Home Journals Articles Updates About
CN
image
收藏切换
Improved treatment of colorectal liver metastases by early response evaluation and regimen adjustment: a prospective study of clinical functional MR-based modeling
收藏切换
PDF
Wenhua Li1, 2, Huan Zhang2, 3, Zhe Gong4, Yue Li2, 3, Zhiyu Chen1, 2, Xiaodong Zhu1, 2, Mingzhu Huang1, 2, Zhe Zhang1, 2, Chenchen Wang1, 2, Lixin Qiu1, 2, Qirong Geng1, 2, Jinjia Chang1, 2, Xiaoying Zhao1, 2, Xuedan Sheng1, 2, Wen Zhang1, 2, Tong Tong2, 3, Weijian Guo1, 2
Cancer Biology & Medicine | 2025, 22(2) : 166 - 176
Less
收藏切换
Cancer Biology & Medicine | 2025, 22(2): 166-176
• Original Article •
Improved treatment of colorectal liver metastases by early response evaluation and regimen adjustment: a prospective study of clinical functional MR-based modeling
Full
Wenhua Li1, 2, Huan Zhang2, 3, Zhe Gong4, Yue Li2, 3, Zhiyu Chen1, 2, Xiaodong Zhu1, 2, Mingzhu Huang1, 2, Zhe Zhang1, 2, Chenchen Wang1, 2, Lixin Qiu1, 2, Qirong Geng1, 2, Jinjia Chang1, 2, Xiaoying Zhao1, 2, Xuedan Sheng1, 2, Wen Zhang1, 2, Tong Tong2, 3, Weijian Guo1, 2
Affiliations
  • 1Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
  • 2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
  • 3Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
  • 4Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250000, China
About Author:

*These authors contributed equally to this work.

Author contributions

Conceived and designed the analysis: Wenhua Li, Huan Zhang, Wen Zhang, Tong Tong, Weijian Guo.

Collected the data: Wenhua Li, Huan Zhang, Zhe Gong, Yue Li

Contributed data or analysis tools: Zhe Gong, Zhiyu Chen, Xiaodong Zhu, Mingzhu Huang, Zhe Zhang, Chenchen Wang, Lixin Qiu, Qirong Geng, Jinjia Chang, Xiaoying Zhao, Xuedan Sheng.

Performed the analysis: Wenhua Li, Huan Zhang, Zhe Gong, Yue Li.

Wrote the paper: Wenhua Li, Huan Zhang, Weijian Guo.

Published: 2025-02-15 doi: 10.20892/j.issn.2095-3941.2024.0389
Outline
收藏切换
Objective:

The aim of the study was to evaluate the feasibility of functional MR in predicting the clinical response to chemotherapy in patients with colorectal liver metastases (CLM).

Methods:

A total of 196 eligible patients were enrolled in the study between August 2016 and January 2023. Functional MR was performed at baseline and after one cycle of chemotherapy. The diffusion kurtosis radiomic texture features were extracted and a signature model was built using the R package. The initial 100 cases were designated as the training set, the following 48 cases were designated as the validation set, and the final 48 cases were designated as the intervention validation set.

Results:

Good performance for the response prediction (AUC = 0.818 in the training set and 0.755 in the validation set) was demonstrated. The objective response rates (ORRs) in the high-risk subgroup were significantly lower than the low-risk subgroup in the training and validation sets. Worse progression-free survival and overall survival rates were noted in the high-risk population. In the intervention set 22.9% (11/48) of the chemotherapy regimens for patients were changed in response to the model-predicted results and the ORR reached 77.1% (37/48), which was significantly higher than the training and validation sets [47.97% (71/148); P = 0.000].

Conclusions:

A functional MR signature effectively predicted the chemotherapy response and long-term survival. The adjustment of the regimen guided by the model significantly improved the ORR.

Colorectal cancer  /  liver metastases  /  objective response rate  /  MR
Wenhua Li, Huan Zhang, Zhe Gong, Yue Li, Zhiyu Chen, Xiaodong Zhu, Mingzhu Huang, Zhe Zhang, Chenchen Wang, Lixin Qiu, Qirong Geng, Jinjia Chang, Xiaoying Zhao, Xuedan Sheng, Wen Zhang, Tong Tong, Weijian Guo. Improved treatment of colorectal liver metastases by early response evaluation and regimen adjustment: a prospective study of clinical functional MR-based modeling[J]. Cancer Biology & Medicine, 2025 , 22 (2) : 166 -176 . DOI: 10.20892/j.issn.2095-3941.2024.0389
Full Text
Less
Introduction
Less
The incidence and mortality rate of colorectal cancer rank third worldwide. Distant metastases are important factors for the poor prognosis of patients with colorectal liver metastases (CLM)1. CLM patients comprise a unique population that have the potential to be cured through resection and chemotherapy2.
Chemotherapy is beneficial for shrinking tumors and increasing the opportunity for R0 resection of the liver in CLM patients3,4. The 5-year overall survival (OS) rate reaches 30% after successful conversion treatment5,6. Traditional efficacy evaluation is usually performed after every 2–3 cycles of medication (6–8 weeks) using computed tomography (CT) or magnetic resonance (MR) imaging, according to the Response Evaluation Criteria In Solid Tumors (RECIST) criteria, which are based on changes in the size of the tumor. An earlier efficacy evaluation or response prediction is critical for CLM patients because the use of inappropriate treatment could be prevented and the drug regimen could be changed as soon as possible.
Molecular and imaging biomarkers for response prediction, such as positron emission tomography and functional MR, have received increasing attention in selecting CLM patients who will benefit from treatment7,8. Functional MR can provide more innate lesion characteristics and features in multiple dimensions with diffusion sequences, such as diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI). DWI is based on a single exponential model of water molecular signal attenuation, which has been reported to reflect the treatment response before the tumor size changes9. However, DKI has an advanced non-Gaussian distribution modality of the water molecule deviation degree from the Gaussian distribution, which is superior to traditional diffusion imaging in describing tissue microstructure10. In recent years correlations between one or two DKI parameters and clinical response have been reported in hepatocellular carcinoma11, nasopharyngeal carcinoma12, gastric cancer13, and breast cancer14. However, the early predictive value of the DKI sequence in liver lesions from patients with colorectal cancer has not been thoroughly investigated.
There are several issues in currently reported studies on functional imaging, which limits application in clinical practice. First, most studies retrospectively analyzed data with a limited sample size (dozens of cases) and without strict enrollment and uniform treatment. The other limitation is that only the correlation between specific parameters and efficacy was observed and there was no further intervention to verify the observations.
The current study was prospectively designed to evaluate the feasibility of a functional MR model for predicting clinical response and further applied to guide treatment regimens in patients with CLM.
Materials and methods
Less
Study design, patient enrollment, treatment, and effect evaluation
The FDZL-MRinCLM study (NCT03088163) was approved by our Institutional Review Board (Clinical trial registration: NCT03088163) and written informed consent was obtained from all participants. Eligible patients who were ≥ 18 years of age with histologically confirmed colorectal adenocarcinoma and had at least 1 measurable liver metastatic lesion were included. No previous systemic chemotherapy or local treatment (including radiotherapy, radiofrequency ablation and/or surgery) had been performed on the liver lesions.
Only patients who were unsuitable or could not afford the target drugs were enrolled in the first period. Patients were treated with oxaliplatin combined with 5-fluorouracil and leucovorin (FOLFOX), oxaliplatin combined with capecitabine (XELOX), or irinotecan combined with 5-fluorouracil and leucovorin (FOLFIRI) at the discretion of the medical oncologists. After the functional MR model was constructed the protocol was amended in 2019 to increase patient enrollment for validation. Because the target drugs (bevacizumab and cetuximab) were both approved for insurance coverage in China at that time, the study allowed the enrolled patients to receive target drug combined with chemotherapy. Performance of the MR model was evaluated after completion of the validation cohort. The area under the receiver operating characteristic (ROC) curve (AUC) exceeded 0.75 and the study proceeded into the intervention validation component. In the third period treatment intervention was carried out according to the model prediction. If the predicted score of the model exhibited low risk (i.e., a predicted response), no change to the regimen was made. If a patient was at high risk (i.e., a predicted non-response), the oxaliplatin-based regimen was switched to the irinotecan-based regimen considering the low probability of the response, which allowed addition of the target drug and vice versa. Figure 1 shows the flowchart of this study.
The functional MR scan with the DKI sequence of the liver was performed at baseline and within 3 days before the second treatment cycle. The traditional response evaluation of CT/MR scanning was conducted every 6–8 weeks according to the RECIST criteria. A complete response (CR) or partial response (PR) was considered a response, whereas stable disease (SD) or progression of disease (PD) was considered a non-response.
The primary endpoint of the study was the accuracy of the MR-predicted response rate (ORR) of liver metastases, and the secondary endpoints were progression-free survival (PFS) and OS in the predicted response and non-response subgroups.
Functional MR imaging
Functional MR imaging was performed using a MAGNETOM Skyra 3T MR scanner (Siemens Healthcare, Erlangen, Germany). The inline-generated parametric maps included K and D. The whole-lesion histogram and texture features were extracted from the above parametric maps using a prototype post-processing MR multiparametric analysis software (Siemens Healthcare).
The two largest hepatic metastatic lesions of multiple liver metastases were selected as regions of interest (ROIs) in each patient. Two experienced radiologists who were blinded to the clinical information independently placed the circular ROI over the entire lesion on each slice with a diameter exceeding 1 cm to generate the corresponding mean values. A senior abdominal radiologist with > 20 years of experience was consulted for final arbitration in cases of disagreement (Figure S1).
Statistical analysis
Analyses were performed using R software version 3.6.0 (https://www.r-project.org/). The correlations between the parameters and clinical response were analyzed using a chi-square test or Fisher’s exact test for categorical variables. The log-rank test was used to estimate PFS and OS. A two-sided P value < 0.05 was considered significant.
The sample size for the modeling cohort was estimated based on the number of independent variables expected to be included in the model. Given that 10 texture features were anticipated, the study adopted the empirical rule of thumb that suggests a sample size should be 10–20 times the number of variables for reliable model estimation and inference. Consequently, the modeling cohort was set at 100 subjects with a 2:1 ratio allocated between the modeling and validation cohorts. A single-arm phase II study design was used in the intervention cohort to quickly assess the effectiveness of model-based interventions. Using PASS 15 software with an α of 0.05, the power was 0.905 in detecting a significant difference with 48 cases, assuming that an increase in ORR from 55% to 75% was clinically significant.
Results
Less
Study population and response evaluation
The initial 100 patients were selected as the training cohort for model building, the following 48 patients comprised the validation cohort, and the remaining 48 patients comprised the intervention validation cohort. The clinical and pathologic characteristics were well-balanced except for the proportion of patients receiving combined targeted therapy (Table 1).
The ORR was 46% (46/100) in the training cohort and the median PFS and OS were 7.6 months (range, 5.91–9.35 months) and 23.5 months (range, 16.95–30.05 months), respectively.
Seven patients in the validation cohort received chemotherapy plus the target drug regimen (two plus bevacizumab; five plus cetuximab). The ORR was 52.1% (25/48). The median PFS was 9.1 months (range, 7.23–12.3 months) and the median OS was not reached.
Eleven patients in the intervention validation cohort were included in the high-risk group and the chemotherapy regimen was changed (Table S1). The ORR was 77.1% (37/48). The median PFS was 11.3 months (range, 10.2–18.5 months) and the median OS was not reached.
Functional MR signature construction and predictive value for response and survival
Twenty-two DKI sequence features were extracted from the liver MR of each patient in the training cohort. Most of the D-value features increased after one treatment cycle, whereas several K-value features decreased. Twenty texture features, which exhibited the ability to predict chemotherapy efficacy (Table S2), were subjected to a binary logistic regression model with a lasso penalty to construct the best texture feature model. One thousand iterations were performed and a texture feature model consisting of 6 MRI texture features had the highest frequency (334). The formula is as follows: risk score = (0.000239681 × baseline-D-std) + (1.174238162 × delta-D-mean) + (4.228526688 × delta-D-DiffEntropy) + (0.034209315 × delta-K-95%) + (0.212348049 × delta-K-DiffEntropy) − (0.002927063 × baseline-K-median). ROC analysis was used and the AUC for the signature was 0.818 (Figure 2).
Patients were divided into low- (n = 58) and high-risk groups (n = 42) based on the median value of risk score (−2.325). The ORR in the low-risk group was significantly higher than the high-risk group (72.41% vs. 9.52%; P < 0.001). Furthermore, the median PFS (5.43 months vs. 9.03 months) and median OS (13.67 months vs. 25.77 months) in high-risk patients were significantly shorter than high-risk patients, which also indicated the prognostic value of the signature (Figure 3).
Validation of the functional MR signature model
The AUC was 0.755 in the validation cohort. The ORRs in low- and high-risk patients were 74.07% (20/27) and 23.81% (5/21), respectively (P = 0.001). In addition, a worse median PFS (7.1 months vs. 11.4 months; P = 0.039) and median OS (19.5 months vs. not reached; P = 0.0095) were observed in the high-risk group (Figure 4).
Among the patients who received the target drug (n = 7), the predicted ORR was 100% in 6 low-risk patients and 0% in 1 high-risk patient.
The good performance of the model was further proven in a total of 148 patients, independent of drug regimen, primary tumor location, and gene status (see Figures S2-S7).
Model-guided treatment intervention to improve the ORR
The ORR reached 77.1% (37/48) in the intervention validation cohort after treatment adjustment [83.8% (31/37) in unmodified patients (low-risk subgroup) and 54.6% (6/11) in modified high-risk group patients]. The median PFS of the two groups were similar after early intervention (11.3 months in unmodified patients and 15.1 months in modified patients; P = 0.47) and the OS was not reached (Figure S8).
The ORR in the intervention validation cohort was significantly improved [77.1% (37/48) vs. 47.97% (71/148); P = 0.000] and the median PFS was prolonged (11.3 months vs. 8.33 months; P = 0.0085) compared to the combined training and validation cohorts. Treatment intervention guided by the model in high-risk patients contributed to a better response and longer PFS according to the subgroup analysis with ORRs of 54.6% (6/11) vs. 14.3% (9/63) and median PFS rates of 15.1 months vs. 5.6 months, with P values of 0.002 and 0.027, respectively. No significant difference in efficacy was detected in low-risk patients (Figure 5).
The combination of cetuximab with a double-drug regimen improves the ORR15 unlike bevacizumab16. The target drugs used in the validation and intervention cohorts were analyzed (Table S3) and patients who received cetuximab were excluded to avoid the influence of target drugs on the response. Improvements in ORR (73.2% vs. 50%; P = 0.027) and PFS (10.3 months vs. 9.1 months; P = 0.044) were demonstrated between the intervention and validation cohorts and a greater ORR trend (50% vs. 23.8%) and prolonged PFS (15.1 months vs. 7.1 months; P = 0.017) were observed in the high-risk subgroup (Figure S9).
Discussion
Less
The current study explored the functional MR signature model, which is capable of predicting the chemotherapy response of CLM patients early. The value of the functional MR signature model was confirmed in informing treatment decision-making. This is the first prospective clinical study to investigate and validate a functional MR model in > 100 patients. This model was also applied to guide regimen changes in intervention validation.
Tumor shrinkage is urgently needed in conversion therapy and in patients with a high tumor burden. It is clinically meaningful to adjust the therapeutic regimen to improve the ORR, increase the likelihood of resection, and prolong survival. The ORR is nearly 70% in patients receiving anti-EGFR therapy (e.g., cetuximab) combined with a double- or triple-drug regimen in the first-line setting17. However, greater than 50% of patients have RAS/RAF mutations and are not suitable for EGFR antibodies18. The ORR is 40%–50% for patients receiving a double-drug regimen with or without bevacizumab. The ORR can reach 70% when a triple-drug regimen is given19 but increased toxicity and poor tolerance limit use. In addition, not all drugs in triple-drug regimens are effective. In the current study a double-drug regimen with or without bevacizumab guided by an MR model was obtained and an ORR of 73.2% was achieved, which is similar to the triple-drug regimen, excluding patients who received anti-EGFR antibodies. Early response prediction by the MR model can help adjust regimens promptly and avoid unnecessary treatment and toxicity, which has important practical clinical value.
MR-DKI is an advanced non-Gaussian distribution modality introduced by Jensen et al. in 2005 to estimate and quantify the skewed distribution of water diffusion based on a probability distribution function10. DKI extracts the kurtosis coefficient (K) in addition to a diffusion coefficient, which shows diffusion deviance from a Gaussian approach and the diffusion coefficient (D) with the correction of non-Gaussian bias. DKI is more accurate for diagnosis and tumor grading compared to traditional ADC mapping20,21. DKI is useful in diagnosing pathologic changes in rectal cancer22,23 by identifying metastatic or benign lymph node status and predicting sensitivity to neoadjuvant chemoradiotherapy24,25. Moreover, recent studies have shown that DKI facilitates assessment of molecular gene expression or mutation. The mean K-value has been proven to be more reliable than the mean D-value for detecting positive MMR status and HER2 expression26. The increased standard deviation (Std) of the mean K has good diagnostic performance in detecting RAS mutations in patients with CLM27.
However, the value of DKI in chemotherapy of patients with CLM has not been reported. Based on our previous analysis of 40 cases, pre-treatment IVIM (Dslow), DKI (D and K), and conventional DWI (ADC) parameters showed a good diagnostic performance in predicting the chemotherapeutic response28. In the present study there was a focus on the DKI sequence and 9 baseline variables and 11 early changes in variables related to the response were identified. Among the variables, baseline D-Std, baseline K-median, delta D-mean, delta D-DiffEntropy, delta K-95%, and delta K-DiffEntropy were included in the functional MR model. In addition, the mean kurtosis (K-mean) is related to chemotherapy response in certain tumor types13,29. Other variables have rarely been reported in previous studies. More importantly, the prognostic value of the model was also demonstrated. Based on the baseline data and data from only one cycle after surgery, high-risk patients not only experienced unsatisfactory tumor shrinkage but also poor survival.
Conclusions
Less
In conclusion, the baseline value and early parameter changes in DKI are good predictive markers for chemotherapeutic response in patients with CLM. The functional MR model is promising for early response prediction and valuable for early guidance of treatment adjustment. The number of patients in the intervention validation cohort was limited and the model needs to be verified in randomized controlled trials with more patients.
Funding
Less
  • This study was supported by the National Natural Science Foundation of China (Grant nos. 82271946 and 82202101) and Innovative Achievement Transformation of Shenkang Medical-Enterprise Integration (Grant no. SHDC2023CRD014).
References
Less
1.
Sung H,Ferlay J,Siegel RL,Laversanne M,Soerjomataram I,Jemal A, et al.Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin.2021; 71: 209–49.
2.
Chandra P,Sacks GD.Contemporary surgical management of colorectal liver metastases.Cancers (Basel).2024; 16: 941.
3.
Bond MJG,Bolhuis K,Loosveld OJL,de Groot JWB,Droogendijk H,Helgason HH, et al.First-line systemic treatment strategies in patients with initially unresectable colorectal cancer liver metastases (CAIRO5): an open-label, multicentre, randomised, controlled, phase 3 study from the Dutch Colorectal Cancer Group.Lancet Oncol.2023; 24: 757–71.
4.
Bolhuis K,Kos M,van Oijen MGH,Swijnenburg RJ,Punt CJA.Conversion strategies with chemotherapy plus targeted agents for colorectal cancer liver-only metastases: a systematic review.Eur J Cancer.2020; 141: 225–38.
5.
Wang DS,Ren C,Li SS,Fong WP,Wu XJ,Xiao J, et al.Cetuximab plus FOLFOXIRI versus cetuximab plus FOLFOX as conversion regimen in RAS/BRAF wild-type patients with initially unresectable colorectal liver metastases (TRICE trial): a randomized controlled trial.PLoS Med.2024; 21: e1004389.
6.
Jones RP,Hamann S,Malik HZ,Fenwick SW,Poston GJ,Folprecht G.Defined criteria for resectability improves rates of secondary resection after systemic therapy for liver limited metastatic colorectal cancer.Eur J Cancer.2014; 50: 1590–601.
7.
Ünal E,Karaosmanoglu AD,Ozmen MN,Akata D,Karcaaltincaba M.Hepatobiliary phase liver MR imaging findings after Oxaliplatin-based chemotherapy in cancer patients.Abdom Radiol (NY).2018; 43: 2321–8.
8.
Wang C,Guo W,Zhou M,Zhu X,Ji D,Li W, et al.The predictive and prognostic value of early metabolic response assessed by positron emission tomography in advanced gastric cancer treated with chemotherapy.Clin Cancer Res.2016; 22: 1603–10.
9.
Uutela A,Ovissi A,Hakkarainen A,Ristimäki A,Lundbom N,Kallio R, et al.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).ESMO Open.2021; 6: 100208.
10.
Jensen JH,Helpern JA,Ramani A,Lu H,Kaczynski K.Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging.Magn Reson Med.2005; 53: 1432–40.
11.
Zhou Y,Zheng J,Yang C,Peng J,Liu N,Yang L, et al.Application of intravoxel incoherent motion diffusion-weighted imaging in hepatocellular carcinoma.World J Gastroenterol.2022; 28: 3334–45.
12.
Zhao DW,Fan WJ,Meng LL,Luo YR,Wei J,Liu K, et al.Comparison of the pre-treatment functional MRI metrics’ efficacy in predicting locoregionally advanced nasopharyngeal carcinoma response to induction chemotherapy.Cancer Imaging.2021; 21: 59.
13.
Fu J,Tang L,Li ZY,Li XT,Zhu HF,Sun YS, et al.Diffusion kurtosis imaging in the prediction of poor responses of locally advanced gastric cancer to neoadjuvant chemotherapy.Eur J Radiol.2020; 128: 108974.
14.
Kang HS,Kim JY,Kim JJ,Kim S,Lee NK,Lee JW, et al.Diffusion kurtosis MR imaging of invasive breast cancer: correlations with prognostic factors and molecular subtypes.J Magn Reson Imaging.2022; 56: 110–20.
15.
Qin S,Li J,Wang L,Xu J,Cheng Y,Bai Y, et al.Efficacy and tolerability of first-line cetuximab plus leucovorin, fluorouracil, and oxaliplatin (FOLFOX-4) versus FOLFOX-4 in patients with RAS wild-type metastatic colorectal cancer: the open-label, randomized, phase III TAILOR trial.J Clin Oncol.2018; 36: 3031–9.
16.
Saltz LB,Clarke S,Diaz-Rubio E,Scheithauer W,Figer A,Wong R, et al.Bevacizumab in combination with oxaliplatin-based chemotherapy as first-line therapy in metastatic colorectal cancer: a randomized phase III study.J Clin Oncol.2023; 41: 3663–9.
17.
Hu H,Wang K,Huang M,Kang L,Wang W,Wang H, et al.Modified FOLFOXIRI with or without cetuximab as conversion therapy in patients with RAS/BRAF wild-type unresectable liver metastases colorectal cancer: the FOCULM multicenter phase II trial.Oncologist.2021; 26: e90–e8.
18.
NCCN Clinical Practice Guidelines in Oncology, Colon Cancer, Version 1, 2023.2023.
19.
Cremolini C,Antoniotti C,Rossini D,Lonardi S,Loupakis F,Pietrantonio F, et al.Upfront FOLFOXIRI plus bevacizumab and reintroduction after progression versus mFOLFOX6 plus bevacizumab followed by FOLFIRI plus bevacizumab in the treatment of patients with metastatic colorectal cancer (TRIBE2): a multicentre, open-label, phase 3, randomised, controlled trial.Lancet Oncol.2020; 21: 497–507.
20.
Cheng Q,Ren A,Xu X,Meng Z,Feng X,Pylypenko D, et al.Application of DKI and IVIM imaging in evaluating histologic grades and clinical stages of clear cell renal cell carcinoma.Front Oncol.2023; 13: 1203922.
21.
Pang H,Dang X,Ren Y,Yao Z,Shen Y,Feng X, et al.DKI can distinguish high-grade gliomas from IDH1-mutant low-grade gliomas and correlate with their different nuclear-to-cytoplasm ratio: a localized biopsy-based study.Eur Radiol.2024; 34: 7539–51.
22.
Peng Y,Luo Y,Hu X,Shen Y,Hu D,Li Z, et al.Quantitative T2*-weighted imaging and reduced field-of-view diffusion-weighted imaging of rectal cancer: correlation of R2* and apparent diffusion coefficient with histopathological prognostic factors.Front Oncol.2021; 11: 670156.
23.
Tang C,Lu G,Xu J,Kuang J,Xu J,Wang P.Diffusion kurtosis imaging and MRI-detected extramural venous invasion in rectal cancer: correlation with clinicopathological prognostic factors.Abdom Radiol (NY).2023; 48: 844–54.
24.
Ma Q,Liu Z,Zhang J,Fu C,Li R,Sun Y, et al.Multi-task reconstruction network for synthetic diffusion kurtosis imaging: predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.Eur J Radiol.2024; 174: 111402.
25.
Li J,Zhou Y,Wang X,Yu Y,Zhou X,Luan K.Histogram analysis of diffusion-weighted magnetic resonance imaging as a biomarker to predict lymph node metastasis in T3 stage rectal carcinoma.Cancer Manag Res.2021; 13: 2983–93.
26.
Feng Q,Yu H,Sun S,Ma Z.The value of diffusion kurtosis imaging in assessing mismatch repair gene expression of rectal carcinoma: preliminary findings.PLoS One.2019; 14: e0211461.
27.
Granata V,Fusco R,Risi C,Ottaiano A,Avallone A,De Stefano A, et al.Diffusion-weighted MRI and diffusion kurtosis imaging to detect RAS mutation in colorectal liver metastasis.Cancers.2020; 12: 2420.
28.
Zhang H,Li W,Fu C,Grimm R,Chen Z,Zhang W, et al.Comparison of intravoxel incoherent motion imaging, diffusion kurtosis imaging, and conventional DWI in predicting the chemotherapeutic response of colorectal liver metastases.Eur J Radiol.2020; 130: 109149.
29.
Liu C,Xi Y,Li M,Jiao Q,Zhang H,Yang Q, et al.Monitoring response to neoadjuvant chemotherapy of primary osteosarcoma using diffusion kurtosis magnetic resonance imaging: initial findings.Korean J Radiol.2019; 20: 801–11.
Appendix
Less
Year 2025 volume 22 Issue 2
PDF
2
1
Cite this Article
BibTeX
Article Info
doi: 10.20892/j.issn.2095-3941.2024.0389
  • Receive Date：2024-09-09
  • Online Date：2026-11-06
  • Published：2025-02-15
Article Data
Affiliations
History
  • Received：2024-09-09
  • Accepted：2024-12-31
Funding
This study was supported by the National Natural Science Foundation of China (Grant nos. 82271946 and 82202101) and Innovative Achievement Transformation of Shenkang Medical-Enterprise Integration (Grant no. SHDC2023CRD014).
Affiliations
    1Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
    2Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
    3Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
    4Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan 250000, China

Corresponding:

Correspondence to: Weijian Guo, Tong Tong and Wen Zhang, E-mail: , and
References
Share
https://castjournals.cast.org.cn/joweb/cbm/EN/10.20892/j.issn.2095-3941.2024.0389
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62662699(Operation and Technical Support)
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House