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

Lahrsow, Maximilian; Albrecht, Moritz H.; Bickford, Matthew W.; Vogl, Thomas J.

doi:10.1007/s00270-017-1634-0

Cited by 12 publications

(9 citation statements)

References 27 publications

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“…DWI is used to detect and characterize abdominal organ lesions, and apparent diffusion coefficient (ADC) values are used to monitor and predict tumor responses to therapy 2–7 …”

mentioning

confidence: 99%

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

Xu¹,

Zhang²,

Ren³

et al. 2021

J Comput Assist Tomogr

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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

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“…DWI is used to detect and characterize abdominal organ lesions, and apparent diffusion coefficient (ADC) values are used to monitor and predict tumor responses to therapy 2–7 …”

mentioning

confidence: 99%

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

Xu¹,

Zhang²,

Ren³

et al. 2021

J Comput Assist Tomogr

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“…23,24 The ADC derived from DW-MRI seems to be the most promising predictor of treatment response; several previous studies have described a significantly lower ADC in the response group. [25][26][27] One study also found that the histogram-derived parameters of the ADC maps, including the mean and the 1st, 10th, 50th, 90th, 99th percentages of the ADC maps, were significantly lower in the response group than in the nonresponse group, with AUCs of 0.76-0.82. 28 However, most studies are limited by their small sample size and selection of a postprocessing model.…”

Section: Discussionmentioning

confidence: 98%

“…In recent years, several quantitative methods of response assessment based on changes in kinetic parameters have been proposed, such as the volume transfer constant (K‐trans), the rate constant of Gd‐DTPA uptake in DCE‐MRI (kep), as well as the T2 relaxation times using T2*MRI (susceptibility‐weighted MRI) and DW‐MRI 23,24 . The ADC derived from DW‐MRI seems to be the most promising predictor of treatment response; several previous studies have described a significantly lower ADC in the response group 25‐27 . One study also found that the histogram‐derived parameters of the ADC maps, including the mean and the 1st, 10th, 50th, 90th, 99th percentages of the ADC maps, were significantly lower in the response group than in the nonresponse group, with AUCs of 0.76‐0.82 28 .…”

Section: Discussionmentioning

confidence: 99%

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

Zhu

et al. 2020

Intl Journal of Cancer

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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.

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“…The prediction of tumor responses to TACE heavily depends on MRI and serological biomarkers[ 61 , 62 ]. But these methods achieved unsatisfactory accuracy of prediction.…”

Section: Application Of Ultrasound-based Ai In Hepatologymentioning

confidence: 99%

Ultrasound-based artificial intelligence in gastroenterology and hepatology

Liu¹,

Ren²,

Xu³

et al. 2022

World J Gastroenterol

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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.

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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

Abstract: In patients with CRLM, ADC measurements are potential biomarkers for assessing response to cTACE.

Cited by 12 publications

References 27 publications

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

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

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

Ultrasound-based artificial intelligence in gastroenterology and hepatology

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