Value of IVIM in Differential Diagnoses between Benign and Malignant Solitary Lung Nodules and Masses: A Meta-analysis

Translational Advances in Oncogene and Tumor-Suppressor Gene Research

Cancer, characterized by the uncontrolled proliferation of cells, is one of the leading causes of death globally, with approximately one in five people developing the disease in their lifetime. While many driver genes were identified decades ago, and most cancers can be classified based on morphology and progression, there is still a significant gap in knowledge about genetic aberrations and nuclear DNA damage. The study of two critical groups of genes-tumor suppressors, which inhibit proliferation and promote apoptosis, and oncogenes, which regulate proliferation and survival-can help to understand the genomic causes behind tumorigenesis, leading to more personalized approaches to diagnosis and treatment. Aberration of tumor suppressors, which undergo two-hit and loss-of-function mutations, and oncogenes, activated forms of proto-oncogenes that experience one-hit and gain-of-function mutations, are responsible for the dysregulation of key signaling pathways that regulate cell division, such as p53, Rb, Ras/Raf/ERK/MAPK, PI3K/AKT, and Wnt/β-catenin. Modern breakthroughs in genomics research, like next-generation sequencing, have provided efficient strategies for mapping unique genomic changes that contribute to tumor heterogeneity. Novel therapeutic approaches have enabled personalized medicine, helping address genetic variability in tumor suppressors and oncogenes. This comprehensive review examines the molecular mechanisms behind tumor-suppressor genes and oncogenes, the key signaling pathways they regulate, epigenetic modifications, tumor heterogeneity, and the drug resistance mechanisms that drive carcinogenesis. Moreover, the review explores the clinical application of sequencing techniques, multiomics, diagnostic procedures, pharmacogenomics, and personalized treatment and prevention options, discussing future directions for emerging technologies.

Intravoxel incoherent motion MRI to assess feto-placental diffusion and perfusion properties in small fetuses

OBJECTIVES

To investigate the use of intravoxel incoherent motion (IVIM) to study microperfusion and microstructural characteristics of lungs, brain, and placenta in normal and small fetuses.

METHODS

We retrospectively enrolled 30 small fetuses and 82 normal pregnancies who underwent a 1.5-T MRI examination using an IVIM-DWI. Small fetuses were distinguished in small for gestational age (SGA) and "true" fetal growth restriction (FGR). ROIs were placed on the brain parenchyma, lungs, and fetal/maternal placental sides. Differences in perfusion fraction f, diffusion coefficient D, and pseudo-diffusion coefficient D* and their correlation with gestational age (GA) and birth weight (BW) were investigated.

RESULTS

LUNG: f showed significantly lower values (p = 2·10-7) in small fetuses (SGA + FGR); f discriminates SGA and FGR from normal (p = 0.001; p = 1·10-6). f increases with GA (p < 0.0001) in the control group; a positive correlation was also obtained in small fetuses, although less significant.

PLACENTA

FGR showed lower f values than normal ones, in both the fetal (p = 1.4·10-7) and maternal side (p = 0.001); f discriminates between SGA and FGR (p = 0.03). In small fetuses (SGA + FGR), f correlates positively with BW.

BRAIN

D values in supratentorial white matter (WM) were significantly higher compared to other regions, in both normal and small fetuses. Small fetuses showed higher D values in occipital WM and pons (p = 0.041; p = 0.027) than in normal. D correlates negatively with GA in the healthy group. No correlation between D and GA was found in SGA + FGR group.

CONCLUSIONS

In our study, IVIM-MRI allowed us to detect microstructural and microperfusion changes in the placenta, brain, and lung of small fetuses, noninvasively.

Monoexponential, biexponential, stretched exponential and diffusion kurtosis models of diffusion-weighted imaging: a quantitative differentiation of solitary pulmonary lesion

BACKGROUND

Diffusion-weighted imaging (DWI) can be used for quantitative tumor assessment. DWI with different models may show different aspects of tissue characteristics.

OBJECTIVE

To investigate the diagnostic performance of parameters derived from monoexponential, biexponential, stretched exponential magnetic resonance diffusion weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in differentiating benign from malignant solitary pulmonary lesions (SPLs).

METHOD

Forty-four SPL subjects were selected according to the inclusion criteria. All patients underwent conventional and multi‑b DWI sequences. Monoexponential DWI and DKI model were fitted using least square method. Levenberg-Marquardt nonlinear fitting biexponential and stretched exponential DWI. Region of interests (ROIs) were described manually. Parameters between benign and malignant SPLs were compared using independent sample t test or the Mann-Whitney U test. Receiver operating characteristic (ROC) curves analysis was used to investigate the diagnostic performance of different DWI parameters. Correlation between all parameters were evaluated by using Spearman correlation.

RESULT

ADC, ADCslow, α, DDC and Dapp values were significantly lower in malignant SPL than in benign SPL (P < 0.001). Kapp was significantly higher in malignant SPL than in benign SPL (P < 0.001). Among all subjects, ADCslow was significantly lower than ADC (P < 0.05), while DDC and Dapp were significantly higher than ADC (P < 0.05). When observing the ROC curves for distinguishing benign and malignant SPL, the AUC values of ADC, ADCslow, DDC, Dapp, and Kapp were 0.904, 0.815, 0.942, 0.93, and 0.815, respectively. The DDC value has the highest area under ROC curve value. DeLong analysis showed no statistically significant difference in the area under ADC, DDC, and Dapp curves. There were strong correlations among ADC, ADCslow, ADCfast, f, α, DDC, Dapp, and Kapp (P < 0.001).

CONCLUSION

Multi‑b DWI is a promising method for differentiating benign from malignant SPLs with high diagnostic accuracy. In addition, the DDC derived from stretched‑exponential model is the most promising DWI parameter for the differentiation of benign and malignant SPLs.

TRAIL REGISTRATION

This study was a clinical trail study, with study protocol published at ClinicalTrails. Retrospectively registered number ChiCTR2300074258, date of registration 02/08/2023.

The diagnostic efficacy of seven autoantibodies in early detection of ground-glass nodular lung adenocarcinoma

Background

Persistent ground-glass nodules (GGNs) carry a potential risk of malignancy, however, early diagnosis remained challenging. This study aimed to investigate the cut-off values of seven autoantibodies in patients with ground-glass nodules smaller than 3cm, and to construct machine learning models to assess the diagnostic value of these autoantibodies.

Methods

In this multi-center retrospective study, we collected peripheral blood specimens from a total of 698 patients. A total of 466 patients with ground-glass nodular lung adenocarcinoma no more than 3cm were identified as a case group based on pathological reports and imaging data, and control group (n=232) of patients consisted of 90 patients with benign nodules and 142 patients with health check-ups. Seven antibodies were quantified in the serum of all participants using enzyme-linked immunosorbent assay (ELISA), and the working characteristic curves of the subjects were plotted to determine the cut-off values of the seven autoantibodies related ground-glass nodular lung adenocarcinoma early. Subsequently, the patients were randomly divided into a training and test set at a 7:3 ratio. Eight machine-learning models were constructed to compare the diagnostic performances of multiple models. The model performances were evaluated using sensitivity, specificity, and the area under the curve (AUC).

Results

The serum levels of the seven autoantibodies in case group were significantly higher than those in the control group (P < 0.05). The combination of the seven autoantibodies demonstrated a significantly enhanced diagnostic efficacy in identifying ground-glass nodular lung adenocarcinoma early when compared to the diagnostic efficacy of the autoantibodies when used respectively. The combined diagnostic approach of the seven autoantibodies exhibited a sensitivity of 84.05%, specificity of 91.85%, and AUC of 0.8870, surpassing the performance of each autoantibody used individually. Furthermore, we determined that Sparrow Search Algorithm-XGBoost (SSA-XGBOOST) had the best diagnostic performance among the models (AUC=0.9265), with MAGEA1, P53, and PGP9.5 having significant feature weight proportions.

Conclusions

Our research assessed the diagnostic performance of seven autoantibodies in patients with ground-glass nodules for benign-malignant distinction, and the nodules are all no more than 3cm especially. Our study set cut-off values for seven autoantibodies in identifying GGNs no more than 3cm and constructed a machine learning model for effective diagnosis. This provides a non-invasive and highly discriminative method for the evaluation of ground-glass nodules in high-risk patients.

Value of turbo spin echo-based diffusion-weighted imaging in the differential diagnosis of benign and malignant solitary pulmonary lesions

To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)-based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE-based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann-Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = - 4.522, p < 0.001). Additionally, the ADC800, ADCtotal, and D values were all significantly lower in lung cancer than in the benign lesions (Z = - 5.054, -5.370, and -6.047, respectively, all p < 0.001), whereas the f values did not exhibit any statistically significant difference between the two groups. D had the highest area under the curve (AUC = 0.887), followed by ADCtotal (AUC = 0.844), ADC800 (AUC = 0.824), and LSR (AUC = 0.789). The LSR, ADC800, ADCtotal, and D values did not differ statistically significantly in diagnostic effectiveness. Lung DWI using TSE is feasible for differentiating SPLs. The LSR method, conventional DWI, and IVIM have comparable diagnostic efficacy for assessing SPLs.

Amide proton transfer-weighted imaging and stretch-exponential model DWI based 18F-FDG PET/MRI for differentiation of benign and malignant solitary pulmonary lesions

OBJECTIVES

To differentiate benign and malignant solitary pulmonary lesions (SPLs) by amide proton transfer-weighted imaging (APTWI), mono-exponential model DWI (MEM-DWI), stretched exponential model DWI (SEM-DWI), and 18F-FDG PET-derived parameters.

METHODS

A total of 120 SPLs patients underwent chest 18F-FDG PET/MRI were enrolled, including 84 in the training set (28 benign and 56 malignant) and 36 in the test set (13 benign and 23 malignant). MTRasym(3.5 ppm), ADC, DDC, α, SUVmax, MTV, and TLG were compared. The area under receiver-operator characteristic curve (AUC) was used to assess diagnostic efficacy. The Logistic regression analysis was used to identify independent predictors and establish prediction model.

RESULTS

SUVmax, MTV, TLG, α, and MTRasym(3.5 ppm) values were significantly lower and ADC, DDC values were significantly higher in benign SPLs than malignant SPLs (all P < 0.01). SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors. Within the training set, the prediction model based on these independent predictors demonstrated optimal diagnostic efficacy (AUC, 0.976; sensitivity, 94.64%; specificity, 92.86%), surpassing any single parameter with statistical significance. Similarly, within the test set, the prediction model exhibited optimal diagnostic efficacy. The calibration curves and DCA revealed that the prediction model not only had good consistency but was also able to provide a significant benefit to the related patients, both in the training and test sets.

CONCLUSION

The SUVmax, ADC, and MTRasym(3.5 ppm) were independent predictors for differentiation of benign and malignant SPLs, and the prediction model based on them had an optimal diagnostic efficacy.

Whole-lesion histogram analysis of multiple diffusion metrics for differentiating lung cancer from inflammatory lesions

Background

Whole-lesion histogram analysis can provide comprehensive assessment of tissues by calculating additional quantitative metrics such as skewness and kurtosis; however, few studies have evaluated its value in the differential diagnosis of lung lesions.

Purpose

To compare the diagnostic performance of conventional diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI) and diffusion kurtosis imaging (DKI) in differentiating lung cancer from focal inflammatory lesions, based on whole-lesion volume histogram analysis.

Methods

Fifty-nine patients with solitary pulmonary lesions underwent multiple b-values DWIs, which were then postprocessed using mono-exponential, bi-exponential and DKI models. Histogram parameters of the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f), apparent diffusional kurtosis (K_app) and kurtosis-corrected diffusion coefficient (D_app) were calculated and compared between the lung cancer and inflammatory lesion groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic performance.

Results

The ADC^mean, ADC^median, D ^mean and D ^median values of lung cancer were significantly lower than those of inflammatory lesions, while the ADC^skewness, K_app ^mean, K_app ^median, K_app ^SD, K_app ^kurtosis and D_app ^skewness values of lung cancer were significantly higher than those of inflammatory lesions (all p < 0.05). ADC^skewness (p = 0.019) and D ^median (p = 0.031) were identified as independent predictors of lung cancer. D ^median showed the best performance for differentiating lung cancer from inflammatory lesions, with an area under the ROC curve of 0.777. Using a D ^median of 1.091 × 10^-3 mm²/s as the optimal cut-off value, the sensitivity, specificity, positive predictive value and negative predictive value were 69.23%, 85.00%, 90.00% and 58.62%, respectively.

Conclusions

Whole-lesion histogram analysis of DWI, IVIM and DKI parameters is a promising approach for differentiating lung cancer from inflammatory lesions, and D ^median shows the best performance in the differential diagnosis of solitary pulmonary lesions.

Feasibility evaluation of intravoxel incoherent motion diffusion-weighted imaging in the diagnosis of skull-base invasion in nasopharyngeal carcinoma

Objective: This study aimed to evaluate the feasibility of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the diagnosis of skull-base invasion (SBI) in nasopharyngeal carcinoma (NPC). Materials and methods: A total of 50 patients pathologically diagnosed with NPC and a group of 40 controls comprised of those with either normal nasopharynx or patients with nasopharyngitis underwent conventional MRI and IVIM-DWI scans with 3 different groups of b values. Among the 50 patients, 36 patients diagnosed with SBI in NPC were included in the case group according to SBI criteria. All subjects (including those in the control group and case group) were divided into the b1, b2, and b3 groups based on their b values. The pure diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), and microvascular volume fraction (f) values obtained in each measurement area of each group were tested for variance. Next,2 groups of b-value parameters with statistically significant data in the 3 groups were randomly selected for use in both the control group and the case group. A t-test was performed on the D, D*, and f values obtained by measuring each area of the skull base, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the diagnostic efficacy of the D, D*, and f values. Results: There was no statistical significance among the D, D*, and f values of the b1 and b3 groups (P>0.05), and the differences in parameters between the b1 and b2 groups were statistically significant(P < 0.05),and the differences in parameters between the b3 and b2 groups were also statistically significant(P < 0.05).The f value of the case group, which was obtained using the b1 and b2 parameters in each area of the skull base, was lower than that of the control group (P <0.05).The D, D*, and f values of the case group obtained by the b1 and b2 parameters in the pars petrosa of the temporal bone (including the foramen lacerum) were lower than those of the control group (P<0.05).When the parameters of the b1 group were used in the corpus of sphenoid bone (including the foramen ovale), the D, D*, and f values of the control group and the case group were compared, yielding a statistically significant difference (P<0.05).When the parameters of the b1 group were used, the diagnostic efficacy of the f value in each area of the skull base was the highest (AUC=0.908-0.991), followed by the D* value (AUC=0.624-0.692). Conclusion: When the number of b values <200 s/mm² in IVIM-DWI accounts for more than half of the selected b values, IVIM-DWI is highly stable for the diagnosis of SBI in NPC. The D, D*, and f values of the bone and muscle areas of the skull base in patients with SBI of NPC showed a downward trend, and the f value had the best diagnostic performance, followed by the D* value, while the D value had the worst. Thus, IVIM-DWI can be used as a noninvasive method in the diagnosis of SBI in NPC.