changes. Such challenges have led to tremendous recent research interest in objective and reproducible methods to examine ILDs. Computer-aided CT analysis to include texture analysis and machine learning methods have recently been shown to be viable supplements to traditional visual assessment through improved characterization and quantification of ILDs. These quantitative tools have not only been shown to correlate well with pulmonary function tests and patient outcomes but are also useful in disease diagnosis, surveillance and management. In this review, we provide an overview of recent computer-aided tools in diagnosis, prognosis, and longitudinal evaluation of fibrotic ILDs, while outlining some of the pitfalls and challenges that have precluded further advancement of these tools as well as potential solutions and further endeavors.

Anatomic Approach to Common and Uncommon Manifestations of Thoracic Leukemias with Radiologic-Pathologic Correlation.

Leukemias are hematopoietic malignancies characterized by the production of abnormal leukocytes in the bone marrow. Clinical manifestations arise from either bone marrow suppression or leukemic organ infiltration. Lymphadenopathy is the most common direct manifestation of intrathoracic leukemia. However, leukemic cells may also infiltrate the lungs, pleura, heart, bones, and soft tissues. Pulmonary complications in patients with leukemia typically include pneumonia, hemorrhage, pulmonary edema, and sequelae of leukemia treatment. However, pulmonary abnormalities can also be related directly to leukemia, including leukemic pulmonary infiltration. The direct, non-treatment-related effects of leukemia on intrathoracic structures will be the focus of this imaging essay. Given the typical anatomic approach for image interpretation, an organ-based depiction of common and less common intrathoracic findings directly caused by leukemic involvement is presented, emphasizing imaging findings with pathologic correlations. Keywords: Leukemia, Pulmonary, Thorax, Soft Tissues/Skin, Hematologic, Bone Marrow © RSNA, 2023.

Magnetic resonance radiomic feature performance in pulmonary nodule classification and impact of segmentation variability on radiomics.

OBJECTIVE: Investigate the performance of multiparametric MRI radiomic features, alone or combined with current standard-of-care methods, for pulmonary nodule classification. Assess the impact of segmentation variability on feature reproducibility and reliability. METHODS: Radiomic features were extracted from 74 pulmonary nodules of 68 patients who underwent nodule resection or biopsy after MRI exam. The MRI features were compared with histopathology and conventional quantitative imaging values (maximum standardized uptake value [SUVmax] and mean Hounsfield unit [HU]) to determine whether MRI radiomic features can differentiate types of nodules and associate with SUVmax and HU using Wilcoxon rank sum test and linear regression. Diagnostic performance of features and four machine learning (ML) models were evaluated with area under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs). Concordance correlation coefficient (CCC) assessed the segmentation variation impact on feature reproducibility and reliability. RESULTS: Elevn diffusion-weighted features distinguished malignant from benign nodules (adjusted p < 0.05, AUC: 0.73-0.81). No features differentiated cancer types. Sixty-seven multiparametric features associated with mean CT HU and 14 correlated with SUVmax. All significant MRI features outperformed traditional imaging parameters (SUVmax, mean HU, apparent diffusion coefficient [ADC], T1, T2, dynamic contrast-enhanced imaging values) in distinguishing malignant from benign nodules with some achieving statistical significance (p < 0.05). Adding ADC and smoking history improved feature performance. Machine learning models demonstrated strong performance in nodule classification, with extreme gradient boosting (XGBoost) having the highest discrimination (AUC = 0.83, CI=[0.727, 0.932]). We found good to excellent inter- and intrareader feature reproducibility and reliability (CCC≥0.80). CONCLUSION: Eleven MRI radiomic features differentiated malignant from benign lung nodules, outperforming traditional quantitative methods. MRI radiomic ML models demonstrated good nodule classification performances with XGBoost superior to three others. There was good to excellent inter- and intrareader feature reproducibility and reliability. ADVANCES IN KNOWLEDGE: Our study identified MRI radiomic features that successfully differentiated malignant from benign lung nodules and demonstrated high performance of our MR radiomic feature-based ML models for nodule classification. These new findings could help further establish thoracic MRI as a non-invasive and radiation-free alternative to standard practice for pulmonary nodule assessment.

Quantitative CT and machine learning classification of fibrotic interstitial lung diseases.

OBJECTIVES: To evaluate quantitative computed tomography (QCT) features and QCT feature-based machine learning (ML) models in classifying interstitial lung diseases (ILDs). To compare QCT-ML and deep learning (DL) models' performance. METHODS: We retrospectively identified 1085 patients with pathologically proven usual interstitial pneumonitis (UIP), nonspecific interstitial pneumonitis (NSIP), and chronic hypersensitivity pneumonitis (CHP) who underwent peri-biopsy chest CT. Kruskal-Wallis test evaluated QCT feature associations with each ILD. QCT features, patient demographics, and pulmonary function test (PFT) results trained eXtreme Gradient Boosting (training/validation set n = 911) yielding 3 models: M1 = QCT features only; M2 = M1 plus age and sex; M3 = M2 plus PFT results. A DL model was also developed. ML and DL model areas under the receiver operating characteristic curve (AUC) and 95% confidence intervals (CIs) were compared for multiclass (UIP vs. NSIP vs. CHP) and binary (UIP vs. non-UIP) classification performances. RESULTS: The majority (69/78 [88%]) of QCT features successfully differentiated the 3 ILDs (adjusted p ≤ 0.05). All QCT-ML models achieved higher AUC than the DL model (multiclass AUC micro-averages 0.910, 0.910, 0.925, and 0.798 and macro-averages 0.895, 0.893, 0.925, and 0.779 for M1, M2, M3, and DL respectively; binary AUC 0.880, 0.899, 0.898, and 0.869 for M1, M2, M3, and DL respectively). M3 demonstrated statistically significant better performance compared to M2 (∆AUC: 0.015, CI: [0.002, 0.029]) for multiclass prediction. CONCLUSIONS: QCT features successfully differentiated pathologically proven UIP, NSIP, and CHP. While QCT-based ML models outperformed a DL model for classifying ILDs, further investigations are warranted to determine if QCT-ML, DL, or a combination will be superior in ILD classification. KEY POINTS: • Quantitative CT features successfully differentiated pathologically proven UIP, NSIP, and CHP. • Our quantitative CT-based machine learning models demonstrated high performance in classifying UIP, NSIP, and CHP histopathology, outperforming a deep learning model. • While our quantitative CT-based machine learning models performed better than a DL model, additional investigations are needed to determine whether either or a combination of both approaches delivers superior diagnostic performance.

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION: Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS: This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS: For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION: Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.

Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.

Background: Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful. This study developed a CT-based visual forecasting system that can visualize and quantify a nodule in three dimensions (3D) in any future time point using follow-up CT scans. Methods: We retrospectively included 246 patients with 313 lung nodules with at least 1 follow-up CT scan. For the manually segmented nodules, we calculated geometric properties including CT value, diameter, volume, and mass, as well as growth properties including volume doubling time (VDT), and consolidation-to-tumor ratio (CTR) at follow-ups. These nodules were divided into growth and non-growth groups by thresholding their VDTs. We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. The model was evaluated on the geometric and radiological properties using either logistic regression or receiver operating characteristic (ROC) curve. Results: The lung nodules consisted of 115 ground glass nodules (GGN) and 198 solid nodules and were followed up for an average of 354 days with 2 to 11 scans. The 2 groups differed significantly in most properties. The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. The prediction-derived VDTs had an area under the curve (AUC) of 0.857 and 0.843 in differentiating growth and non-growth nodules for GGN and solid nodules, respectively. The prediction-derived CTRs had an AUC of 0.892 in classifying high- and low-risk nodules. Conclusions: This proof-of-concept study demonstrated that the deep learning-based model can accurately forecast the imagery of a nodule in a given future for both GGNs and solid nodules and is worthy of further investigation. With a larger dataset and more validation, such a system has the potential to become a prognostication tool for assessing lung nodules.

Improving the Robustness of Diagnostic Accuracy Results By Asking Study Readers to Further Distinguish Subjects Who Appear to be Without the Condition Of Interest.

RATIONALE AND OBJECTIVES: In diagnostic accuracy studies, cases in which a reader does not see the condition of interest are often given the same score for ROC analysis (e.g. confidence score of 0%). However, many of these cases can be further distinguished and doing so may result in more robust ROC results. MATERIALS AND METHODS: We examined two recent, real-world studies which used different procedures to encourage readers to further distinguish subjects who appear to be without the condition of interest. For each study, we calculated the results under two conditions. In the "zeros distinguished" (ZD) condition, we incorporated the confidence scores collected to further distinguish the normal-looking subjects. In the "zeros not distinguished" (ZND) condition, we disregarded these scores and simply gave the unit of analysis a score of zero whenever the reader did not identify the condition of interest in that unit. We compared the two conditions on (1) coverage of the ROC space and (2) discrepancy between parametric and nonparametric results. RESULTS: Compared to the ZND condition, coverage of the ROC space was improved in the ZD condition for all ROC curves in both studies. In the first study, there was a significant reduction in the discrepancy between parametric and nonparametric results (median discrepancy in ZND vs ZD condition: 0.033 vs 0.011, p = 0.012). A similar reduction was not seen in the second study, though the discrepancies were very low in both conditions (0.003 vs 0.006, p = 0.313). CONCLUSION: Prompting readers to further distinguish cases in which they do not see the condition of interest may result in more robust ROC results, though further exploration of this topic is warranted.

Is the combination of bilateral pulmonary nodules and mosaic attenuation on chest CT specific for DIPNECH?

BACKGROUND: Diffuse idiopathic pulmonary neuroendocrine cell hyperplasia (DIPNECH) is characterized by multifocal proliferation of pulmonary neuroendocrine cells. On chest CT, DIPNECH exhibits bilateral pulmonary nodules and mosaic attenuation in most patients. We sought to: (1) assess the specificity of this pattern (i.e., bilateral pulmonary nodules together with mosaic attenuation) for DIPNECH; (2) describe its differential diagnosis; and (3) identify the clinico-radiologic features that may help prioritize DIPNECH over other diagnostic considerations. METHODS: We searched the Mayo Clinic records from 2015 to 2019 for patients with bilateral pulmonary nodules and mosaic attenuation on CT who had a diagnostic lung biopsy. A thoracic radiologist reviewed all CT scans. Chi-square test was used for categorical variables, and odds ratios were utilized to measure the association between certain variables and DIPNECH. RESULTS: Fifty-one patients met our inclusion criteria; 40 (78%) were females and 34 (67%) were never-smokers. Median age was 65 (interquartile range 55-73) years. Lung biopsy was surgical in 21 patients (41%), transbronchial in 17 (33%), and transthoracic in 12 (24%); explanted lungs were examined in 1 (2%). Metastatic/multifocal cancer was the most common diagnosis, and was found in 17 (33%) cases. Bronchiolitis was diagnosed in 12 patients (24%), interstitial lung disease in 10 (20%), and DIPNECH in 5 (10%). Previous diagnosis of an obstructive lung disease (odds ratio 15.8; P = 0.002), and peribronchial nodular distribution on CT (odds ratio 14.4; P = 0.006) were significantly correlated with DIPNECH. Although statistical significance was not reached, DIPNECH nodules were more likely to display solid attenuations (80% vs. 67%, P = 0.45), and were more numerous; > 10 nodules were seen in 80% of DIPNECH cases vs. 52% in others (P = 0.23). Because DIPNECH primarily affects women, we analyzed the women-only cohort and found similar results. CONCLUSIONS: Various disorders can manifest the CT pattern of bilateral pulmonary nodules together with mosaic attenuation, and this combination is nonspecific for DIPNECH, which was found in only 10% of our cohort. Previous diagnosis of an obstructive lung disease, and peribronchial distribution of the nodules on CT increased the likelihood of DIPNECH vs. other diagnoses.

A Pilot Study to Estimate the Impact of High Matrix Image Reconstruction on Chest Computed Tomography.

OBJECTIVES: The objectives of the study were to estimate the impact of high matrix image reconstruction on chest computed tomography (CT) compared to standard image reconstruction. MATERIAL AND METHODS: This retrospective study included patients with interstitial or parenchymal lung disease, airway disease, and pulmonary nodules who underwent chest CT. Chest CT images were reconstructed using high matrix (1024 × 1024) or standard matrix (512 × 512), with all other parameters matched. Two radiologists, blinded to reconstruction technique, independently examined each lung, viewing image sets side by side and rating the conspicuity of imaging findings using a 5-point relative conspicuity scale. The presence of pulmonary nodules and confidence in classification of internal attenuation was also graded. Overall image quality and subjective noise/artifacts were assessed. RESULTS: Thirty-four patients with 68 lungs were evaluated. Relative conspicuity scores were significantly higher using high matrix image reconstruction for all imaging findings indicative of idiopathic lung fibrosis (peripheral airway visualization, interlobular septal thickening, intralobular reticular opacity, and end-stage fibrotic change; P ≤ 0.001) along with emphysema, mosaic attenuation, and fourth order bronchi for both readers (P ≤ 0.001). High matrix reconstruction did not improve confidence in the presence or classification of internal nodule attenuation for either reader. Overall image quality was increased but not subjective noise/artifacts with high matrix image reconstruction for both readers (P < 0.001). CONCLUSION: High matrix image reconstruction significantly improves the conspicuity of imaging findings reflecting interstitial lung disease and may be useful for diagnosis or treatment response assessment.

Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient.

BACKGROUND: The intravoxel incoherent motion (IVIM) method of magnetic resonance imaging (MRI) analysis can provide information regarding many physiological and pathological processes. This study aimed to investigate whether IVIM-derived parameters and the apparent diffusion coefficient (ADC) can act as imaging biomarkers for predicting non-small cell lung cancer (NSCLC) response to anti-tumor therapy and compare their performances. METHODS: This prospective study included 45 patients with NSCLC treated with chemotherapy (29 men and 16 women, mean age 57.9±9.7 years). Diffusion-weighted imaging was performed with 13 b-values before and 2-4 weeks after treatment. The IVIM parameter pseudo-diffusion coefficient (D*), perfusion fraction (f), diffusion coefficient (D), and ADC from a mono-exponential model were obtained. Responses 2 months after chemotherapy were assessed. The diagnostic performance was evaluated, and optimal cut-off values were determined by receiver operating characteristic (ROC) curve analysis, and the differences of progression-free survival (PFS) in groups of responders and non-responders were tested by Cox regression and Kaplan-Meier survival analyses. RESULTS: Of 45 patients, 30 (66.7%) were categorized as responders, and 15 as non-responders. Differences in the diffusion coefficient D and ADC between responders and non-responders were statistically significant (all P<0.05). Conversely, differences in f and D* between responders and non-responders were both not statistically significance (all P>0.05). The ROC analyses showed the change in D value (ΔD) was the best predictor of early response to anti-tumor therapy [area under the ROC curve (AUC), 0.764]. The Cox-regression model showed that all ADC and D parameters were independent predictors of PFS, with a range of reduction in risk from 56.2% to 82.7%, and ΔD criteria responders had the highest reduction (82.7%). CONCLUSIONS: ADC and D derived from IVIM are potentially useful for the prediction of NSCLC treatment response to anti-tumor therapy. Although ΔD is best at predicting response to treatment, ΔADC measurement may simplify manual efforts and reduce the workload.