Predicting Treatment Response of Colorectal Cancer Liver Metastases to Conventional Lipiodol-Based Transarterial Chemoembolization Using Diffusion-Weighted MR Imaging: Value of Pretreatment Apparent Diffusion Coefficients (ADC) and ADC Changes Under Therapy

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Role of diffusion-weighted imaging in response prediction and evaluation after high dose rate brachytherapy in patients with colorectal liver metastases

BACKGROUND

The aim of the study was to assess the role of diffusion-weighted imaging (DWI) to evaluate treatment response in patients with liver metastases of colorectal cancer.

PATIENTS AND METHODS

In this retrospective, observational cohort study, we included 19 patients with 18 responding metastases (R-Mets; follow-up at least one year) and 11 non-responding metastases (NR-Mets; local tumor recurrence within one year) who were treated with high-dose-rate brachytherapy (HDR-BT) and underwent pre- and post-interventional MRI. DWI (qualitatively, mean apparent diffusion coefficient [ADCmean], ADCmin, intraindividual change of ADCmean and ADCmin) were evaluated and compared between pre-interventional MRI, first follow-up after 3 months and second follow-up at the time of the local tumor recurrence (in NR-Mets, mean: 284 ± 122 d) or after 12 months (in R-Mets, mean: 387+/-64 d). Sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) for detection of local tumor recurrence were calculated on second follow up, evaluating (1) DWI images only, and (2) DWI with Gd-enhanced T1-weighted images on hepatobiliary phase (contrast-enhanced [CE] T1-weight [T1w] hepatobiliary phase [hb]).

RESULTS

ADCmean significantly increased 3 months after HDR-BT in both groups (R-Mets: 1.48 ± 0.44 and NR-Mets: 1.49 ± 0.19 x 10-3 mm2;/s, p < 0.0001 and p = 0.01), however, intraindividual change of ADCmean (175% vs.127%, p = 0.03) and ADCmin values (0.44 ± 0.24 to 0.82 ± 0.58 x 10-3 mm2/s) significantly increased only in R-Mets (p < 0.0001 and p < 0.001). ADCmin was significant higher in R-Mets compared to NR-Mets on first follow-up (p = 0.04). Sensitivity (1 vs. 0.72), specificity (0.94 vs. 0.72), PPV (0.91 vs. 0.61) and NPV (1 vs. 0.81) could be improved by combining DWI with CE T1w hb compared to DWI only.

CONCLUSIONS

DW-MRI seems to be helpful in the qualitative and quantitative evaluation of treatment response after HDR-BT of colorectal metastases in the liver.

Ultrasound-based artificial intelligence in gastroenterology and hepatology

Artificial intelligence (AI), especially deep learning, is gaining extensive attention for its excellent performance in medical image analysis. It can automatically make a quantitative assessment of complex medical images and help doctors to make more accurate diagnoses. In recent years, AI based on ultrasound has been shown to be very helpful in diffuse liver diseases and focal liver lesions, such as analyzing the severity of nonalcoholic fatty liver and the stage of liver fibrosis, identifying benign and malignant liver lesions, predicting the microvascular invasion of hepatocellular carcinoma, curative transarterial chemoembolization effect, and prognoses after thermal ablation. Moreover, AI based on endoscopic ultrasonography has been applied in some gastrointestinal diseases, such as distinguishing gastric mesenchymal tumors, detection of pancreatic cancer and intraductal papillary mucinous neoplasms, and predicting the preoperative tumor deposits in rectal cancer. This review focused on the basic technical knowledge about AI and the clinical application of AI in ultrasound of liver and gastroenterology diseases. Lastly, we discuss the challenges and future perspectives of AI.

The Role of Conventional TACE (cTACE) and DEBIRI-TACE in Colorectal Cancer Liver Metastases

Colorectal cancer (CRC) is one of the most common tumor entities worldwide and a common cause of cancer-associated death. Colorectal cancer liver metastases (CRLM) thereby constitute a severe life-limiting factor. The therapy of CRLM presents a major challenge and surgical resection as well as systemic chemotherapy remain the first-line treatment options. Over the years several locoregional, vascular- and image-based treatments offered by interventional radiologists have emerged when conventional therapies fail, or metastases recurrence occurs. Among such options is the conventional/traditional transarterial chemoembolization (cTACE) by local injection of a combination of chemotherapeutic- and embolic-agents. A similar treatment is the more recent irinotecan-loaded drug-eluting beads TACE (DEBIRI-TACE), which are administered using the same approach. Numerous studies have shown that these different types of chemoembolization can be applied in different clinical settings safely. Furthermore, such treatments can also be combined with other local or systemic therapies. Unfortunately, due to the incoherent patient populations of studies investigating TACE in CRLM, critics state that the definite evidence supporting positive patient outcomes is still lacking. In the following article we review studies on conventional and DEBIRI-TACE. Although highly dependent on the clinical setting, prior therapies and generally the study population, cTACE and DEBIRI-TACE show comparable results. We present the most representative studies on the different chemoembolization procedures and compare the results. Although there is compelling evidence for both approaches, further studies are necessary to determine which patients profit most from these therapies. In conclusion, we determine TACE to be a viable option in CRLM in different clinical settings. Nevertheless, a multidisciplinary approach is desired to offer patients the best possible care.

Scan Time Reduction in Intravoxel Incoherent Motion Diffusion-Weighted Imaging and Diffusion Kurtosis Imaging of the Abdominal Organs: Using a Simultaneous Multislice Technique With Different Acceleration Factors

OBJECTIVE

To investigate the feasibility of quantitative intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) analyses in the upper abdominal organs by simultaneous multislice diffusion-weighted imaging (SMS-DWI).

SUBJECTS AND METHODS

In this prospective study, a total of 32 participants underwent conventional DWI (C-DWI) and SMS-DWI sequences with acceleration factors of 2 and 3 (SMS2-DWI and SMS3-DWI, respectively) in the upper abdomen with multiple b-values (0, 10, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, and 2000 seconds/mm2) on a 3 T system (MAGNETOM Prisma; Siemens Healthcare, Erlangen, Germany). Image quality and quantitatively measurements of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), mean kurtosis (MK), and mean apparent diffusivity (MD) for the liver, pancreas, kidney cortex and medulla, spleen, and erector spine muscle were compared between the 3 sequences.

RESULTS

The acquisition times for C-DWI, SMS2-DWI, and SMS3-DWI were 10 minutes 57 seconds, 5 minutes 9 seconds, and 3 minutes 54 seconds. For image quality parameters, C-DWI and SMS2-DWI yielded better results than SMS3-DWI (P < 0.05). SMS2-DWI had equivalent IVIM and DKI parameters compared with that of C-DWI (P > 0.05). No statistically significant differences in the ADC, D, f, and MD values between the 3 sequences (P > 0.05) were observed. The D* and MK values of the liver (P = 0.005 and P = 0.012) and pancreas (P = 0.019) between SMS3-DWI and C-DWI were significantly different.

CONCLUSIONS

SMS2-DWI can substantially reduce the scan time while maintaining equivalent IVIM and DKI parameters in the abdominal organs compared with C-DWI.

Optimizing Loco Regional Management of Oligometastatic Colorectal Cancer: Technical Aspects and Biomarkers, Two Sides of the Same Coin

Colorectal cancer (CRC) is the third most common cancer worldwide and has a high rate of metastatic disease which is the main cause of CRC-related death. Oligometastatic disease is a clinical condition recently included in ESMO guidelines that can benefit from a more aggressive locoregional approach. This review focuses the attention on colorectal liver metastases (CRLM) and highlights recommendations and therapeutic locoregional strategies drawn from the current literature and consensus conferences. The different percutaneous therapies (radiofrequency ablation, microwave ablation, irreversible electroporation) as well as trans-arterial approaches (chemoembolization and radioembolization) are discussed. Ablation margins, the choice of the imaging guidance as well as characteristics of the different ablation techniques and other technical aspects are analyzed. A specific attention is then paid to the increasing role of biomarkers (in particular molecular profiling) and their role in the selection of the proper treatment for the right patient. In conclusion, in this review an up-to-date state of the art of the application of locoregional treatments on CRLM is provided, highlighting both technical aspects and the role of biomarkers, two sides of the same coin.

Deep learning-assisted magnetic resonance imaging prediction of tumor response to chemotherapy in patients with colorectal liver metastases

Accurate evaluation of tumor response to preoperative chemotherapy is crucial for assigning appropriate patients with colorectal liver metastases (CRLM) to surgery or conservative therapy. However, there is no well-recognized method for predicting pathological response before surgery. Our study constructed and validated a deep learning algorithm using prechemotherapy and postchemotherapy magnetic resonance imaging (MRI) to predict pathological response in CRLM. CRLM patients from center one who had ≤5 lesions and were scheduled to receive preoperative chemotherapy followed by liver resection between January 2013 and November 2016, were included prospectively and chronologically divided into a training cohort (80% of patients) and a testing cohort (20% of patients). Patients from center two were included January 2017 and December 2018 as an external validation cohort. MRI-based models were constructed to discriminate according to pathology tumor regression grade (TRG) between the response (TRG1/2) and nonresponse (TRG3/4/5) groups at the lesion level. From center one, 155 patients (328 lesions) were included; chronologically, 101 (264 lesions) in the training cohort and 54 (64 lesions) in the testing cohort. The model achieved better accuracy (0.875 vs 0.578) and AUC (0.849 vs 0.615) than RECIST for discriminating response; it also distinguished the survival outcomes after hepatectomy better than the RECIST criteria. Evaluations of the external validation cohort (25 patients, 61 lesions) also showed good ability with an AUC of 0.833. In conclusion, the MRI-based deep learning model provided accurate prediction of pathological tumor response to preoperative chemotherapy in patients with CRLM and may inform individualized treatment.

Diffusion-Weighted MR Imaging of Primary and Secondary Lung Cancer: Predictive Value for Response to Transpulmonary Chemoembolization and Transarterial Chemoperfusion

PURPOSE

To examine predictive value of apparent diffusion coefficient (ADC) in diffusion-weighted imaging (DWI) for response of patients with primary and secondary lung neoplasms undergoing transpulmonary chemoembolization (TPCE) and transarterial chemoperfusion (TACP) treatment.

MATERIALS AND METHODS

Thirty-one patients (mean age ± SD 64 ± 12.4 y) with 42 lung target lesions (13 primary and 29 secondary) underwent DWI and subsequent ADC analysis on a 1.5T MR imaging scanner before and 30.3 days ± 6.4 after first session of TPCE or TACP. After 3.1 treatment sessions ± 1.4 performed in 2- to 4-week intervals, morphologic response was analyzed by comparing tumor diameter and volume before and after treatment on unenhanced T1-weighted MR images. On a per-lesion basis, response was classified according to Response Evaluation Criteria In Solid Tumors.

RESULTS

Threshold ADC increase of 20.7% indicated volume response with 88% sensitivity and 78% specificity (area under the curve [AUC] = 0.84). Differences between ADC changes in volume response groups were significant (P = .002). AUC for volume response predicted by ADC before treatment was 0.77. Median ADC before treatment and mean ADC change were 1.09 × 10-3 mm2/second and 0.36 × 10-3 mm2/second ± 0.23, 1.45 × 10-3 mm2/second and 0.14 × 10-3 mm2/second ± 0.16, and 1.30 × 10-3 mm2/second and 0.06 × 10-3 mm2/second ± 0.19 in partial response, stable disease, and progressive disease groups. In primary lung cancer lesions, strong negative correlation of ADC change with change in diameter (ρ = -.87, P < .001) and volume (ρ = -.66, P = .016) was found. In metastases, respective correlation coefficients were ρ = -.18 (P = .356) and ρ = -.35 (P = .061).

CONCLUSIONS

ADC quantification shows considerable diagnostic value for predicting response and monitoring TPCE and TACP treatment of patients with primary and secondary lung neoplasms.

Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound

OBJECTIVES

We aimed to establish and validate an artificial intelligence-based radiomics strategy for predicting personalized responses of hepatocellular carcinoma (HCC) to first transarterial chemoembolization (TACE) session by quantitatively analyzing contrast-enhanced ultrasound (CEUS) cines.

METHODS

One hundred and thirty HCC patients (89 for training, 41 for validation), who received ultrasound examination (CEUS and B-mode) within 1 week before the first TACE session, were retrospectively enrolled. Ultrasonographic data was used for building and validating deep learning radiomics-based CEUS model (R-DLCEUS), machine learning radiomics-based time-intensity curve of CEUS model (R-TIC), and machine learning radiomics-based B-Mode images model (R-BMode), respectively, to predict responses (objective-response and non-response) to TACE with reference to modified response evaluation criteria in solid tumor. The performance of models was compared by areas under the receiver operating characteristic curve (AUC) and the DeLong test was used to compare different AUCs. The prediction robustness was assessed for each model.

RESULTS

AUCs of R-DLCEUS, R-TIC, and R-BMode were 0.93 (95% CI, 0.80-0.98), 0.80 (95% CI, 0.64-0.90), and 0.81 (95% CI, 0.67-0.95) in the validation cohort, respectively. AUC of R-DLCEUS shows significant difference compared with that of R-TIC (p = 0.034) and R-BMode (p = 0.039), whereas R-TIC was not significantly different from R-BMode. The performance was highly reproducible with different training and validation cohorts.

CONCLUSIONS

DL-based radiomics method can effectively utilize CEUS cines to achieve accurate and personalized prediction. It is easy to operate and holds good potential for benefiting TACE candidates in clinical practice.

KEY POINTS

• Deep learning (DL) radiomics-based CEUS model can accurately predict responses of HCC patients to their first TACE session by quantitatively analyzing their pre-operative CEUS cines. • The visualization of the 3D CNN analysis adopted in CEUS model provided direct insight into what computers "see" on CEUS cines, which can help people understand the interpretation of CEUS data. • The proposed prediction method is easy to operate and labor-saving for clinical practice, facilitating the clinical treatment decision of HCCs with very few time costs.

The feasibility of b-value maps based on threshold DWI for detection of breast cancer: A case-control STROBE compliant study

Diffusion-weighted imaging (DWI) plays an important role in the diagnosis of breast cancer as well as the evaluation of treatment effects. A novel technique named b-value map based on thresholded DWI images has been proposed and can achieve good contrast for demonstrating prostate lesions only by manipulating the window width and center of the images. Its application on the breast has not yet explored, so the aim of the study was to investigate the feasibility of b-value maps based on threshold DWI for detection of breast cancer. A total of 25 patients with pathologically proven invasive ductal breast carcinoma were included and underwent preoperative magnetic resonance imaging (MRI) examinations including DWI at 3T. The capabilities to display lesions of DWIb=800, b-value maps and optimal computed DWI (cDWI) images were evaluated by using a 4-point method of scoring. Apparent diffusion coefficient (ADC) values of lesions were measured for the breast carcinoma. Mean scores indicating the display capability were compared among DWIb=800, optimal cDWI and b-value maps by using Kruskal-Wallis test followed by Nemenyi test. The scores of both b-value maps (3.92 ± 0.28) and optimal cDWI images (3.80 ± 0.41) were higher than that of DWIb=800 (3.48 ± 0.51), with statistical differences (P = .001 and P = .033, respectively). The optimal b values for manifesting breast carcinoma based on cDWI were 1000 to 1200 s/mm. The b-value map enables fast identification for breast lesions and shows similar performance to the optimal cDWI images.

Baseline 3D-ADC outperforms 2D-ADC in predicting response to treatment in patients with colorectal liver metastases

OBJECTIVES

To examine the value of baseline 3D-ADC and to predict short-term response to treatment in patients with hepatic colorectal metastases (CLMs).

METHODS

Liver MR images of 546 patients with CLMs (2008-2015) were reviewed retrospectively and 68 patients fulfilled inclusion criteria. Patients had received systemic chemotherapy (n = 17), hepatic trans-arterial chemoembolization or TACE (n = 34), and ⁹⁰Y radioembolization (n = 17). Baseline (pre-treatment) 3D-ADC (volumetric) of metastatic lesions was calculated employing prototype software. RECIST 1.1 was used to assess short-term response to treatment. Prediction of response to treatment by baseline 3D-ADC and 2D-ADC (ROI-based) was also compared in all patients.

RESULTS

Partial response to treatment (minimum 30% decrease in tumor largest transverse diameter) was seen in 35.3% of patients; 41.2% with systemic chemotherapy, 32.4% with TACE, and 35.3% with ⁹⁰Y radioembolization (p = 0.82). Median baseline 3D-ADC was significantly lower in responding than in nonresponding lesions. Area under the curve (AUC) of 3D-ADC was 0.90 in ⁹⁰Y radioembolization patients, 0.88 in TACE patients, and 0.77 in systemic chemotherapy patients (p < 0.01). Optimal prediction was observed with the 10th percentile of ADC (1006 × 10^-6 mm²/s), yielding sensitivity and specificity of 77.4% and 91.3%, respectively. 3D-ADC outperformed 2D-ADC in predicting response to treatment (AUC; 0.86 vs. 0.71; p < 0.001).

CONCLUSION

Baseline 3D-ADC is a highly specific biomarker in predicting partial short-term response to treatment in hepatic CLMs.

KEY POINTS

• Baseline 3D-ADC is a highly specific biomarker in predicting response to different treatments in hepatic CLMs. • The prediction level of baseline ADC is better for⁹⁰Y radioembolization than for systemic chemotherapy/TACE in hepatic CLMs. • 3D-ADC outperforms 2D-ADC in predicting short-term response to treatment in hepatic CLMs.