Early Assessment of Colorectal Cancer Patients with Liver Metastases Treated with Antiangiogenic Drugs: The Role of Intravoxel Incoherent Motion in Diffusion-Weighted Imaging

Application of Intravoxel Incoherent Motion in Clinical Liver Imaging: A Literature Review

Intravoxel incoherent motion (IVIM) modeling is a widely used double-exponential model for describing diffusion-weighted imaging (DWI) signal, with a slow component related to pure molecular diffusion and a fast component associated with microcirculatory perfusion, which compensates for the limitations of traditional DWI. IVIM is a noninvasive technique for obtaining liver pathological information and characterizing liver lesions, and has potential applications in the initial diagnosis and treatment monitoring of liver diseases. Recent studies have demonstrated that IVIM-derived parameters are useful for evaluating liver lesions, including nonalcoholic fatty liver disease (NAFLD), liver fibrosis and liver tumors. However, the results are not stable. Therefore, it is necessary to summarize the current applications of IVIM in liver disease research, identify existing shortcomings, and point out the future development direction. In this review, we searched for studies related to hepatic IVIM-DWI applications over the past two decades in the PubMed database. We first introduce the fundamental principles and influential factors of IVIM, and then discuss its application in NAFLD, liver fibrosis, and focal hepatic lesions. It has been found that IVIM is still unstable in ensuring the robustness and reproducibility of measurements in the assessment of liver fibrosis grade and liver tumors differentiation, due to inconsistent and substantial overlap in the range of IVIM-derived parameters for different fibrotic stages. In the end, the future direction of IVIM-DWI in the assessment of liver diseases is discussed, emphasizing the need for further research on the stability of IVIM-derived parameters, particularly perfusion-related parameters, in order to promote the clinical practice of IVIM-DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 3.

Repeatability and reproducibility comparisons of liver IVIM imaging with free-breathing or respiratory-triggered sequences

For liver intravoxel incoherent motion (IVIM) data acquisition, respiratory-triggering (RT) MRI is commonly used, and there are strong motivations to shorten the scan duration. For the same scan duration, more b values or higher numbers of excitations can be allowed for free-breathing (FB) imaging than for RT. We studied whether FB can be used to replace RT when careful IVIM image acquisition and image processing are conducted. MRI data of 22 healthy participants were acquired using a 3.0 T scanner. Diffusion imaging was based on a single-shot spin-echo-type echo-planar sequence and 16 b values of 0, 2, 4, 7, 10, 15, 20, 30, 46, 60, 72, 100, 150, 200, 400, and 600 s/mm2 . Each subject attended two scan sessions with an interval of 10-20 days. For each scan session, a subject was scanned twice, first with RT and then with FB. The mean image acquisition time was 5.4 min for FB and 10.8 min for RT. IVIM parameters were calculated with bi-exponential model segmented fitting with a threshold b value of 60 s/mm2 , and fitting started from b = 2 s/mm2 . There was no statistically significant difference between IVIM parameters measured with FB imaging or RT imaging. Perfusion fraction ICC (intraclass correlation coefficient) for FB imaging and RT imaging in the same scan session was 0.824. For perfusion fraction, wSD (within-subject standard deviation), BA (Bland-Altman) difference, BA 95% limit, and ICC were 0.022, 0.0001, -0.0635~0.0637, and 0.687 for FB and 0.031, 0.0122, -0.0723~0.0967, and 0.611 for RT. For Dslow (×10-3  s/mm2 ), wSD, BA difference, BA 95% limit, and ICC were 0.057, 0.0268, -0.1258~0.1793, and 0.471 for FB and 0.073, -0.0078, -0.2170-0.2014, and <0.4 for RT. The Dfast coefficient of variation was 0.20 for FB imaging and 0.28 for RT imaging. All reproducibility indicators slightly favored FB imaging.

Role of intravoxel incoherent motion (IVIM) imaging in response assessment of hepatocellular carcinoma after transarterial chemoembolization (TACE) - A prospective study

INTRODUCTION

Response evaluation of hepatocellular carcinoma (HCC) currently is based on arterial phase enhancement which doesn't take into microstructural changes in the tumor after trans-arterial chemoembolization (TACE).

AIM

This prospective study was conducted to assess the feasibility and efficacy of intravascular incoherent motion imaging (IVIM) in response evaluation of HCC after TACE. 39 cirrhotic patients with 48 HCC underwent MR imaging 1 week within and 6weeks after TACE. IVIM parameters like Dslow (true diffusion), Dfast (pseudodiffusion), perfusion fraction and ADC were measured prior to and postTACE. The pre and post TACE values in LR-TR (LIRADS - treatment response) nonviable and viable lesions were compared using paired t-tests. ROC curve analysis was done to calculate sensitivity and specificity and propose cut-off values.

RESULT

Non-viable lesions showed a significant increase in Dslow (1.208 ± 0.581 vs 1.560 ± 0.494, P-value -.0207) and ADC (1.37 ± 0.53 vs 1.65 ± 0.4287, P value .016) after TACE. There was also significant decrease in Dfast (33.7 ± 10.4 vs 23.75 ± 12.13, P value .0005) and f (19.92 ± 10.54 vs 12.9 ± 10.41, P value .012) values after TACE in non-viable lesions compared to viable lesions. The change in true diffusion had the highest AUC (0.741) among IVIM parameters with greater than 0.075 increase between preTACE and postTACE values having a sensitivity and specificity of 81.8% and 60% respectively for complete response.

CONCLUSION

IVIM imaging is feasible to assess the response in HCC after TACE. True diffusion is more sensitive and specific than apparent diffusion in evaluating the response.

Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment

OBJECTIVE

The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated.

METHODS

The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed.

RESULTS

The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence.

CONCLUSIONS

The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.

Diffusion-weighted MRI for predicting and assessing treatment response of liver metastases from CRC - A systematic review and meta-analysis

The evaluation of response to chemotherapy and targeted therapies in colorectal liver metastases has traditionally been based on size changes, as per the RECIST criteria. However, therapy may alter tissue composition and not only tumor size, therefore, functional imaging techniques such as diffusion-weighted magnetic resonance imaging (DWI) may offer a more comprehensive assessment of treatment response. The aim of this systematic review and meta-analysis was to evaluate the use of DWI in the prediction and assessment of response to treatment in colorectal liver metastases and to determine if there is a baseline apparent diffusion coefficient (ADC) cut-off value that can predict a favorable response. A literature search was conducted using the MEDLINE/PubMed database, and risk of bias was evaluated using the QUADAS-2 tool. The mean differences between responders and non-responders were pooled. A total of 16 studies met the inclusion criteria, and various diffusion-derived techniques and coefficients were found to have potential for predicting and assessing treatment response. However, discrepancies were noted between studies. The most consistent predictor of response was a lower baseline ADC value calculated using traditional mono-exponential methods. Non-mono-exponential techniques for calculating DWI-derived parameters were also reported. A meta-analysis of a subset of studies failed to establish a cut-off value of ADC due to heterogeneity, but revealed a pooled mean difference of -0.12 × 10^-3 mm²/s between responders and non-responders. The results of this systematic review suggest that diffusion-derived techniques and coefficients may contribute to the evaluation and prediction of treatment response in colorectal liver metastases. Further controlled prospective studies are needed to confirm these findings and to guide clinical and radiological decision-making in the management of patients with CRC liver metastases.

Colorectal liver metastases patients prognostic assessment: prospects and limits of radiomics and radiogenomics

In this narrative review, we reported un up-to-date on the role of radiomics to assess prognostic features, which can impact on the liver metastases patient treatment choice. In the liver metastases patients, the possibility to assess mutational status (RAS or MSI), the tumor growth pattern and the histological subtype (NOS or mucinous) allows a better treatment selection to avoid unnecessary therapies. However, today, the detection of these features require an invasive approach. Recently, radiomics analysis application has improved rapidly, with a consequent growing interest in the oncological field. Radiomics analysis allows the textural characteristics assessment, which are correlated to biological data. This approach is captivating since it should allow to extract biological data from the radiological images, without invasive approach, so that to reduce costs and time, avoiding any risk for the patients. Several studies showed the ability of Radiomics to identify mutational status, tumor growth pattern and histological type in colorectal liver metastases. Although, radiomics analysis in a non-invasive and repeatable way, however features as the poor standardization and generalization of clinical studies results limit the translation of this analysis into clinical practice. Clear limits are data-quality control, reproducibility, repeatability, generalizability of results, and issues related to model overfitting.

Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence

Intrahepatic cholangiocarcinoma (iCCA) is the second most common primary liver tumor, with a median survival of only 13 months. Surgical resection remains the only curative therapy; however, at first detection, only one-third of patients are at an early enough stage for this approach to be effective, thus rendering early diagnosis as an efficient approach to improving survival. Therefore, the identification of higher-risk patients, whose risk is correlated with genetic and pre-cancerous conditions, and the employment of non-invasive-screening modalities would be appropriate. For several at-risk patients, such as those suffering from primary sclerosing cholangitis or fibropolycystic liver disease, the use of periodic (6-12 months) imaging of the liver by ultrasound (US), magnetic Resonance Imaging (MRI)/cholangiopancreatography (MRCP), or computed tomography (CT) in association with serum CA19-9 measurement has been proposed. For liver cirrhosis patients, it has been proposed that at-risk iCCA patients are monitored in a similar fashion to at-risk HCC patients. The possibility of using Artificial Intelligence models to evaluate higher-risk patients could favor the diagnosis of these entities, although more data are needed to support the practical utility of these applications in the field of screening. For these reasons, it would be appropriate to develop screening programs in the research protocols setting. In fact, the success of these programs reauires patient compliance and multidisciplinary cooperation.

Imaging Features of Main Hepatic Resections: The Radiologist Challenging

Liver resection is still the most effective treatment of primary liver malignancies, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), and of metastatic disease, such as colorectal liver metastases. The type of liver resection (anatomic versus non anatomic resection) depends on different features, mainly on the type of malignancy (primary liver neoplasm versus metastatic lesion), size of tumor, its relation with blood and biliary vessels, and the volume of future liver remnant (FLT). Imaging plays a critical role in postoperative assessment, offering the possibility to recognize normal postoperative findings and potential complications. Ultrasonography (US) is the first-line diagnostic tool to use in post-surgical phase. However, computed tomography (CT), due to its comprehensive assessment, allows for a more accurate evaluation and more normal findings than the possible postoperative complications. Magnetic resonance imaging (MRI) with cholangiopancreatography (MRCP) and/or hepatospecific contrast agents remains the best tool for bile duct injuries diagnosis and for ischemic cholangitis evaluation. Consequently, radiologists should be familiar with the surgical approaches for a better comprehension of normal postoperative findings and of postoperative complications.

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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-25^th-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.

Multimodality Imaging Assessment of Desmoid Tumors: The Great Mime in the Era of Multidisciplinary Teams

Desmoid tumors (DTs), also known as desmoid fibromatosis or aggressive fibromatosis, are rare, locally invasive, non-metastatic soft tissue tumors. Although histological results represent the gold standard diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. Although histological analysis represents the gold standard for diagnosis, imaging represents the fundamental tool for the diagnosis of these tumors. DTs represent a challenge for the radiologist, being able to mimic different pathological conditions. A proper diagnosis is required to establish an adequate therapeutic approach. Multimodality imaging, including ultrasound (US), computed tomography (CT) and Magnetic Resonance Imaging (MRI), should be preferred. Different imaging techniques can also guide minimally invasive treatments and monitor their effectiveness. The purpose of this review is to describe the state-of-the-art multidisciplinary imaging of DTs; and its role in patient management.

A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls

Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.

Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases

PURPOSE

The purpose of this study is to evaluate the Radiomics and Machine Learning Analysis based on MRI in the assessment of Liver Mucinous Colorectal Metastases.Query METHODS: The cohort of patients included a training set (121 cases) and an external validation set (30 cases) with colorectal liver metastases with pathological proof and MRI study enrolled in this approved study retrospectively. About 851 radiomics features were extracted as median values by means of the PyRadiomics tool on volume on interest segmented manually by two expert radiologists. Univariate analysis, linear regression modelling and pattern recognition methods were used as statistical and classification procedures.

RESULTS

The best results at univariate analysis were reached by the wavelet_LLH_glcm_JointEntropy extracted by T2W SPACE sequence with accuracy of 92%. Linear regression model increased the performance obtained respect to the univariate analysis. The best results were obtained by a linear regression model of 15 significant features extracted by the T2W SPACE sequence with accuracy of 94%, a sensitivity of 92% and a specificity of 95%. The best classifier among the tested pattern recognition approaches was k-nearest neighbours (KNN); however, KNN achieved lower precision than the best linear regression model.

CONCLUSIONS

Radiomics metrics allow the mucinous subtype lesion characterization, in order to obtain a more personalized approach. We demonstrated that the best performance was obtained by T2-W extracted textural metrics.

Bi-exponential fitting excluding b=0 data improves the scan-rescan stability of liver IVIM parameter measures and particularly so for the perfusion fraction

BACKGROUND

A prerequisite to translating intravoxel incoherent motion (IVIM) imaging into meaningful clinical applications is sufficient scan-rescan reproducibility. This study aims to confirm the hypothesis that IVIM data fitting by not using b=0 images will improve the stability of liver IVIM measurement.

METHODS

Healthy volunteers' liver IVIM images were prospectively acquired using a 1.5-T magnet or a 3.0 T with 16 b-values. Repeatability study subjects were scanned twice during the same session, resulted in 35 paired scans for 35 subjects (11 men, mean age: 41.82 years, range: 32-60 years; 24 women, mean age: 42.67 years, range: 20-71 years). IVIM analysis was performed with full-fitting and segmented-fitting with a threshold b-value of 60 s/mm2, and fitting started from b=0 s/mm2 or from b=2 s/mm2. Reproducibility study subjects were scanned and then rescanned with an interval of 5-18 days, resulted in 20 paired scans for 11 subjects (4 men, mean age: 26.25 years, range: 25-27 years; 7 women, mean age: 25.57 years, range: 24-27 years). IVIM analysis was performed with segmented-fitting with a threshold b-value of 50 s/mm2, and fitting started from b=0 s/mm2 or from b=3 s/mm2.

RESULTS

Fitting without b=0 data generally improved the repeatability and reproducibility for both PF and Dslow, and particularly so for PF. For with b=0 data segmented fitting repeatability, PF had within-subject standard deviation of 0.019, bland-Atman 75% agreement limit of -31.52% to 28.35%, and ICC of 0.647, while these values were 0.009, -20.78% to 16.86%, and 0.837 for without b=0 analysis. Though the repeatability and reproducibility for Dfast generally also improved, they remained suboptimal. Measurement stability was better for repeatability than for reproducibility.

CONCLUSIONS

Scan-rescan repeatability and reproducibility of liver IVIM parameters can be improved by fitting without b=0 data, which is particularly so for PF.

Radiomics and Machine Learning Analysis Based on Magnetic Resonance Imaging in the Assessment of Colorectal Liver Metastases Growth Pattern

To assess Radiomics and Machine Learning Analysis in Liver Colon and Rectal Cancer Metastases (CRLM) Growth Pattern, we evaluated, retrospectively, a training set of 51 patients with 121 liver metastases and an external validation set of 30 patients with a single lesion. All patients were subjected to MRI studies in pre-surgical setting. For each segmented volume of interest (VOI), 851 radiomics features were extracted using PyRadiomics package. Nonparametric test, univariate, linear regression analysis and patter recognition approaches were performed. The best results to discriminate expansive versus infiltrative front of tumor growth with the highest accuracy and AUC at univariate analysis were obtained by the wavelet_LHH_glrlm_ShortRunLowGray Level Emphasis from portal phase of contrast study. With regard to linear regression model, this increased the performance obtained respect to the univariate analysis for each sequence except that for EOB-phase sequence. The best results were obtained by a linear regression model of 15 significant features extracted by the T2-W SPACE sequence. Furthermore, using pattern recognition approaches, the diagnostic performance to discriminate the expansive versus infiltrative front of tumor growth increased again and the best classifier was a weighted KNN trained with the 9 significant metrics extracted from the portal phase of contrast study, with an accuracy of 92% on training set and of 91% on validation set. In the present study, we have demonstrated as Radiomics and Machine Learning Analysis, based on EOB-MRI study, allow to identify several biomarkers that permit to recognise the different Growth Patterns in CRLM.

Magnetic Resonance Features of Liver Mucinous Colorectal Metastases: What the Radiologist Should Know

Purpose: The aim of this study is to assess MRI features of mucinous liver metastases compared to non-mucinous metastases and hepatic hemangioma. Methods: A radiological archive was assessed from January 2017 to June 2021 to select patients subjected to liver resection for CRCLM and MRI in the staging phase. We selected 20 patients with hepatic hemangioma (study group B). We evaluated (a) the maximum diameter of the lesions, in millimeters, on T1-W flash 2D in phase and out phase, on axial HASTE T2-W and on portal phase axial VIBE T1 W; and (b) the signal intensity (SI) in T1-W sequences, in T2-W sequences, Diffusion-Weighted Imaging (DWI) sequences and apparent diffusion coefficient (ADC) maps so as to observe (c) the presence and the type of contrast enhancement during the contrast study. The chi-square test was employed to analyze differences in percentage values of the categorical variable, while the non-parametric Kruskal−Wallis test was used to test for statistically significant differences between the median values of the continuous variables. A p-value < 0.05 was considered statistically significant. Results: The final study population included 52 patients (33 men and 19 women) with 63 years of median age (range 37−82 years) and 157 metastases. In 35 patients, we found 118 non-mucinous type metastases (control group), and in 17 patients, we found 39 mucinous type metastases (study group A). During follow-up, recurrence occurred in 12 patients, and three exhibited mucinous types among them. In the study group, all lesions (100%) showed hypointense SI on T1-W, very high SI (similar to hepatic hemangioma) in T2-W with restricted diffusion and iso-hypointense signals in the ADC map. During the contrast study, the main significant feature is the peripheral progressive enhancement.

Combined Hepatocellular-Cholangiocarcinoma: What the Multidisciplinary Team Should Know

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a rare type of primary liver malignancy. Among the risk factors, hepatitis B and hepatitis C virus infections, cirrhosis, and male gender are widely reported. The clinical appearance of cHCC-CCA is similar to that of HCC and iCCA and it is usually silent until advanced states, causing a delay of diagnosis. Diagnosis is mainly based on histology from biopsies or surgical specimens. Correct pre-surgical diagnosis during imaging studies is very problematic and is due to the heterogeneous characteristics of the lesion in imaging, with overlapping features of HCC and CCA. The predominant histological subtype within the lesion establishes the predominant imaging findings. Therefore, in this scenario, the radiological findings characteristic of HCC show an overlap with those of CCA. Since cHCC-CCAs are prevalent in patients at high risk of HCC and there is a risk that these may mimic HCC, it is currently difficult to see a non-invasive diagnosis of HCC. Surgery is the only curative treatment of HCC-CCA. The role of liver transplantation (LT) in the treatment of cHCC-CCA remains controversial, as is the role of ablative or systemic therapies in the treatment of this tumour. These lesions still remain challenging, both in diagnosis and in the treatment phase. Therefore, a pre-treatment imaging diagnosis is essential, as well as the identification of prognostic factors that could stratify the risk of recurrence and the most adequate therapy according to patient characteristics.

Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma

Background

This paper offers an assessment of diagnostic tools in the evaluation of Intrahepatic Cholangiocarcinoma (ICC).

Methods

Several electronic datasets were analysed to search papers on morphological and functional evaluation in ICC patients. Papers published in English language has been scheduled from January 2010 to December 2021.

Results

We found that 88 clinical studies satisfied our research criteria. Several functional parameters and morphological elements allow a truthful ICC diagnosis. The contrast medium evaluation, during the different phases of contrast studies, support the recognition of several distinctive features of ICC. The imaging tool to employed and the type of contrast medium in magnetic resonance imaging, extracellular or hepatobiliary, should change considering patient, departement, and regional features. Also, Radiomics is an emerging area in the evaluation of ICCs.

Post treatment studies are required to evaluate the efficacy and the safety of therapies so as the patient surveillance.

Conclusions

Several morphological and functional data obtained during Imaging studies allow a truthful ICC diagnosis.

Radiomics in medical imaging: pitfalls and challenges in clinical management

BACKGROUND

Radiomics and radiogenomics are two words that recur often in language of radiologists, nuclear doctors and medical physicists especially in oncology field. Radiomics is the technique of medical images analysis to extract quantitative data that are not detected by human eye.

METHODS

This article is a narrative review on Radiomics in Medical Imaging. In particular, the review exposes the process, the limitations related to radiomics, and future prospects are discussed.

RESULTS

Several studies showed that radiomics is very promising. However, there were some critical issues: poor standardization and generalization of radiomics results, data-quality control, repeatability, reproducibility, database balancing and issues related to model overfitting.

CONCLUSIONS

Radiomics procedure should made considered all pitfalls and challenges to obtain robust and reproducible results that could be generalized in other patients cohort.

Radiomics textural features by MR imaging to assess clinical outcomes following liver resection in colorectal liver metastases

PURPOSE

To assess the efficacy of radiomics features obtained by T2-weighted sequences to predict clinical outcomes following liver resection in colorectal liver metastases patients.

METHODS

This retrospective analysis was approved by the local Ethical Committee board and radiological databases were interrogated, from January 2018 to May 2021, to select patients with liver metastases with pathological proof and MRI study in pre-surgical setting. The cohort of patients included a training set and an external validation set. The internal training set included 51 patients with 61 years of median age and 121 liver metastases. The validation cohort consisted a total of 30 patients with single lesion with 60 years of median age. For each volume of interest, 851 radiomics features were extracted as median values using PyRadiomics. Nonparametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbours (KNN), artificial neural network (NNET) and decision tree (DT)) were considered.

RESULTS

The best predictor to discriminate expansive versus infiltrative front of tumour growth was obtained by wavelet_LHL_gldm_DependenceNonUniformityNormalized with an accuracy of 82%; to discriminate high grade versus low grade or absent was the wavelet_LLH_glcm_Imc1 with accuracy of 88%; to differentiate the mucinous type of tumour was the wavelet_LLH_glcm_JointEntropy with accuracy of 92% while to identify tumour recurrence was the wavelet_LLL_glcm_Correlation with accuracy of 85%. Linear regression model increased the performance obtained with respect to the univariate analysis exclusively in the discrimination of expansive versus infiltrative front of tumour growth reaching an accuracy of 90%, a sensitivity of 95% and a specificity of 80%. Considering significant texture metrics tested with pattern recognition approaches, the best performance was reached by the KNN in the discrimination of the tumour budding considering the four textural predictors obtaining an accuracy of 93%, a sensitivity of 81% and a specificity of 97%.

CONCLUSIONS

Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

CT-Based Radiomics Analysis to Predict Histopathological Outcomes Following Liver Resection in Colorectal Liver Metastases

Simple Summary

The objective of the study was to assess the radiomic features obtained by computed tomography (CT) examination as prognostic biomarkers in patients with colorectal liver metastases, in order to predict histopathological outcomes following liver resection. We obtained good performance considering the single significant textural metric in the identification of the front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type, and in the detection of recurrences.

Abstract

Purpose: We aimed to assess the efficacy of radiomic features extracted by computed tomography (CT) in predicting histopathological outcomes following liver resection in colorectal liver metastases patients, evaluating recurrence, mutational status, histopathological characteristics (mucinous), and surgical resection margin. Methods: This retrospectively approved study included a training set and an external validation set. The internal training set included 49 patients with a median age of 60 years and 119 liver colorectal metastases. The validation cohort consisted of 28 patients with single liver colorectal metastasis and a median age of 61 years. Radiomic features were extracted using PyRadiomics on CT portal phase. Nonparametric Kruskal–Wallis tests, intraclass correlation, receiver operating characteristic (ROC) analyses, linear regression modeling, and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The median value of intraclass correlation coefficients for the features was 0.92 (range 0.87–0.96). The best performance in discriminating expansive versus infiltrative front of tumor growth was wavelet_HHL_glcm_Imc2, with an accuracy of 79%, a sensitivity of 84%, and a specificity of 67%. The best performance in discriminating expansive versus tumor budding was wavelet_LLL_firstorder_Mean, with an accuracy of 86%, a sensitivity of 91%, and a specificity of 65%. The best performance in differentiating the mucinous type of tumor was original_firstorder_RobustMeanAbsoluteDeviation, with an accuracy of 88%, a sensitivity of 42%, and a specificity of 100%. The best performance in identifying tumor recurrence was the wavelet_HLH_glcm_Idmn, with an accuracy of 85%, a sensitivity of 81%, and a specificity of 88%. The best linear regression model was obtained with the identification of recurrence considering the linear combination of the 16 significant textural metrics (accuracy of 97%, sensitivity of 94%, and specificity of 98%). The best performance for each outcome was reached using KNN as a classifier with an accuracy greater than 86% in the training and validation sets for each classification problem; the best results were obtained with the identification of tumor front growth considering the seven significant textural features (accuracy of 97%, sensitivity of 90%, and specificity of 100%). Conclusions: This study confirmed the capacity of radiomics data to identify several prognostic features that may affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

An update on radiomics techniques in primary liver cancers

BACKGROUND

Radiomics is a progressing field of research that deals with the extraction of quantitative metrics from medical images. Radiomic features detention indirectly tissue features such as heterogeneity and shape and can, alone or in combination with demographic, histological, genomic, or proteomic data, be used for decision support system in clinical setting.

METHODS

This article is a narrative review on Radiomics in Primary Liver Cancers. Particularly, limitations and future perspectives are discussed.

RESULTS

In oncology, assessment of tissue heterogeneity is of particular interest: genomic analysis have demonstrated that the degree of tumour heterogeneity is a prognostic determinant of survival and an obstacle to cancer control. Therefore, that Radiomics could support cancer detection, diagnosis, evaluation of prognosis and response to treatment, so as could supervise disease status in hepatocellular carcinoma (HCC) and Intrahepatic Cholangiocarcinoma (ICC) patients. Radiomic analysis is a convenient radiological image analysis technique used to support clinical decisions as it is able to provide prognostic and / or predictive biomarkers that allow a fast, objective and repeatable tool for disease monitoring.

CONCLUSIONS

Although several studies have shown that this analysis is very promising, there is little standardization and generalization of the results, which limits the translation of this method into the clinical context. The limitations are mainly related to the evaluation of data quality, repeatability, reproducibility, overfitting of the model.

TRIAL REGISTRATION

Not applicable.

EOB-MR Based Radiomics Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases

Simple Summary

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. Ours results confirmed the capacity of radiomics to identify, as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach. These results were confirmed by external validation dataset. We obtained a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences.

Abstract

The aim of this study was to assess the efficacy of radiomics features obtained by EOB-MRI phase in order to predict clinical outcomes following liver resection in Colorectal Liver Metastases Patients, and evaluate recurrence, mutational status, pathological characteristic (mucinous) and surgical resection margin. This retrospective analysis was approved by the local Ethical Committee board of National Cancer of Naples, IRCCS “Fondazione Pascale”. Radiological databases were interrogated from January 2018 to May 2021 in order to select patients with liver metastases with pathological proof and EOB-MRI study in pre-surgical setting. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest by 2 expert radiologists, 851 radiomics features were extracted as median values using PyRadiomics. non-parametric test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. The best predictor to discriminate expansive versus infiltrative front of tumor growth was HLH_glcm_MaximumProbability extraxted on VIBE_FA30 with an accuracy of 84%, a sensitivity of 83%, and a specificity of 82%. The best predictor to discriminate tumor budding was Inverse Variance obtained by the original GLCM matrix extraxted on VIBE_FA30 with an accuracy of 89%, a sensitivity of 96% and a specificity of 65%. The best predictor to differentiate the mucinous type of tumor was the HHL_glszm_ZoneVariance extraxted on VIBE_FA30 with an accuracy of 85%, a sensitivity of 46% and a specificity of 95%. The best predictor to identify tumor recurrence was the LHL_glcm_Correlation extraxted on VIBE_FA30 with an accuracy of 86%, a sensitivity of 52% and a specificity of 97%. The best linear regression model was obtained in the identification of the tumor growth front considering the height textural significant metrics by VIBE_FA10 (an accuracy of 89%; sensitivity of 93% and a specificity of 82%). Considering significant texture metrics tested with pattern recognition approaches, the best performance for each outcome was reached by a KNN in the identification of recurrence with the 3 textural significant features extracted by VIBE_FA10 (AUC of 91%, an accuracy of 93%; sensitivity of 99% and a specificity of 77%). Ours results confirmed the capacity of radiomics to identify as biomarkers, several prognostic features that could affect the treatment choice in patients with liver metastases, in order to obtain a more personalized approach.

Contrast MR-Based Radiomics and Machine Learning Analysis to Assess Clinical Outcomes following Liver Resection in Colorectal Liver Metastases: A Preliminary Study

Simple Summary

The objective of the study was to evaluate the radiomics features obtained by contrast MRI studies as prognostic biomarkers in colorectal liver metastases patients to predict clinical outcomes following liver resection. We demonstrated a good performance considering the single textural significant metric in the identification of front of tumor growth (expansive versus infiltrative) and tumor budding (high grade versus low grade or absent), in the recognition of mucinous type and in the detection of recurrences. Moreover, considering linear regression models or neural network classifiers in a multivariate approach was possible to increase the performance in terms of accuracy, sensitivity, and specificity.

Abstract

Purpose: To assess radiomics features efficacy obtained by arterial and portal MRI phase in the prediction of clinical outcomes in the colorectal liver metastases patients, evaluating recurrence, mutational status, pathological characteristic (mucinous and tumor budding) and surgical resection margin. Methods: This retrospective analysis was approved by the local Ethical Committee board, and radiological databases were used to select patients with colorectal liver metastases with pathological proof and MRI study in a pre-surgical setting after neoadjuvant chemotherapy. The cohort of patients included a training set (51 patients with 61 years of median age and 121 liver metastases) and an external validation set (30 patients with single lesion with 60 years of median age). For each segmented volume of interest on MRI by two expert radiologists, 851 radiomics features were extracted as median values using the PyRadiomics tool. Non-parametric Kruskal-Wallis test, intraclass correlation, receiver operating characteristic (ROC) analysis, linear regression modelling and pattern recognition methods (support vector machine (SVM), k-nearest neighbors (KNN), artificial neural network (NNET), and decision tree (DT)) were considered. Results: The best predictor to discriminate expansive versus infiltrative tumor growth front was wavelet_LHH_glrlm_ShortRunLowGrayLevelEmphasis extracted on portal phase with accuracy of 82%, sensitivity of 84%, and specificity of 77%. The best predictor to discriminate tumor budding was wavelet_LLH_firstorder_10Percentile extracted on portal phase with accuracy of 92%, a sensitivity of 96%, and a specificity of 81%. The best predictor to differentiate the mucinous type of tumor was the wavelet_LLL_glcm_ClusterTendency extracted on portal phase with accuracy of 88%, a sensitivity of 38%, and a specificity of 100%. The best predictor to identify the recurrence was the wavelet_HLH_ngtdm_Complexity extracted on arterial phase with accuracy of 90%, a sensitivity of 71%, and a specificity of 95%. The best linear regression model was obtained in the identification of mucinous type considering the 13 textural significant metrics extracted by arterial phase (accuracy of 94%, sensitivity of 77% and a specificity of 99%). The best results were obtained in the identification of tumor budding with the eleven textural significant features extracted by arterial phase using a KNN (accuracy of 95%, sensitivity of 84%, and a specificity of 99%). Conclusions: Our results confirmed the capacity of radiomics to identify as biomarkers and several prognostic features that could affect the treatment choice in patients with liver metastases in order to obtain a more personalized approach.

Tumor Microenvironment Modifications Recorded With IVIM Perfusion Analysis and DCE-MRI After Neoadjuvant Radiotherapy: A Preclinical Study

PURPOSE

Neoadjuvant radiotherapy (NeoRT) improves tumor local control and facilitates tumor resection in many cancers. Some clinical studies demonstrated that both timing of surgery and RT schedule influence tumor dissemination, and subsequently patient overall survival. Previously, we developed a pre-clinical model demonstrating the impact of NeoRT schedule and timing of surgery on metastatic spreading. We report on the impact of NeoRT on tumor microenvironment by MRI.

METHODS

According to our NeoRT model, MDA-MB 231 cells were implanted in the flank of SCID mice. Tumors were locally irradiated (PXI X-Rad SmART) with 2x5Gy and then surgically removed at different time points after RT. Diffusion-weighted (DW) and Dynamic contrast enhancement (DCE) MRI images were acquired before RT and every 2 days between RT and surgery. IntraVoxel Incoherent Motion (IVIM) analysis was used to obtain information on intravascular diffusion, related to perfusion (F: perfusion factor) and subsequently tumor vessels perfusion. For DCE-MRI, we performed semi-quantitative analyses.

RESULTS

With this experimental model, a significant and transient increase of the perfusion factor F [50% of the basal value (n=16, p<0.005)] was observed on day 6 after irradiation as well as a significant increase of the WashinSlope with DCE-MRI at day 6 (n=13, p<0.05). Using immunohistochemistry, a significant increase of perfused vessels was highlighted, corresponding to the increase of perfusion in MRI at this same time point. Moreover, Tumor surgical resection during this peak of vascularization results in an increase of metastasis burden (n=10, p<0.05).

CONCLUSION

Significant differences in perfusion-related parameters (F and WashinSlope) were observed on day 6 in a neoadjuvant radiotherapy model using SCID mice. These modifications are correlated with an increase of perfused vessels in histological analysis and also with an increase of metastasis spreading after the surgical procedure. This experimental observation could potentially result in a way to personalize treatment, by modulating the time of surgery guided on MRI functional data, especially tumor perfusion.

Effects of intraoperative dexmedetomidine infusion on renal function in elective living donor kidney transplantation: a randomized controlled trial

PURPOSE

Ischemia-reperfusion injury is inevitable during donor organ harvest and recipient allograft reperfusion in kidney transplantation, and affects graft outcomes. Dexmedetomidine, an α2-adrenoreceptor agonist, has renoprotective effects against ischemia-reperfusion injury. We investigated the effects of intraoperative dexmedetomidine infusion on renal function and the development of delayed graft function after elective living donor kidney transplantation in a randomized controlled trial.

METHODS

A total of 104 patients were randomly assigned to receive either an intraoperative infusion of dexmedetomidine 0.4 μg·kg^-1·hr^-1 or 0.9% saline. The primary outcome was the serum creatinine level on postoperative day (POD) 7. Secondary outcomes were renal function and the degree of inflammation and included the following variables: serum creatinine level and estimated glomerular filtration rate up to six months; incidence of delayed graft function; and levels of serum cystatin C, plasma interleukin (IL)-1β, and IL-18 during the perioperative period.

RESULTS

The mean (standard deviation) serum creatinine level on POD 7 was comparable between the groups (dexmedetomidine vs control: 1.11 [0.87] mg·dL^-1 vs 1.06 [0.73] mg·dL^-1; mean difference, 0.05; 95% confidence interval, -0.27 to 0.36; P = 0.77). Delayed graft function occurred in one patient in each group (odds ratio, 1.020; P > 0.99). There were no significant differences in the secondary outcomes between the groups (all P > 0.05).

CONCLUSIONS

Intraoperative dexmedetomidine infusion did not produce any beneficial effects on renal function or delayed graft function in patients undergoing elective living donor kidney transplantation.

STUDY REGISTRATION

www.ClinicalTrials.gov (NCT03327389); registered 31 October 2017.

DCE-MRI is more sensitive than IVIM-DWI for assessing anti-angiogenic treatment-induced changes in colorectal liver metastases

BACKGROUND

Diffusion weighted imaging (DWI) with intravoxel incoherent motion (IVIM) modelling can inform on tissue perfusion without exogenous contrast administration. Dynamic-contrast-enhanced (DCE) MRI can also characterise tissue perfusion, but requires a bolus injection of a Gadolinium-based contrast agent. This study compares the use of DCE-MRI and IVIM-DWI methods in assessing response to anti-angiogenic treatment in patients with colorectal liver metastases in a cohort with confirmed treatment response.

METHODS

This prospective imaging study enrolled 25 participants with colorectal liver metastases to receive Regorafenib treatment. A target metastasis > 2 cm in each patient was imaged before and at 15 days after treatment on a 1.5T MR scanner using slice-matched IVIM-DWI and DCE-MRI protocols. MRI data were motion-corrected and tumour volumes of interest drawn on b=900 s/mm2 diffusion-weighted images were transferred to DCE-MRI data for further analysis. The median value of four IVIM-DWI parameters [diffusion coefficient D (10-3 mm2/s), perfusion fraction f (ml/ml), pseudodiffusion coefficient D* (10-3 mm2/s), and their product fD* (mm2/s)] and three DCE-MRI parameters [volume transfer constant Ktrans (min-1), enhancement fraction EF (%), and their product KEF (min-1)] were recorded at each visit, before and after treatment. Changes in pre- and post-treatment measurements of all MR parameters were assessed using Wilcoxon signed-rank tests (P<0.05 was considered significant). DCE-MRI and IVIM-DWI parameter correlations were evaluated with Spearman rank tests. Functional MR parameters were also compared against Response Evaluation Criteria In Solid Tumours v.1.1 (RECIST) evaluations.

RESULTS

Significant treatment-induced reductions of DCE-MRI parameters across the cohort were observed for EF (91.2 to 50.8%, P<0.001), KEF (0.095 to 0.045 min-1, P<0.001) and Ktrans (0.109 to 0.078 min-1, P=0.002). For IVIM-DWI, only D (a non-perfusion parameter) increased significantly post treatment (0.83 to 0.97 × 10-3 mm2/s, P<0.001), while perfusion-related parameters showed no change. No strong correlations were found between DCE-MRI and IVIM-DWI parameters. A moderate correlation was found, after treatment, between Ktrans and D* (r=0.60; P=0.002) and fD* (r=0.67; P<0.001). When compared to RECIST v.1.1 evaluations, KEF and D correctly identified most clinical responders, whilst non-responders were incorrectly identified.

CONCLUSION

IVIM-DWI perfusion-related parameters showed limited sensitivity to the anti-angiogenic effects of Regorafenib treatment in colorectal liver metastases and showed low correlation with DCE-MRI parameters, despite profound and significant post-treatment reductions in DCE-MRI measurements.

TRIAL REGISTRATION

NCT03010722 clinicaltrials.gov; registration date 6th January 2015.

Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Predicting and Monitoring the Response of Anti-Angiogenic Treatment in the Orthotopic Nude Mouse Model of Lung Adenocarcinoma

BACKGROUND

The treatment efficacy of angiogenesis inhibitor could be underestimated at an early stage based on tumor volume changes. Intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) can quantitatively assess tumors at the cellular level, but it is unclear whether it can provide useful information for assessing treatment response of anti-angiogenic treatment for lung adenocarcinoma.

PURPOSE

To determine the use of IVIM-DWI for non-invasive monitoring of the early response to anti-angiogenic treatment in the orthotopic transplantation of lung adenocarcinoma model.

STUDY TYPE

Prospective.

POPULATION

Thirty-seven nude mice were randomized into two groups: treatment group (received bevacizumab + cisplatin, N = 20) and control group (received saline, N = 17).

FIELD STRENGTH/SEQUENCE

Single-shot turbo spin-echo (TSE) IVIM-DWI, TSE T2-weighted imaging at 3.0 T.

ASSESSMENT

Tumor volume, IVIM parameters (apparent diffusion coefficient [ADC], diffusivity [D], perfusion fraction [f], and pseudo-diffusion coefficient [D*]) were measured before and 2 hours, 3, 7, 10 and 14 days after treatment. Regions of interest were manually drawn along the inner edge of the tumor by two radiologists with 5 and 10-year experience in magnetic resonance imaging. Pathological examinations (hematoxylin and eosin stain, cluster of differentiation 34) were performed.

STATISTICAL TESTS

Kolmogorov-Smirnov test, repeated-measure two-way analysis of variance test, Mann-Whitney U test, Pearson correlation analysis, receiver operating characteristic curve. P < 0.05 was considered statistically significant.

RESULTS

The tumor volume of the two groups was significantly different only on day 14 (control group vs. treatment group, 43.15 ± 18.28 mm³ vs. 28.41 ± 1.71 mm³ ). ADC_2h , ADC_10d , D_2h , D_7d , D_10d , and D_14d were significantly higher, while f_10d and f_14d were significantly lower in the treatment group compared to those of the control group. Both the △ADC_2h (r = -0.631) and △D_2h (r = -0.700) showed moderate correlations with the relative tumor volume on day 14.

DATA CONCLUSION

IVIM has the potential to predict and monitor the early response to anti-angiogenic treatment, earlier than size changes, for lung adenocarcinoma.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 4.

Quantitative diffusion MRI of the abdomen and pelvis

Diffusion MRI has enormous potential and utility in the evaluation of various abdominal and pelvic disease processes including cancer and noncancer imaging of the liver, prostate, and other organs. Quantitative diffusion MRI is based on acquisitions with multiple diffusion encodings followed by quantitative mapping of diffusion parameters that are sensitive to tissue microstructure. Compared to qualitative diffusion-weighted MRI, quantitative diffusion MRI can improve standardization of tissue characterization as needed for disease detection, staging, and treatment monitoring. However, similar to many other quantitative MRI methods, diffusion MRI faces multiple challenges including acquisition artifacts, signal modeling limitations, and biological variability. In abdominal and pelvic diffusion MRI, technical acquisition challenges include physiologic motion (respiratory, peristaltic, and pulsatile), image distortions, and low signal-to-noise ratio. If unaddressed, these challenges lead to poor technical performance (bias and precision) and clinical outcomes of quantitative diffusion MRI. Emerging and novel technical developments seek to address these challenges and may enable reliable quantitative diffusion MRI of the abdomen and pelvis. Through systematic validation in phantoms, volunteers, and patients, including multicenter studies to assess reproducibility, these emerging techniques may finally demonstrate the potential of quantitative diffusion MRI for abdominal and pelvic imaging applications.

Treatment response of colorectal cancer liver metastases to neoadjuvant or conversion therapy: a prospective multicentre follow-up study using MRI, diffusion-weighted imaging and 1H-MR spectroscopy compared with histology (subgroup in the RAXO trial)

BACKGROUND

Colorectal cancer liver metastases respond to chemotherapy and targeted agents not only by shrinking, but also by morphologic and metabolic changes. The aim of this study was to evaluate the value of advanced magnetic resonance imaging (MRI) methods in predicting treatment response and survival.

PATIENTS AND METHODS

We investigated contrast-enhanced MRI, apparent diffusion coefficient (ADC) in diffusion-weighted imaging and 1H-magnetic resonance spectroscopy (1H-MRS) in detecting early morphologic and metabolic changes in borderline or resectable liver metastases, as a response to first-line neoadjuvant or conversion therapy in a prospective substudy of the RAXO trial (NCT01531621, EudraCT2011-003158-24). MRI findings were compared with histology of resected liver metastases and Kaplan-Meier estimates of overall survival (OS).

RESULTS

In 2012-2018, 52 patients at four Finnish university hospitals were recruited. Forty-seven patients received neoadjuvant or conversion chemotherapy and 40 liver resections were carried out. Low ADC values (below median) of the representative liver metastases, at baseline and after systemic therapy, were associated with partial response according to RECIST criteria, but not with morphologic MRI changes or histology. Decreasing ADC values following systemic therapy were associated with improved OS compared to unchanged or increasing ADC, both in the liver resected subgroup (5-year OS rate 100% and 34%, respectively, P = 0.022) and systemic therapy subgroup (5-year OS rate 62% and 23%, P = 0.049). 1H-MRS revealed steatohepatosis induced by systemic therapy.

CONCLUSIONS

Low ADC values at baseline or during systemic therapy were associated with treatment response by RECIST but not with histology, morphologic or detectable metabolic changes. A decreasing ADC during systemic therapy is associated with improved OS both in all patients receiving systemic therapy and in the resected subgroup.

Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma

This article provides an overview of diagnostic evaluation and ablation treatment assessment in Hepatocellular Carcinoma (HCC). Only studies, in the English language from January 2010 to January 202, evaluating the diagnostic tools and assessment of ablative therapies in HCC patients were included. We found 173 clinical studies that satisfied the inclusion criteria.HCC may be noninvasively diagnosed by imaging findings. Multiphase contrast-enhanced imaging is necessary to assess HCC. Intravenous extracellular contrast agents are used for CT, while the agents used for MRI may be extracellular or hepatobiliary. Both gadoxetate disodium and gadobenate dimeglumine may be used in hepatobiliary phase imaging. For treatment-naive patients undergoing CT, unenhanced imaging is optional; however, it is required in the post treatment setting for CT and all MRI studies. Late arterial phase is strongly preferred over early arterial phase. The choice of modality (CT, US/CEUS or MRI) and MRI contrast agent (extracelllar or hepatobiliary) depends on patient, institutional, and regional factors. MRI allows to link morfological and functional data in the HCC evaluation. Also, Radiomics is an emerging field in the assessment of HCC patients.Postablation imaging is necessary to assess the treatment results, to monitor evolution of the ablated tissue over time, and to evaluate for complications. Post- thermal treatments, imaging should be performed at regularly scheduled intervals to assess treatment response and to evaluate for new lesions and potential complications.

Radiological Imaging of Gastro-Entero-Pancreatic Neuroendocrine Tumors. The Review of Current Literature Emphasizing the Diagnostic Value of Chosen Imaging Methods

Despite development of radiologic imaging, detection and follow-up of neuroendocrine neoplasms (NENs) still pose a diagnostic challenge, due to the heterogeneity of NEN, their relatively long-term growth, and small size of primary tumor. A set of information obtained by using different radiological imaging tools simplifies a choice of the most appropriate treatment method. Moreover, radiological imaging plays an important role in the assessment of metastatic lesions, especially in the liver, as well as, tumor response to treatment. This article reviews the current, broadly in use imaging modalities which are applied to the diagnosis of GEP-NETs, (the most common type of NENs) and put emphasis on the strengths and limitations of each modality.

Radiological assessment of secondary biliary tree lesions: an update

Objective

To conduct a systematic literature review of imaging techniques and findings in patients with peribiliary liver metastasis.

Methods

Several electronic datasets were searched from January 1990 to June 2017 to identify studies assessing the use of different imaging techniques for the detection and staging of peribiliary metastases.

Results

The search identified 44 studies, of which six met the inclusion criteria and were included in the systematic review. Multidetector computed tomography (MDCT) is the technique of choice in the preoperative setting and during the follow-up of patients with liver tumors. However, the diagnostic performance of MDCT for the assessment of biliary tree neoplasms was low compared with magnetic resonance imaging (MRI). Ultrasound (US), without and with contrast enhancement (CEUS), is commonly employed as a first-line tool for evaluating focal liver lesions; however, the sensitivity and specificity of US and CEUS for both the detection and characterization are related to operator expertise and patient suitability. MRI has thus become the gold standard technique because of its ability to provide morphologic and functional data. MRI showed the best diagnostic performance for the detection of peribiliary metastases.

Conclusions

MRI should be considered the gold standard technique for the radiological assessment of secondary biliary tree lesions.

Diffusion-weighted MRI of the liver: challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion

Diffusion-weighted imaging (DWI) is sensitive to the mobility of water molecule at cellular and macromolecular level, much smaller than the spatial resolution of the images. It is commonly based on single shot echo-planar imaging sequence with the addition of motion-probing gradient pulses and fat suppression. DWI is increasingly incorporated into routine body magnetic resonance imaging protocols. However, the liver is particularly affected by physiological motions such as respiration; the left liver is also affected by cardiac motion artifacts and susceptibility artefact due to contents in the stomach. Intravoxel incoherent motion (IVIM) DWI data analysis requires high-quality data acquisition using multiple b-values and confidence in the measurements at low b-values. This article reviews the technical developments of DWI and its applications in the liver. Challenges and some solutions for the quantification of apparent diffusion coefficient and intravoxel incoherent motion are discussed. Currently, acquisition protocols vary between research groups; patient preparation and data post-processing are not standardized. Increased standardization, both in data acquisition and in image analysis, is imperative so to allow generation of reliable DW-MRI biomarker measures that are broadly applicable.

Colorectal liver metastases: ADC as an imaging biomarker of tumor behavior and therapeutic response

PURPOSE

To correlate the ADC values of colorectal liver metastases, evaluated before (preADC) and after (postADC) neoadjuvant chemotherapy (ChT), as well as their difference (ΔADC), with the histological tumor regression grade (TRG) and to determine whether the preADC value can be predictive of the lesion ChT response.

METHOD

Twenty-four patients with colorectal liver metastases, who had undergone 3 T-MRI before and after ChT and were subsequently treated by parenchymal-spearing surgery, were retrospectively included. Diffusion-weighted MRI (DW-MRI) was performed using a spin-echo echo-planar sequence with multiple b values, obtaining an ADC map. Fitted ADC values were calculated for each lesion before and after ChT. The maximum diameter of each lesion in both examinations was recorded. Diameter variations and RECIST1.1 criteria were assessed. All MRI findings were histopathologically correlated to TRG of resected liver metastases. Statistical analysis was performed on a per-lesion basis.

RESULTS

A total of 58 colorectal liver metastases were analysed; after ChT, 8 out of 58 lesions disappeared. TRG1, TRG2, TRG3, TRG4 and TRG5 were observed in 6, 12, 12, 13 and 7 lesions, respectively. The preADC values showed a different distribution according to the TRG scores (p = 0.0027), even though the distribution was not linear. The postADC and ΔADC values were significant different based on the TRG system (both p < 0.0001). A significant correlation between the lesion TRG and the evaluation according to RECIST1.1 criteria was observed by a per-lesion analysis (p = 0.0009).

CONCLUSIONS

PostADC and ΔADC could be proposed as reliable biomarkers to assess tumor treatment response after preoperative ChT in patients with colorectal liver metastases.

Radiomics-Derived Data by Contrast Enhanced Magnetic Resonance in RAS Mutations Detection in Colorectal Liver Metastases

Simple Summary

In the present study, we assessed the association of RAS mutation status and radiomics derived data by Contrast Enhanced Magnetic Resonance Imaging (CE-MRI) in liver metastases by CRC. We performed the evaluation extracting by CE-MRI both texture and morphological metrics in a 3D setting. We demonstrated that radiomics with texture parameters could add value to qualitative assessment of MR studies and with better results compared to morphological metrics, providing individualized evaluation of CRLM. Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.

Abstract

Purpose: To assess the association of RAS mutation status and radiomics-derived data by Contrast Enhanced-Magnetic Resonance Imaging (CE-MRI) in liver metastases. Materials and Methods: 76 patients (36 women and 40 men; 59 years of mean age and 36–80 years as range) were included in this retrospective study. Texture metrics and parameters based on lesion morphology were calculated. Per-patient univariate and multivariate analysis were made. Wilcoxon-Mann-Whitney U test, receiver operating characteristic (ROC) analysis, pattern recognition approaches with features selection approaches were considered. Results: Significant results were obtained for texture features while morphological parameters had not significant results to classify RAS mutation. The results showed that using a univariate analysis was not possible to discriminate accurately the RAS mutation status. Instead, considering a multivariate analysis and classification approaches, a KNN exclusively with texture parameters as predictors reached the best results (AUC of 0.84 and an accuracy of 76.9% with 90.0% of sensitivity and 67.8% of specificity on training set and an accuracy of 87.5% with 91.7% of sensitivity and 83.3% of specificity on external validation cohort). Conclusions: Texture parameters derived by CE-MRI and combined using multivariate analysis and patter recognition approaches could allow stratifying the patients according to RAS mutation status.

Intravoxel incoherent motion as a tool to detect early microstructural changes in meningiomas treated with proton therapy

PURPOSE

To assess early microstructural changes of meningiomas treated with proton therapy through quantitative analysis of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) parameters.

METHODS

Seventeen subjects with meningiomas that were eligible for proton therapy treatment were retrospectively enrolled. Each subject underwent a magnetic resonance imaging (MRI) including DWI sequences and IVIM assessments at baseline, immediately before the 1st (t0), 10th (t10), 20th (t20), and 30th (t30) treatment fraction and at follow-up. Manual tumor contours were drawn on T2-weighted images by two expert neuroradiologists and then rigidly registered to DWI images. Median values of the apparent diffusion coefficient (ADC), true diffusion (D), pseudo-diffusion (D*), and perfusion fraction (f) were extracted at all timepoints. Statistical analysis was performed using the pairwise Wilcoxon test.

RESULTS

Statistically significant differences from baseline to follow-up were found for ADC, D, and D* values, with a progressive increase in ADC and D in conjunction with a progressive decrease in D*. MRI during treatment showed statistically significant differences in D values between t0 and t20 (p = 0.03) and t0 and t30 (p = 0.02), and for ADC values between t0 and t20 (p = 0.04), t10 and t20 (p = 0.02), and t10 and t30 (p = 0.035). Subjects that showed a volume reduction greater than 15% of the baseline tumor size at follow-up showed early D changes, whereas ADC changes were not statistically significant.

CONCLUSION

IVIM appears to be a useful tool for detecting early microstructural changes within meningiomas treated with proton therapy and may potentially be able to predict tumor response.

Abbreviated MRI protocol for colorectal liver metastases: How the radiologist could work in pre surgical setting

Background

MRI is the most reliable imaging modality that allows to assess liver metastases. Our purpose is to compare the per-lesion and per-patient detection rate of gadoxetic acid-(Gd-EOB) enhanced liver MRI and fast MR protocol including Diffusion Weighted Imaging (DWI) and T2-W Fat Suppression sequence in the detection of liver metastasis in pre surgical setting.

Methods

One hundred and eight patients with pathologically proven liver metastases (756 liver metastases) underwent Gd-EOBMRI were enrolled in this study. Three radiologist independently graded the presence of liver lesions on a five-point confidence scale assessed only abbreviated protocol (DWI and sampling perfection with application-optimized contrasts using different flip angle evolution (SPACE) fat suppressed sequence) and after an interval of more than 2 weeks the conventional study (all acquired sequences). Per-lesion and per-patient detection rate of metastases were calculated. Weighted к values were used to evaluate inter-reader agreement of the confidence scale regarding the presence of the lesion.

Results

MRI detected 732 liver metastases. All lesions were identified both by conventional study as by abbreviated protocol. In terms of per-lesion detection rate of liver metastasis, all three readers had higher detection rate both with abbreviated protocol and with standard protocol with Gd-EOB (96.8% [732 of 756] vs. 96.5% [730 of 756] for reader 1; 95.8% [725 of 756] vs. 95.2% [720 of 756] for reader 2; 96.5% [730 of 756] vs. 96.5% [730 of 756] for reader 3). Inter-reader agreement of lesions detection rate between the three radiologists was excellent (k range, 0.86–0.98) both for Gd-EOB MRI and for Fast protocol (k range, 0.89–0.99).

Conclusion

Abbreviated protocol showed the same detection rate than conventional study in detection of liver metastases.

Necrosis volume and Choi criteria predict the response to endoscopic ultrasonography-guided HybridTherm ablation of locally advanced pancreatic cancer

Background and study aims  Endoscopic ultrasound (EUS)-guided ablation of pancreatic ductal adenocarcinoma (PDAC) with HybridTherm-Probe (EUS-HTP) is feasible and safe, but the radiological response and ideal tool to measure it have not been investigated yet. The aims of this study were to: 1) assess the radiological response to EUS-HTP evaluating the vital tumor volume reduction rate, Response Evaluation Criteria in Solid Tumors (RECIST1.1) and Choi criteria; 2) determine the prognostic predictive yield of these criteria. Patients and methods  A retrospective analysis was performed of patients with locally advanced PDAC after primary treatment or unfit for chemotherapy prospectively treated by EUS-HTP. Computed tomography scan was performed 1 month after EUS-HTP to evaluate: 1) vital tumor volume reduction rate (VTVRR) by measuring necrosis and tumor volumes through a computer-aided detection system; and 2) RECIST1.1 and Choi criteria. Results  EUS-HTP was feasible in 22 of 31 patients (71 %), with no severe adverse events. Median post-HTP survival was 7 months (1 - 35). Compared to pre-HTP tumor volume, a significant 1-month VTVRR (mean 21.4 %) was observed after EUS-HTP ( P  = 0.005). We identified through ROC analysis a VTVRR > 11.46 % as the best cut-off to determine post-HTP 6-month survival outcome (AUC = 0.733; sensitivity = 70.0 %, specificity = 83.3 %). This cut-off was significantly associated with longer overall survival (HR = 0.372; P  = 0.039). According to RECIST1.1 and Choi criteria, good responders to EUS-HTP were 60 % and 46.7 %, respectively. Good responders according to Choi, but not to RECIST1.1, had longer survival (HR = 0.407; P  = 0.04). Conclusions  EUS-HTP induces a significant 1-month VTVRR. This effect is assessed accurately by evaluation of necrosis and tumor volumes. Use of VTVRR and Choi criteria, but not RECIST 1.1 criteria, might identify patients who could benefit clinically from EUS-HTP.

Diffusion-Weighted MRI and Diffusion Kurtosis Imaging to Detect RAS Mutation in Colorectal Liver Metastasis

Simple Summary

Imaging derived parameters can provide data on tumor phenotype as well as cancer microenvironment. Radiomics has recently shown potential in realizing personalized medicine. The aim of the manuscript is to detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. We demonstrated that DKI derived parameters allows to detect RAS mutation in liver metastasis.

Abstract

Objectives: To detect RAS mutation in colorectal liver metastasis by Diffusion-Weighted Magnetic Resonance Imaging (DWI-MRI) - and Diffusion Kurtosis imaging (DKI)-derived parameters. Methods: In total, 106 liver metastasis (60 metastases with RAS mutation) in 52 patients were included in this retrospective study. Diffusion and perfusion parameters were derived by DWI (apparent diffusion coefficient (ADC), basal signal (S0), pseudo-diffusion coefficient (DP), perfusion fraction (FP) and tissue diffusivity (DT)) and DKI data (mean of diffusion coefficient (MD) and mean of diffusional Kurtosis (MK)). Wilcoxon–Mann–Whitney U tests for non-parametric variables and receiver operating characteristic (ROC) analyses were calculated with area under ROC curve (AUC). Moreover, pattern recognition approaches (linear classifier, support vector machine, k-nearest neighbours, decision tree), with features selection methods and a leave-one-out cross validation approach, were considered. Results: A significant discrimination between the group with RAS mutation and the group without RAS mutation was obtained by the standard deviation value of MK (MK STD), by the mean value of MD, and by that of FP. The best results were reached by MK STD with an AUC of 0.80 (sensitivity of 72%, specificity of 85%, accuracy of 79%) using a cut-off of 203.90 × 10⁻³, and by the mean value of MD with AUC of 0.80 (sensitivity of 84%, specificity of 73%, accuracy of 77%) using a cut-off of 1694.30 mm²/s × 10⁻⁶. Considering all extracted features or the predictors obtained by the features selection method (the mean value of S0, the standard deviation value of MK, FP and of DT), the tested pattern recognition approaches did not determine an increase in diagnostic accuracy to detect RAS mutation (AUC of 0.73 and 0.69, respectively). Conclusions: Diffusion-Weighted imaging and Diffusion Kurtosis imaging could be used to detect the RAS mutation in liver metastasis. The standard deviation value of MK and the mean value of MD were the more accurate parameters in the RAS mutation detection, with an AUC of 0.80.

Comparison of intravoxel incoherent motion imaging, diffusion kurtosis imaging, and conventional DWI in predicting the chemotherapeutic response of colorectal liver metastases

PURPOSE

To assess the usefulness and performance of intravoxel incoherent motion imaging (IVIM) with diffusion kurtosis imaging (DKI) and conventional DWI for predicting the chemotherapeutic response of colorectal liver metastases (CRLMs).

METHOD

A prospective study was conducted. Up to February 2018, forty consecutive patients treated with the standard first-line chemotherapy regimens were enrolled. MRI was performed within 1 week before chemotherapy, as well as 2-3 weeks and 6-8 weeks after chemotherapy. The apparent diffusion coefficient map, IVIM and DKI parameter maps were calculated using a prototype postprocessing software. The response was assessed by the Response Evaluation Criteria in Solid Tumors. The parameters were compared between the responding group (complete and partial response) and the nonresponding group (stable and progressive disease).

RESULTS

A total of 15 responding and 25 nonresponding patients were evaluated. Low baseline ADC, D_slow, and D values (P =  0.001, <0.001, and =0.003, respectively) and a high baseline K value (P =  0.002) were independently associated with a good response to chemotherapy. The combination of all the significant parameters yielded an AUC of 0.867. After treatment, the ADC, D_slow, and D values all showed an upward trend, while the K value showed a decreasing trend, but there were no significant differences (P >  0.05).

CONCLUSION

The study showed that the pretreatment IVIM (D_slow), DKI (D and K), and conventional DWI (ADC) parameters all demonstrated a good diagnostic performance in predicting the chemotherapeutic response of CRLMs.

Magnetic resonance imaging in the assessment of pancreatic cancer with quantitative parameter extraction by means of dynamic contrast-enhanced magnetic resonance imaging, diffusion kurtosis imaging and intravoxel incoherent motion diffusion-weighted imaging

BACKGROUND

Despite great technical advances in imaging, such as multidetector computed tomography and magnetic resonance imaging (MRI), diagnosing pancreatic solid lesions correctly remains challenging, due to overlapping imaging features with benign lesions. We wanted to evaluate functional MRI to differentiate pancreatic tumors, peritumoral inflammatory tissue, and normal pancreatic parenchyma by means of dynamic contrast-enhanced MRI (DCE-MRI)-, diffusion kurtosis imaging (DKI)-, and intravoxel incoherent motion model (IVIM) diffusion-weighted imaging (DWI)-derived parameters.

METHODS

We retrospectively analyzed 24 patients, each with histopathological diagnosis of pancreatic tumor, and 24 patients without pancreatic lesions. Functional MRI was acquired using a 1.5 MR scanner. Peritumoral inflammatory tissue was assessed by drawing regions of interest on the tumor contours. DCE-MRI, IVIM and DKI parameters were extracted. Nonparametric tests and receiver operating characteristic (ROC) curves were calculated.

RESULTS

There were statistically significant differences in median values among the three groups observed by Kruskal-Wallis test for the DKI mean diffusivity (MD), IVIM perfusion fraction (fp) and IVIM tissue pure diffusivity (Dt). MD had the best results to discriminate normal pancreas plus peritumoral inflammatory tissue versus pancreatic tumor, to separate normal pancreatic parenchyma versus pancreatic tumor and to differentiate peritumoral inflammatory tissue versus pancreatic tumor, respectively, with an accuracy of 84%, 78%, 83% and area under ROC curve (AUC) of 0.85, 0.82, 0.89. The findings were statistically significant compared with those of other parameters (p value < 0.05 using McNemar's test). Instead, to discriminate normal pancreas versus peritumoral inflammatory tissue or pancreatic tumor and to differentiate normal pancreatic parenchyma versus peritumoral inflammatory tissue, there were no statistically significant differences between parameters' accuracy (p > 0.05 at McNemar's test).

CONCLUSIONS

Diffusion parameters, mainly MD by DKI, could be helpful for the differentiation of normal pancreatic parenchyma, perilesional inflammation, and pancreatic tumor.

Optimization of intravoxel incoherent motion (IVIM): variability of parameters measurements using a reduced distribution of b values for breast tumors analysis

OBJECTIVES

This study aimed to examine the variability of intravoxel incoherent motion measurements acquired from reduced distributions of b values for breast tumors analysis.

MATERIALS AND METHODS

The investigations were carried out on twenty-four patients with diagnosed breast tumors. A conventional unenhanced MRI and various IVIM series preset with different distributions of b values (0-1000 s/mm²) were performed. We assessed the variability in D_slow, D_fast, and PF measurements for different distributions of 9 to 4 b values compared with the IVIM metrics for 10 b values using Wilcoxon-Signed rank test. The data was statistically significant at P < 0.05.

RESULTS

The results showed no significant variation in the estimations of IVIM parameters in patients. However, the measurements acquired with the combination of 5 b values Showed some variation in D_fast (P = 0.028) compared with 10 b values. The data showed high wCVs in the measurements acquired using the reduced set of 6 b values for D_slow and PF and with the combination of 7 b values for D_fast. There were inconsistencies noticed in the measurements acquired from malignant tumors using reduced distributions of b values (9 b values-4 b values). However, the set of 4 b values displayed the lowest wCVs for both benign and malignant datasets. We also observed unsystematic correlations among different combinations of b values in the categories of IVIM parameters.

CONCLUSION

There was no relevant variation in the parameters measurements irrespective of the number of b values used. Reduced distributions of b values may find use in estimations of IVIM parameters for breast lesions analysis.

Intravoxel Incoherent Motion of Colon Cancer Liver Metastases for the Assessment of Response to Antiangiogenic Treatment: Results from a Pilot Study

OBJECTIVE

This study was aimed at evaluating the intravoxel incoherent motion (IVIM) parameter alterations of liver metastases of colorectal carcinoma (CRC) during antiangiogenic bevacizumab combination therapy.

METHODS

Twenty-five patients with CRC liver metastases treated with bevacizumab in combination with FOLFOX-or-FOLFIRI protocols were enrolled in the study. MRI was performed using a 1.5-tesla scanner pre-treatment (PT) and at 3, 6, and 9 months of therapy. Routine abdominal MRI sequences and an IVIM-DWI (diffusion-weighted imaging) sequence were obtained. The IVIM-DWI sequence was executed with 16 b-values varying from 0 to 1,400 s/mm2. The mean values of apparent diffusion coefficient (ADC), true diffusion (D), pseudodiffusion (D*), and perfusion fraction (f) of each metastasis were obtained for all b-values, and the time-related changes were recorded to analyze the chronologic responses to antiangiogenic therapy. The RECIST 1.1 criteria were used for the evaluation of treatment response.

RESULTS

The diameters of the metastases diminished significantly at 9 months when compared with PT (p = 0.03). The D (p = 0.10) and ADC (p = 0.21) values of the metastases increased at 9 months of therapy. D* was the highest at 3 months (p =0.24); it decreased at 6 (p =0.97) and 9 months (p =0.87) of therapy. The f value had peaked at 3 months (p =0.51) and started to decrease thereafter. At 6 months, f decreased to the lowest values (p =0.12).

CONCLUSION

IVIM parameters, particularly the perfusion fraction, may quantitatively reflect the response to antiangiogenic treatment. The antiangiogenic response manifests after 3 months of therapy before the RECIST-related response.

Diffusion and perfusion MR parameters to assess preoperative short-course radiotherapy response in locally advanced rectal cancer: a comparative explorative study among Standardized Index of Shape by DCE-MRI, intravoxel incoherent motion- and diffusion kurtosis imaging-derived parameters

PURPOSE

To assess preoperative short-course radiotherapy (SCR) tumor response in locally advanced rectal cancer (LARC) by means of Standardized Index of Shape (SIS) by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters derived from diffusion-weighted MRI (DW-MRI).

MATERIALS AND METHODS

Thirty-four patients with LARC who underwent MRI scans before and after SCR followed by delayed surgery, retrospectively, were enrolled. SIS, ADC, IVIM parameters [tissue diffusion (D_t), pseudo-diffusion (D_p), perfusion fraction (f_p)] and DKI parameters [mean diffusivity (MD), mean of diffusional kurtosis (MK)] were calculated for each patient. IVIM parameters were estimated using two methods, namely conventional biexponential fitting (CBFM) and variable projection (VARPRO). After surgery, the pathological TNM and tumor regression grade (TRG) were estimated. For each parameter, percentage changes between before and after SCR were evaluated. Furthermore, an artificial neural network was trained for outcome prediction. Nonparametric sample tests and receiver operating characteristic curve (ROC) analysis were performed.

RESULTS

Fifteen patients were classified as responders (TRG ≤ 2) and 19 as not responders (TRG > 3). Seven patients had TRG 1 (pathological complete response, pCR). Mean and standard deviation values of pre-treatment CBFM D_p and mean value of VARPRO D_p pre-treatment showed statistically significant differences to predict pCR. (p value at Mann-Whitney test was 0.05, 0.03 and 0.008, respectively.) Exclusively SIS percentage change showed significant differences between responder and non-responder patients after SCR (p value << 0.001) and to assess pCR after SCR (p value << 0.001). The best results to predict pCR were obtained by VARPRO Fp mean value pre-treatment with area under ROC of 0.84, a sensitivity of 96.4%, a specificity of 71.4%, a positive predictive value (PPV) of 92.9%, a negative predictive value (NPV) of 83.3% and an accuracy of 91.2%. The best results to assess after treatment complete pathological response were obtained by SIS with an area under ROC of 0.89, a sensitivity of 85.7%, a specificity of 92.6%, a PPV of 75.0%, a NPV of 96.1% and an accuracy of 91.2%. Moreover, the best results to differentiate after treatment responders vs. non-responders were obtained by SIS with an area under ROC of 0.94, a sensitivity of 93.3%, a specificity of 84.2%, a PPV of 82.4%, a NPV of 94.1% and an accuracy of 88.2%. Promising initial results were obtained using a decision tree tested with all ADC, IVIM and DKI extracted parameter: we reached high accuracy to assess pathological complete response after SCR in LARC (an accuracy of 85.3% to assess pathological complete response after SCR using VARPRO D_p mean value post-treatment, ADC standard deviation value pre-treatment, MD standard deviation value post-treatment).

CONCLUSION

SIS is a hopeful DCE-MRI angiogenic biomarker to assess preoperative treatment response after SCR with delayed surgery. Furthermore, an important prognostic role was obtained by VARPRO F_p mean value pre-treatment and by a decision tree composed by diffusion parameters derived by DWI and DKI to assess pathological complete response.

Efficacy and clinical monitoring strategies for immune checkpoint inhibitors and targeted cytokine immunotherapy for locally advanced and metastatic colorectal cancer

Colorectal cancer (CRC) is the fourth most common cancer type and is the second leading cause of cancer deaths annually in the United States. Conventional treatment options include postoperative (adjuvant) and preoperative (neoadjuvant) chemotherapy and radiotherapy. Although these treatment modalities have shown to decrease tumor burden, a major limitation to chemothearpy/radiotherapy is the high recurrence rate in patients. Immune-modulation strategies have emerged as a promising new therapeutic avenue to reduce this recurrence rate while minimizing undesirable systemic side effects. This review will focus specifically on the mechanisms of monoclonal antibodies: immune checkpoint inhibitors and cytokines, as well as current drugs approved by the Food and Drug Administration (FDA) and new clinical/pre-clinical trials. Finally, this review will investigate emerging methods used to monitor tumor response post-treatment.

Diagnostic performance of gadoxetic acid-enhanced liver MRI versus multidetector CT in the assessment of colorectal liver metastases compared to hepatic resection

Background

Imaging is an essential tool in the management of patients with Colorectal cancer (CRC) by helping evaluate number and sites of metastases, determine resectability, assess response to treatment, detect drug toxicities and recurrences. Although multidetector computed tomography (MDCT) is the first tool used for staging and patient’s surveillance, magnetic resonance imaging (MRI) is the most reliable imaging modality that allows to assess liver metastases. Our purpose is to compare the diagnostic performance of gadoxetic acid-(Gd-EOB) enhanced liver MRI and contrast-enhanced MDCT in the detection of liver metastasis from colorectal cancer (mCRC).

Methods

One hundred and twenty-eight patients with pathologically proven mCRC (512 liver metastases) underwent Gd-EOB MRI and MDCT imaging. An additional 46 patients without mCRC were included as control subjects. Three radiologists independently graded the presence of liver nodules on a five-point confidence scale. Sensitivity and specificity for the detection of metastases were calculated. Weighted к values were used to evaluate inter-reader agreement of the confidence scale regarding the presence of the lesion.

Results

MRI detected 489 liver metastases and MDCT 384. In terms of per-lesion sensitivity in the detection of liver metastasis, all three readers had higher diagnostic sensitivity with Gd-EOB MRI than with MDCT (95.5% vs. 72% reader 1; 90% vs. 72% reader 2; 96% vs. 75% reader 3). Each reader showed a statistical significant difference (p < <.001 at Chi square test). MR imaging showed a higher performance than MDCT in per-patient detection sensitivity (100% vs. 74.2% [p < <.001] reader 1, 98% vs. 73% [p < <.001] reader 2, and 100% vs. 78% [p < <.001] reader 3). In the control group, MRI and MDCT showed similar per-patient specificity (100% vs. 98% [p = 0.31] reader 1, 100% vs. 100% [p = 0.92] reader 2, and 100% vs. 96% [p = 0.047] reader 3). Inter-reader agreement of lesion detection between the three radiologists was moderate to excellent (k range, 0.56–0.86) for Gd-EOB MRI and substantial to excellent for MDCT (k range, 0.75–0.8).

Conclusion

Gadoxetic acid-enhanced MRI performs significantly better in the detection of mCRC, than MDCT, particularly in patients treated with chemotherapy, in subcapsular lesions, and in peribiliary metastases.