Radiologists' Rating for Comparative Qualitative Assessment of Intravoxel Incoherent Motion Using Novel Analysis Methods.

OBJECTIVE: The objective was to assess qualitative interpretability and quantitative precision and reproducibility of intravoxel incoherent motion ( IVIM) parametric images evaluated using novel IVIM analysis methods for diagnostic accuracy. METHODS: Intravoxel incoherent motion datasets of 55 patients (male/female = 41:14; age = 17.8 ± 5.5 years) with histopathology-proven osteosarcoma were analyzed. Intravoxel incoherent motion parameters-diffusion coefficient ( D ), perfusion fraction ( f ), and perfusion coefficient ( D* )-were estimated using 5 IVIM analysis methods-(i) biexponential (BE) model, (ii) BE-segmented fitting 2-parameter (BESeg-2), (iii) BE-segmented fitting 1-parameter (BESeg-1), (iv) BE model with total variation penalty function (BE + TV), and (v) BE model with Huber penalty function (BE + HPF). Qualitative scoring in a 5-point Likert scale (uninterpretable: 1; poor: 2; fair: 3; good: 4; excellent: 5) was performed by 2 radiologists for 4 criteria: (a) tumor shape and margin, (b) morphologic correlation, (c) noise suppression, and (d) overall interpretability. Interobserver agreement was evaluated using Spearman rank-order correlation ( rs ). Precision and reproducibility were evaluated using within-subject coefficient of variation (wCV) and between-subject coefficient of variation (bCV). RESULTS: BE + TV and BE + HPF produced significantly ( P < 10 -3 ) higher qualitative scores for D (fair-good [3.3-3.8]) than BE (poor [2.3]) and for D* (poor-fair [2.2-2.7]) and f (fair-good [3.2-3.8]) than BE, BESeg-2, and BESeg-1 ( D* : uninterpretable-poor [1.3-1.9] and f : poor-fair [1.5-3]). Interobserver agreement for qualitative scoring was rs = 0.48-0.59, P < 0.009. BE + TV and BE + HPF showed significantly ( P < 0.05) improved reproducibility in estimating D (wCV: 24%-31%, bCV: 21%-31% improvement) than the BE method and D* (wCV: 4%-19%, bCV: 5%-19% improvement) and f (wCV: 25%-49%, bCV: 25%-47% improvement) than BE, BESeg-2, and BESeg-1 methods. CONCLUSIONS: BE + TV and BE + HPF demonstrated qualitatively and quantitatively improved IVIM parameter estimation and may be considered for clinical use further.

Non-invasive intravoxel incoherent motion MRI in prediction of histopathological response to neoadjuvant chemotherapy and survival outcome in osteosarcoma at the time of diagnosis.

BACKGROUND: Early prediction of response to neoadjuvant chemotherapy (NACT) is important to aid personalized treatment in osteosarcoma. Diffusion-weighted Intravoxel Incoherent Motion (IVIM) MRI was used to evaluate the predictive value for response to NACT and survival outcome in osteosarcoma. METHODS: Total fifty-five patients with biopsy-proven osteosarcoma were recruited prospectively, among them 35 patients were further analysed. Patients underwent 3 cycles of NACT (Cisplatin + Doxorubicin) followed by surgery and response adapted adjuvant chemotherapy. Treatment outcomes were histopathological response to NACT (good-response ≥ 50% necrosis and poor-response < 50% necrosis) and survival outcome (event-free survival (EFS) and overall survival (OS)). IVIM MRI was acquired at 1.5T at baseline (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Quantitative IVIM parameters (D, D*, f & D*.f) were estimated using advanced state-of-the-art spatial penalty based IVIM analysis method bi-exponential model with total-variation penalty function (BETV) at 3 time-points and histogram analysis was performed. RESULTS: Good-responders: Poor-responders ratio was 13 (37%):22 (63%). EFS and OS were 31% and 69% with 16.27 and 25.9 months of median duration respectively. For predicting poor-response to NACT, IVIM parameters showed AUC = 0.87, Sensitivity = 86%, Specificity = 77% at t0, and AUC = 0.96, Sensitivity = 86%, Specificity = 100% at t1. Multivariate Cox regression analysis showed smaller tumour volume (HR = 1.002, p = 0.001) higher ADC-25th-percentile (HR = 0.047, p = 0.005) & D-Mean (HR = 0.1, p = 0.023) and lower D*-Mean (HR = 1.052, p = 0.039) were independent predictors of longer EFS (log-rank p-values: 0.054, 0.0034, 0.0017, 0.0019 respectively) and non-metastatic disease (HR = 4.33, p < 10-3), smaller tumour-volume (HR = 1.001, p = 0.042), lower D*-Mean (HR = 1.045, p = 0.056) and higher D*.f-skewness (HR = 0.544, p = 0.048) were independent predictors of longer OS (log-rank p-values: < 10-3, 0.07, < 10-3, 0.019 respectively). CONCLUSION: IVIM parameters obtained with a 1.5T scanner along with novel BETV method and their histogram analysis indicating tumour heterogeneity were informative in characterizing NACT response and survival outcome in osteosarcoma.

IVIM-DKI for differentiation between prostate cancer and benign prostatic hyperplasia: comparison of 1.5 T vs. 3 T MRI.

OBJECTIVE: To implement an advanced spatial penalty-based reconstruction to constrain the intravoxel incoherent motion (IVIM)-diffusion kurtosis imaging (DKI) model and investigate whether it provides a suitable alternative at 1.5 T to the traditional IVIM-DKI model at 3 T for clinical characterization of prostate cancer (PCa) and benign prostatic hyperplasia (BPH). MATERIALS AND METHODS: Thirty-two patients with biopsy-proven PCa were recruited for MRI examination (n = 16 scanned at 1.5 T, n = 16 scanned at 3 T). Diffusion-weighted imaging (DWI) with 13 b values (b = 0 to 2000 s/mm2 up to 3 averages, 1.5 T: TR = 5.774 s, TE = 81 ms and 3 T: TR = 4.899 s, TE = 100 ms), T2-weighted, and T1-weighted imaging were used on the 1.5 T and 3 T MRI scanner, respectively. The IVIM-DKI signal was modeled using the traditional IVIM-DKI model and a novel model in which the total variation (TV) penalty function was combined with the traditional model to optimize non-physiological variations. Paired and unpaired t-tests were used to compare intra-scanner and scanner group differences in IVIM-DKI parameters obtained using the novel and the traditional models. Analysis of variance with post hoc test and receiver operating characteristic (ROC) curve analysis were used to assess the ability of parameters obtained using the novel model (at 1.5 T) and the traditional model (at 3 T) to characterize prostate lesions. RESULTS: IVIM-DKI modeled using novel model with TV spatial penalty function at 1.5 T, produced parameter maps with 50-78% lower coefficient of variation (CV) than traditional model at 3 T. Novel model estimated higher D with lower D*, f and k values at both field strengths compared to traditional model. For scanner differences, the novel model at 1.5 T estimated lower D* and f values as compared to traditional model at 3 T. At 1.5 T, D and f values were significantly lower with k values significantly higher in tumor than BPH and healthy tissue. D (AUC: 0.98), f (AUC: 0.82), and k (AUC: 0.91) parameters estimated using novel model showed high diagnostic performance in cancer lesion detection at 1.5 T. DISCUSSION: In comparison with the IVIM-DKI model at 3 T, IVIM-DKI signal modeled with the TV penalty function at 1.5 T showed lower estimation errors. The proposed novel model can be utilized for improved detection of prostate lesions.

Texture analysis for chemotherapy response evaluation in osteosarcoma using MR imaging.

The efficacy of MRI-based statistical texture analysis (TA) in predicting chemotherapy response among patients with osteosarcoma was assessed. Forty patients (male: female = 31:9; age = 17.2 ± 5.7 years) with biopsy-proven osteosarcoma were analyzed in this prospective study. Patients were scheduled for three cycles of neoadjuvant chemotherapy (NACT) and diffusion-weighted MRI acquisition at three time points: at baseline (t0), after the first NACT (t1) and after the third NACT (t2) using a 1.5 T scanner. Eight patients (nonsurvivors) died during NACT while 34 patients (survivors) completed the NACT regimen followed by surgery. Histopathological evaluation was performed in the resected tumor to assess NACT response (responder [≤50% viable tumor] and nonresponder [>50% viable tumor]) and revealed nonresponder: responder = 20:12. Apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters, diffusion coefficient (D), perfusion coefficient (D*) and perfusion fraction (f) were evaluated. A total of 25 textural features were evaluated on ADC, D, D* and f parametric maps and structural T1-weighted (T1W) and T2-weighted (T2W) images in the entire tumor volume using 3D TA methods gray-level cooccurrence matrix (GLCM), neighborhood gray-tone-difference matrix (NGTDM) and run-length matrix (RLM). Receiver-operating-characteristic curve analysis was performed on the selected textural feature set to assess the role of TA features (a) as marker(s) of tumor aggressiveness leading to mortality at baseline and (b) in predicting the NACT response among survivors in the course of treatment. Findings showed that the NGTDM features coarseness, busyness and strength quantifying tumor heterogeneity in D, D* and f maps and T1W and T2W images were useful markers of tumor aggressiveness in identifying the nonsurvivor group (area-under-the-curve [AUC] = 0.82-0.88) at baseline. The GLCM features contrast and correlation, NGTDM features contrast and complexity and RLM feature short-run-low-gray-level-emphasis quantifying homogeneity/terogeneity in tumor were effective markers for predicting chemotherapeutic response using D (AUC = 0.80), D* (AUC = 0.80) and T2W (AUC = 0.70) at t0, and D* (AUC = 0.80) and f (AUC = 0.70) at t1. 3D statistical TA features might be useful as imaging-based markers for characterizing tumor aggressiveness and predicting chemotherapeutic response in patients with osteosarcoma.

SLIC-supervoxels-based response evaluation of osteosarcoma treated with neoadjuvant chemotherapy using multi-parametric MR imaging.

OBJECTIVE: Histopathological examination (HPE) is the current gold standard for assessing chemotherapy response to tumor, but it is possible only after surgery. The purpose of the study was to develop a noninvasive, imaging-based robust method to delineate, visualize, and quantify the proportions of necrosis and viable tissue present within the tumor along with peritumoral edema before and after neoadjuvant chemotherapy (NACT) and to evaluate treatment response with correlation to HPE necrosis after surgery. METHODS: The MRI dataset of 30 patients (N = 30; male:female = 24:6; age = 17.6 ± 2.7 years) with osteosarcoma was acquired using 1.5 T Philips Achieva MRI scanner before (baseline) and after 3 cycles of NACT (follow-up). After NACT, all patients underwent surgical resection followed by HPE. Simple linear iterative clustering supervoxels and Otsu multithresholding were combined to develop the proposed method-SLICs+MTh-to subsegment and quantify viable and nonviable regions within tumor using multiparametric MRI. Manually drawn ground-truth ROIs and SLICs+MTh-based segmentation of tumor, edema, and necrosis were compared using Jacquard index (JI), Dice coefficient (DC), precision (P), and recall (R). Postcontrast T1W images (PC-T1W) were used to validate the SLICs+MTh-based necrosis. SLICs+MTh-based necrosis volume at follow-up was compared with HPE necrosis using paired t test (p ≤ 0.05). RESULTS: Active tumor, necrosis, and edema were segmented with moderate to satisfactory accuracy (JI = 62-78%; DC = 72-87%; P = 67-87%; R = 63-88%). Qualitatively and quantitatively (DC = 74 ± 9%), the SLICs+MTh-based necrosis area correlated well with the hypointense necrosis areas in PC-T1W. No significant difference (paired t test, p = 0.26; Bland-Altman plot, bias = 2.47) between SLICs+MTh-based necrosis at follow-up and HPE necrosis was observed. CONCLUSION: The proposed multiparametric MRI-based SLICs+MTh method performs noninvasive assessment of NACT response in osteosarcoma that may improve cancer treatment monitoring, planning, and overall prognosis. KEY POINTS: • The simple linear iterative clustering supervoxels and Otsu multithresholding-based technique (SLICs+MTh) successfully estimates the proportion of necrosis, viable tumor, and edema in osteosarcoma in the course of chemotherapy. • The proposed technique is noninvasive and uses multiparametric MRI to measure necrosis as an indication of anticancer treatment response. • SLICs+MTh-based necrosis was in satisfactory agreement with histological necrosis after surgery.

Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans.

PURPOSE: The proposed work aims to develop an algorithm to precisely segment the lung parenchyma in thoracic CT scans. To achieve this goal, the proposed technique utilized a combination of deep learning and traditional image processing algorithms. The initial step utilized a trained convolutional neural network (CNN) to generate preliminary lung masks, followed by the proposed post-processing algorithm for lung boundary correction. METHODS: First, the proposed method trained an improved 2D U-Net CNN model with Inception-ResNet-v2 as its backbone. The model was trained on 32 CT scans from two different sources: one from the VESSEL12 grand challenge and the other from AIIMS Delhi. Further, the model's performance was evaluated on a test dataset of 16 CT scans with juxta-pleural nodules obtained from AIIMS Delhi and the LUNA16 challenge. The model's performance was assessed using evaluation metrics such as average volumetric dice coefficient (DSCavg), average IoU score (IoUavg), and average F1 score (F1avg). Finally, the proposed post-processing algorithm was implemented to eliminate false positives from the model's prediction and to include juxta-pleural nodules in the final lung masks. RESULTS: The trained model reported a DSCavg of 0.9791 ± 0.008, IoUavg of 0.9624 ± 0.007, and F1avg of 0.9792 ± 0.004 on the test dataset. Applying the post-processing algorithm to the predicted lung masks obtained a DSCavg of 0.9713 ± 0.007, IoUavg of 0.9486 ± 0.007, and F1avg of 0.9701 ± 0.008. The post-processing algorithm successfully included juxta-pleural nodules in the final lung mask. CONCLUSIONS: Using a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model's predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.

Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.

PURPOSE: The current study explores the application of 3D U-Net architectures combined with Inception and ResNet modules for precise lung nodule detection through deep learning-based segmentation technique. This investigation is motivated by the objective of developing a Computer-Aided Diagnosis (CAD) system for effective diagnosis and prognostication of lung nodules in clinical settings. METHODS: The proposed method trained four different 3D U-Net models on the retrospective dataset obtained from AIIMS Delhi. To augment the training dataset, affine transformations and intensity transforms were utilized. Preprocessing steps included CT scan voxel resampling, intensity normalization, and lung parenchyma segmentation. Model optimization utilized a hybrid loss function that combined Dice Loss and Focal Loss. The model performance of all four 3D U-Nets was evaluated patient-wise using dice coefficient and Jaccard coefficient, then averaged to obtain the average volumetric dice coefficient (DSCavg) and average Jaccard coefficient (IoUavg) on a test dataset comprising 53 CT scans. Additionally, an ensemble approach (Model-V) was utilized featuring 3D U-Net (Model-I), ResNet (Model-II), and Inception (Model-III) 3D U-Net architectures, combined with two distinct patch sizes for further investigation. RESULTS: The ensemble of models obtained the highest DSCavg of 0.84 ± 0.05 and IoUavg of 0.74 ± 0.06 on the test dataset, compared against individual models. It mitigated false positives, overestimations, and underestimations observed in individual U-Net models. Moreover, the ensemble of models reduced average false positives per scan in the test dataset (1.57 nodules/scan) compared to individual models (2.69-3.39 nodules/scan). CONCLUSIONS: The suggested ensemble approach presents a strong and effective strategy for automatically detecting and delineating lung nodules, potentially aiding CAD systems in clinical settings. This approach could assist radiologists in laborious and meticulous lung nodule detection tasks in CT scans, improving lung cancer diagnosis and treatment planning.

A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.

The presence of lung metastases in patients with primary malignancies is an important criterion for treatment management and prognostication. Computed tomography (CT) of the chest is the preferred method to detect lung metastasis. However, CT has limited efficacy in differentiating metastatic nodules from benign nodules (e.g., granulomas due to tuberculosis) especially at early stages (<5 mm). There is also a significant subjectivity associated in making this distinction, leading to frequent CT follow-ups and additional radiation exposure along with financial and emotional burden to the patients and family. Even 18F-fluoro-deoxyglucose positron emission technology-computed tomography (18F-FDG PET-CT) is not always confirmatory for this clinical problem. While pathological biopsy is the gold standard to demonstrate malignancy, invasive sampling of small lung nodules is often not clinically feasible. Currently, there is no non-invasive imaging technique that can reliably characterize lung metastases. The lung is one of the favored sites of metastasis in sarcomas. Hence, patients with sarcomas, especially from tuberculosis prevalent developing countries, can provide an ideal platform to develop a model to differentiate lung metastases from benign nodules. To overcome the lack of optimal specificity of CT scan in detecting pulmonary metastasis, a novel artificial intelligence (AI)-based protocol is proposed utilizing a combination of radiological and clinical biomarkers to identify lung nodules and characterize it as benign or metastasis. This protocol includes a retrospective cohort of nearly 2,000-2,250 sample nodules (from at least 450 patients) for training and testing and an ambispective cohort of nearly 500 nodules (from 100 patients; 50 patients each from the retrospective and prospective cohort) for validation. Ground-truth annotation of lung nodules will be performed using an in-house-built segmentation tool. Ground-truth labeling of lung nodules (metastatic/benign) will be performed based on histopathological results or baseline and/or follow-up radiological findings along with clinical outcome of the patient. Optimal methods for data handling and statistical analysis are included to develop a robust protocol for early detection and classification of pulmonary metastasis at baseline and at follow-up and identification of associated potential clinical and radiological markers.

T1 mapping as a surrogate marker of chemotherapy response evaluation in patients with osteosarcoma.

OBJECTIVES: To characterize baseline T1 values of tumors, measure changes after the course of chemotherapy, and evaluate its potential as a marker of response assessment in patients with osteosarcoma. MATERIALS AND METHODS: A total of 35 patients (male:female = 27:8; age = 17.9 ± 6 years; metastatic:localized = 11:24) with biopsy-proven osteosarcoma were analyzed prospectively. All patients underwent magnetic resonance imaging before neoadjuvant chemotherapy (NACT) (baseline) and after NACT completion (follow-up), followed by surgery and histopathological evaluation. Histopathological necrosis served as the gold standard for assessing chemotherapy response (responder: ≥50% necrosis and non-responder: <50% necrosis). Three-dimensional spoiled gradient recalled echo images were acquired with varying flip angles (50, 100, 200, and 300) using a 1.5 Tesla scanner. T1 values were estimated in healthy muscle tissue and tumors at baseline and follow-up, and the relative percentage changes after NACT (ΔT1) were evaluated, and histogram analysis was performed to characterize the T1 value of the tumor and predict the NACT response using the Pearson correlation coefficient (r) and receiver operating characteristic curve analysis. RESULTS: At baseline, a significantly higher T1-mean (830.96 ± 193.88 msec versus 683.29 ± 210.00 msec; p = 0.003) and lower T1-skewness (0.86 ± 1.66 versus 1.60 ± 1.55; p = 0.02) were observed in osteosarcoma than healthy tissue. Responder:non-responder ratio was 13:22. At baseline, a significantly higher T1-mean (936.14 ± 193.17 msec versus 768.82 ± 169.25 msec; p = 0.018) and lower T1-skewness (-0.17 ± 0.85 versus 1.47 ± 1.73; p = 0.003) were observed among responders, than non-responders. After NACT, ΔT1 in tumor was significantly higher among responders than non-responders (-27.22 ± 12.17% versus -15.70 ± 18.99%; p = 0.034). ΔT1-mean and ΔT1-skewness after NACT were moderately correlated (r =  - 0.4, p = 0.030; r = 0.4, p = 0.011) with histopathological necrosis. For predicting NACT response, T1-mean and T1-skewness jointly produced an area under the curve (AUC) = 0.80 at baseline, and ΔT1-mean and ΔT1-skewness jointly produced AUC = 0.76 after NACT. CONCLUSION: The mean and skewness of T1 values in osteosarcoma are potential non-invasive imaging markers for chemotherapy response assessment.

Automatic segmentation and RECIST score evaluation in osteosarcoma using diffusion MRI: A computer aided system process.

PURPOSE: Accuracy and consistency in RECIST (Response evaluation criteria in solid tumors) measurements are crucial for treatment planning. Manual RECIST measurement is tedious, prone-to-error and operator-subjective. Objective was to develop a fully automated system for tumor segmentation and RECIST score estimation with reasonable accuracy, consistency and speed. METHODS: Diffusion weight images (DWI) of forty patients (N = 40; Male:Female = 30:10; Age = 17.7 ±â€¯5.9years) with Osteosarcoma was acquired using 1.5 T MRI scanner before (baseline) and after neoadjuvant chemotherapy (follow-up). 3D tumor volume was segmented applying Simple-linear-iterative-clustering Superpixels (SLIC-S) and Fuzzy-c-means-clustering (FCM) separately. Connected-component-analysis was performed to identify image-slice with maximum tumor-burden (Max-burden-sliceno) and measure tumor-sizes (Tumor-diameter(cm) & Tumor-volume(cc)). Relative-percentage-changes in tumor-sizes across time-points were scored using RECIST1.1 and Volumetric-response criterion. Segmentation accuracy was estimated by Dice-coefficient (DC), Jaccard-Index (JI), Precision (P) and Recall (R). Evaluated Apparent-diffusion-coefficient (ADC), Tumor-diameter, Max-burden-sliceno and Tumor-volume in segmented tumor-mask and ground-truth tumor-mask were compared using paired-t-test (p < 0.05), Pearson-correlation-coefficient(PCC) and Bland-Altman plots. Misclassification-error-rate (MER) was evaluated for automated RECIST1.1 and Volumetric-response scoring methods. RESULTS: Automated SLIC-S and FCM produced satisfactory tumor segmentation (DC:∼70-83%;JI:∼55-72%;P:∼64-85%;R:∼73-83%) and showed excellent correlation with ground-truth measurements in estimating ADC (p > 0.05; PCC=0.84-0.89), Tumor-diameters (p > 0.05; PCC=0.90-0.95; bias=0.3-2.41), Max-burden-sliceno (p > 0.05; PCC=0.87-0.96) and Tumor-volumes (p > 0.05; PCC=0.89-0.94; bias=15.19-131.81) at baseline and follow-up. MER for SLIC-S and FCM were comparable for RECIST1.1 (15-18 %) and Volumetric-response (18-20 %) scores and assessment times were 2-3s and 4-6s per patient respectively. CONCLUSIONS: Proposed method produced promising segmentation and RECIST score measurements in current bone tumor dataset and might be useful as decision-support-tool for response evaluation in other tumors.

Intravoxel incoherent motion (IVIM) for response assessment in patients with osteosarcoma undergoing neoadjuvant chemotherapy.

PURPOSE: To explore the role of quantitative Intravoxel incoherent motion (IVIM) parameters and their histogram analysis in characterizing changes in Osteosarcoma receiving neoadjuvant chemotherapy (NACT) and evaluating therapeutic response. METHODS: Forty patients (N = 40; Male:Female = 30:10; Age = 17.7 ± 5.9years; Metastatic:localized = 17:23) with histologically confirmed Osteosarcoma treated with 3-cycles of NACT were analyzed prospectively. All patients underwent Diffusion weighted imaging (DWI) with 11 b-values (0-800 s/mm2) using 1.5 T MRI scanner at pre-treatment (t0), after 1-cycle (t1) and after 3-cycles (t2) of NACT. Non-invasive response evaluation of NACT was performed using RECIST1.1 criteria. Apparent-diffusion-coefficient (ADC) and IVIM parameters - Diffusion-coefficient (D), Perfusion-coefficient (D*) & Perfusion-fraction (f) and their relative percentage changes from time-point t0-t1 (Δ2) and t0-t2 (Δ2) were evaluated and histogram analysis was performed at three time-points and compared with respect to RECIST1.1 scores. RESULTS: Using RECIST1.1 criteria, 11 (27.5%), 21 (52.5%) and 8 (20%) patients were in Partial-responder (PR), Stable-disease (SD) and Progressive-disease (PD) groups respectively. Pre-NACT (t0), average ADC, D,D*&f in tumor volume were 1.36 ± 0.33 × 10-3 mm2/s, 1.3 ± 0.3 × 10-3 mm2/s, 28.44 ± 10.34 × 10-3 mm2/s & 13.95 ± 2.83% respectively. Using ANOVA test, during NACT (t1, t2), D*-variance (p = 0.038, 0.003) and f-skewness (p = 0.03, 0.03) and at t2, D*-entropy (p = 0.001) and f-entropy (p = 0.002) and their Δ2 changes (p = 0.001, 0.003) were statistically significant among response groups. At t1, D*-variance and f-skewness jointly showed AUC = 0.77 & 0.74 in classifying PR (Sensitivity = 73%; Specificity = 70%) and SD (Sensitivity = 74; Specificity = 75%) groups respectively in patient cohort. Δ1 & Δ2 changes of D*-mean, D*-variance, D*-entropy and f-entropy correlated well (0.5-0.6) with tumor-diameter and tumor-volume changes. CONCLUSIONS: Quantitative IVIM parameters, especially D* &f and their histogram analysis were informative and can be used as noninvasive surrogate markers for early response assessment during the course of NACT in Osteosarcoma.

Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT.

PURPOSE: High mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach. METHODS: Contrast-enhanced CT dataset of 40 patients (n = 40; M:F = 5:3; age = 25-55 years) with three groups of subjects: healthy (n = 14), cirrhosis (n = 12) and cirrhosis with HCC (n = 14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM). RESULTS: The proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p < 0.05) among healthy and diseased liver, and using logistic regression, classification accuracy obtained was 92.5%. For detecting cirrhosis and HCC liver, SVM with RBF kernel obtained highest slice-wise and patient-wise prediction accuracy of 86.9% (precision = 0.93, recall = 0.7) and 80% (precision = 0.86, recall = 0.75), respectively, than that of linear kernel (slice-wise: accuracy = 85.4%, precision = 0.92, recall = 0.67; patient-wise: accuracy = 73.33%, precision = 0.75, recall = 0.75). CONCLUSIONS: The proposed computer-aided diagnosis system for detecting cirrhosis and hepatocellular carcinoma (HCC) showed promising results and can be used as effective screening tool in medical image analysis.