Preoperative prediction of microsatellite instability status in colorectal cancer based on a multiphasic enhanced CT radiomics nomogram model.

BACKGROUND: To investigate the value of a nomogram model based on the combination of clinical-CT features and multiphasic enhanced CT radiomics for the preoperative prediction of the microsatellite instability (MSI) status in colorectal cancer (CRC) patients. METHODS: A total of 347 patients with a pathological diagnosis of colorectal adenocarcinoma, including 276 microsatellite stabilized (MSS) patients and 71 MSI patients (243 training and 104 testing), were included. Univariate and multivariate regression analyses were used to identify the clinical-CT features of CRC patients linked with MSI status to build a clinical model. Radiomics features were extracted from arterial phase (AP), venous phase (VP), and delayed phase (DP) CT images. Different radiomics models for the single phase and multiphase (three-phase combination) were developed to determine the optimal phase. A nomogram model that combines clinical-CT features and the optimal phasic radscore was also created. RESULTS: Platelet (PLT), systemic immune inflammation index (SII), tumour location, enhancement pattern, and AP contrast ratio (ACR) were independent predictors of MSI status in CRC patients. Among the AP, VP, DP, and three-phase combination models, the three-phase combination model was selected as the best radiomics model. The best MSI prediction efficacy was demonstrated by the nomogram model built from the combination of clinical-CT features and the three-phase combination model, with AUCs of 0.894 and 0.839 in the training and testing datasets, respectively. CONCLUSION: The nomogram model based on the combination of clinical-CT features and three-phase combination radiomics features can be used as an auxiliary tool for the preoperative prediction of the MSI status in CRC patients.

SG-Transunet: A segmentation-guided Transformer U-Net model for KRAS gene mutation status identification in colorectal cancer.

Accurately identifying the Kirsten rat sarcoma virus (KRAS) gene mutation status in colorectal cancer (CRC) patients can assist doctors in deciding whether to use specific targeted drugs for treatment. Although deep learning methods are popular, they are often affected by redundant features from non-lesion areas. Moreover, existing methods commonly extract spatial features from imaging data, which neglect important frequency domain features and may degrade the performance of KRAS gene mutation status identification. To address this deficiency, we propose a segmentation-guided Transformer U-Net (SG-Transunet) model for KRAS gene mutation status identification in CRC. Integrating the strength of convolutional neural networks (CNNs) and Transformers, SG-Transunet offers a unique approach for both lesion segmentation and KRAS mutation status identification. Specifically, for precise lesion localization, we employ an encoder-decoder to obtain segmentation results and guide the KRAS gene mutation status identification task. Subsequently, a frequency domain supplement block is designed to capture frequency domain features, integrating it with high-level spatial features extracted in the encoding path to derive advanced spatial-frequency domain features. Furthermore, we introduce a pre-trained Xception block to mitigate the risk of overfitting associated with small-scale datasets. Following this, an aggregate attention module is devised to consolidate spatial-frequency domain features with global information extracted by the Transformer at shallow and deep levels, thereby enhancing feature discriminability. Finally, we propose a mutual-constrained loss function that simultaneously constrains the segmentation mask acquisition and gene status identification process. Experimental results demonstrate the superior performance of SG-Transunet over state-of-the-art methods in discriminating KRAS gene mutation status.

Application research of radiomics in colorectal cancer: A bibliometric study.

BACKGROUND: Radiomics has shown great potential in the clinical field of colorectal cancer (CRC). However, few bibliometric studies have systematically analyzed existing research in this field. The purpose of this study is to understand the current research status and future development directions of CRC. METHODS: Search the English documents on the application of radiomics in the field of CRC research included in the Web of Science Core Collection from its establishment to October 2023. VOSviewer and CiteSpace software were used to conduct bibliometric and visual analysis of online publications related to countries/regions, authors, journals, references, and keywords in this field. RESULTS: A total of 735 relevant documents published from Web of Science Core Collection to October 2023 were retrieved, and a total of 419 documents were obtained based on the screening criteria, including 376 articles and 43 reviews. The number of publications is increasing year by year. Among them, China publishes the most relevant documents (n = 238), which is much higher than Italy (n = 69) and the United States (n = 63). Tian Jie is the author with the most publications and citations (n = 17, citations = 2128), GE Healthcare is the most productive institution (n = 26), Frontiers in Oncology is the journal with the most publications (n = 60), and European Radiology is the most cited journal (n = 776). Hot spots for the application of radiomics in CRC include magnetic resonance, neoadjuvant chemoradiotherapy, survival, texture analysis, and machine learning. These directions are the current hot spots for the application of radiomics research in CRC and may be the direction of continued development in the future. CONCLUSION: Through bibliometric analysis, the application of radiomics in CRC has been increasing year by year. The application of radiomics improves the accuracy of preoperative diagnosis, prediction, and prognosis of CRC. The results of bibliometrics analysis provide a valuable reference for the research direction of radiomics. However, radiomics still faces many challenges in the future, such as the single nature of the data source which may affect the comprehensiveness of the results. Future studies can further expand the data sources and build a multicenter public database to more comprehensively reflect the research status and development trend of CRC radiomics.

SC-Net: Symmetrical conical network for colorectal pathology image segmentation.

BACKGROUND AND OBJECTIVE: Image segmentation of histopathology of colorectal cancer is a core task of computer aided medical image diagnosis system. Existing convolutional neural networks generally extract multi-scale information in linear flow structures by inserting multi-branch modules, which is difficult to extract heterogeneous semantic information under multi-level and different receptive field and tough to establish context dependency among different receptive field features. METHODS: To address these issues, we propose a symmetric spiral progressive feature fusion encoder-decoder network called the Symmetric Conical Network (SC-Net). First, we design a Multi-scale Feature Extraction Block (MFEB) matching with the Symmetric Conical Network to obtain multi-branch heterogeneous semantic information under different receptive fields, so as to enrich the diversity of extracted feature information. The encoder is composed of MFEB through spiral and multi-branch arrangement to enhance context dependence between different information flow. Secondly, the information loss of contour, color and others in high-level semantic information through causally stacking MFEB, the Feature Mapping Layer (FML) is designed to map low-level features to high-level semantic features along the down-sampling branch and solve the problem of insufficient global feature extraction in deep levels. RESULTS: The SC-Net was evaluated on our self-constructed colorectal cancer dataset, a publicly available breast cancer dataset and a polyp dataset. The results revealed that the mDice of segmentation reached 0.8611, 0.7259 and 0.7144. We compare our model with the state-of-art semantic segmentation UNet++, PSPNet, Attention U-Net, R2U-Net and other advanced segmentation networks. The experimental results demonstrate that we achieve the most advanced performance. CONCLUSIONS: The results indicate that the proposed SC-Net excels in segmenting H&E stained pathology images, effectively preserving morphological features and spatial information even in scenarios with weak texture, poor contrast, and variations in appearance.

Photoactive imaging and therapy for colorectal cancer using a CEA-Affimer conjugated Foslip nanoparticle.

Theranostic nanoparticles hold promise for simultaneous imaging and therapy in colorectal cancer. Carcinoembryonic antigen can be used as a target for these nanoparticles because it is overexpressed in most colorectal cancers. Affimer reagents are synthetic proteins capable of binding specific targets, with additional advantages over antibodies for targeting. We fabricated silica nanoparticles using a water-in-oil microemulsion technique, loaded them with the photosensitiser Foslip, and functionalised the surface with anti-CEA Affimers to facilitate fluorescence imaging and photodynamic therapy of colorectal cancer. CEA-specific fluorescence imaging and phototoxicity were quantified in colorectal cancer cell lines and a LS174T murine xenograft colorectal cancer model. Anti-CEA targeted nanoparticles exhibited CEA-specific fluorescence in the LoVo, LS174T and HCT116 cell lines when compared to control particles (p < 0.0001). No toxicity was observed in LS174T cancer mouse xenografts or other organs. Following photo-irradiation, the anti-CEA targeted particles caused significant cell death in LoVo (60%), LS174T (90%) and HCT116 (70%) compared to controls (p < 0.0001). Photodynamic therapy (PDT) at 24 h in vivo showed a 4-fold reduction in tumour volume compared to control mouse xenografts (p < 0.0001). This study demonstrates the efficacy of targeted fluorescence imaging and PDT using Foslip nanoparticles conjugated to anti-CEA Affimer nanoparticles in in vitro and in vivo colorectal cancer models.

Pulmonary Recurrence of Colorectal Mucinous Adenocarcinoma and Sarcoidosis on 18 F-FDG PET/CT.

ABSTRACT: A 67-year-old woman, previously diagnosed with pulmonary sarcoidosis and sigmoid colon mucinous adenocarcinoma with pulmonary metastasis, showed an enlarged pulmonary nodule in routine follow-up. Because of the absence of treatment for either condition over the past 3 years, the nodule raised concerns of cancer recurrence or sarcoidosis progression. Its distinctive 18 F-FDG PET/CT appearance, compared with other pulmonary lesions, suggested a mucinous histology. The diagnosis was confirmed by pathological examination. This emphasizes the importance of knowledge of the 18 F-FDG PET/CT phenotype of neoplastic histological variants to address challenging diagnostic scenarios.

A New Automated Prognostic Prediction Method Based on Multi-Sequence Magnetic Resonance Imaging for Hepatic Resection of Colorectal Cancer Liver Metastases.

Colorectal cancer is a prevalent and life-threatening disease, where colorectal cancer liver metastasis (CRLM) exhibits the highest mortality rate. Currently, surgery stands as the most effective curative option for eligible patients. However, due to the insufficient performance of traditional methods and the lack of multi-modality MRI feature complementarity in existing deep learning methods, the prognosis of CRLM surgical resection has not been fully explored. This paper proposes a new method, multi-modal guided complementary network (MGCNet), which employs multi-sequence MRI to predict 1-year recurrence and recurrence-free survival in patients after CRLM resection. In light of the complexity and redundancy of features in the liver region, we designed the multi-modal guided local feature fusion module to utilize the tumor features to guide the dynamic fusion of prognostically relevant local features within the liver. On the other hand, to solve the loss of spatial information during multi-sequence MRI fusion, the cross-modal complementary external attention module designed an external mask branch to establish inter-layer correlation. The results show that the model has accuracy (ACC) of 0.79, the area under the curve (AUC) of 0.84, C-Index of 0.73, and hazard ratio (HR) of 4.0, which is a significant improvement over state-of-the-art methods. Additionally, MGCNet exhibits good interpretability.

Imaging-guided prognostic score-based approach to assess the benefits of combotherapy versus monotherapy with immune checkpoint inhibitors in metastatic MSI-H colorectal cancer patients.

BACKGROUND: This retrospective study determined survival responses to immune checkpoint inhibitors (ICIs), comparing mono- (mono) and combo-immunotherapy (combo) in patients with microsatellite instability-high (MSI-H) metastatic colorectal cancer (mCRC) by analyzing quantitative imaging data and clinical factors. METHODS: One hundred fifty patients were included from two centers and divided into training (n = 105) and validation (n = 45) cohorts. Radiologists manually annotated chest-abdomen-pelvis computed tomography and calculated tumor burden. Progression-free survival (PFS) was assessed, and variables were selected through Recursive Feature Elimination. Cutoff values were determined using maximally selected rank statistics to binarize features, forming a risk score with hazard ratio-derived weights. RESULTS: In total, 2258 lesions were annotated with excellent reproducibility. Key variables in the training cohort included: total tumor volume (cutoff: 73 cm3), lesion count (cutoff: 20), age (cutoff: 60) and the presence of peritoneal carcinomatosis. Their respective weights were 1.13, 0.96, 0.91, and 0.38, resulting in a risk score cutoff of 1.36. Low-score patients showed similar overall survival and PFS regardless of treatment, while those with a high-score had significantly worse survivals with mono vs combo (P = 0.004 and P = 0.0001). In the validation set, low-score patients exhibited no significant difference in overall survival and PFS with mono or combo. However, patients with a high-score had worse PFS with mono (P = 0.046). CONCLUSIONS: A score based on total tumor volume, lesion count, the presence of peritoneal carcinomatosis, and age can guide MSI-H mCRC treatment decisions, allowing oncologists to identify suitable candidates for mono and combo ICI therapies.

EARLY STAGES OF COLORECTAL CANCER CHARACTERIZATION BY AUTOFLUORESCENCE 3D MICROSCOPY: A PRELIMINARY STUDY.

BACKGROUND: Colorectal cancer is one of the most prevalent pathologies worldwide whose prognosis is linked to early detection. Colonoscopy is the gold standard for screening, and diagnosis is usually made histologically from biopsies. Aiming to reduce the inspection and diagnostic time as well as the biopsies and resources involved, other techniques are being promoted to conduct accurate in vivo colonoscopy assessments. Optical biopsy aims to detect normal and neoplastic tissues analysing the autofluorescence spectrum based on the changes in the distribution and concentration of autofluorescent molecules caused by colorectal cancer. Therefore, the autofluorescence contribution analysed by image processing techniques could be an approach to a faster characterization of the target tissue. OBJECTIVE: Quantify intensity parameters through digital processing of two data sets of three-dimensional widefield autofluorescence microscopy images, acquired by fresh colon tissue samples from a colorectal cancer murine model. Additionally, analyse the autofluorescence data to provide a characterization over a volume of approximately 50 µm of the colon mucosa for each image, at second (2nd), fourth (4th) and eighth (8th) weeks after colorectal cancer induction. METHODS: Development of a colorectal cancer murine model using azoxymethane/dextran sodium sulphate induction, and data sets acquisition of Z-stack images by widefield autofluorescence microscopy, from control and colorectal cancer induced animals. Pre-processing steps of intensity value adjustments followed by quantification and characterization procedures using image processing workflow automation by Fiji's macros, and statistical data analysis. RESULTS: The effectiveness of the colorectal cancer induction model was corroborated by a histological assessment to correlate and validate the link between histological and autofluorescence changes. The image digital processing methodology proposed was then performed on the three-dimensional images from control mice and from the 2nd, 4th, and 8th weeks after colorectal cancer chemical induction, for each data set. Statistical analyses found significant differences in the mean, standard deviation, and minimum parameters between control samples and those of the 2nd week after induction with respect to the 4th week of the first experimental study. This suggests that the characteristics of colorectal cancer can be detected after the 2nd week post-induction. CONCLUSION: The use of autofluorescence still exhibits levels of variability that prevent greater systematization of the data obtained during the progression of colorectal cancer. However, these preliminary outcomes could be considered an approach to the three-dimensional characterization of the autofluorescence of colorectal tissue, describing the autofluorescence features of samples coming from dysplasia to colorectal cancer. BACKGROUND: • A new digital image processing method was developed to measure intensity in 3D autofluorescence images of colorectal samples using a CRC mouse model. BACKGROUND: • This method showed that autofluorescence intensity in colon mucosa is similar in healthy tissue but changes significantly in tumor development. BACKGROUND: • Statistical analysis revealed CRC traits detectable from the second week post-induction, aiding in early CRC detection. BACKGROUND: • The study provides a basis for 3D autofluorescence characterization in colorectal tissue from dysplasia to cancer, although variability in autofluorescence limits data systematization during cancer progression.

Decreased mebrofenin uptake in patients with non-colorectal liver tumors requiring liver volume augmentation-a single-center analysis.

BACKGROUND: Posthepatectomy liver failure (PHLF) remains a life-threatening complication after hepatectomy. To reduce PHLF, a preoperative assessment of liver function is indispensable. For this purpose, 99mTc-mebrofenin hepatobiliary scintigraphy with SPECT (MSPECT) can be used. The aim of the current study was to evaluate the predictive value of MSPECT for PHLF in patients with non-colorectal liver tumors (NCRLT) compared to patients with colorectal liver metastasis (CRLM) undergoing extended liver resection. METHODS: We included all patients undergoing extended liver resections via two-stage procedures between January 2019 and December 2021 at the University Medical Center Hamburg-Eppendorf, Germany. All patients received a preoperative MSPECT. RESULTS: Twenty patients were included. In every fourth patient, PHLF was observed. Four patients had PHLF grade C. There were no differences between patients with CRLM and NCRLT regarding PHLF rate and future liver remnant (FLR) volume. Patients with CRLM had higher mebrofenin uptake in the FLR compared to those with NCRLT (2.49%/min/m2 vs. 1.51%/min/m2; p = 0.004). CONCLUSION: Mebrofenin uptake in patients with NCRLT was lower compared to those patients with CRLM. However, there was no difference in the PHLF rate and FLR volume. Cut-off values for the mebrofenin uptake might need adjustments for different surgical indications, surgical procedures, and underlying diseases.

Preoperative T-staging of Colorectal Cancer by Dual-energy Computed Tomography: A Retrospective Study.

BACKGROUND: Preoperative T-staging is essential for planning optimal treatment and care for colorectal cancer (CRC). OBJECTIVE: To evaluate the accuracy of Dual-energy CT (DECT) in preoperative T-staging of CRC. METHODS: The clinical data and DECT images of 37 patients with 39 CRC lesions were retrospectively analyzed. The performance of the DECT quantitative parameters in CRC T-staging was evaluated. Postoperative pathologic results were used as a gold standard. Receiver operating characteristic curves were used to assess the diagnostic efficacy of DECT parameters. P < 0.05 was deemed significant. RESULTS: The overall accuracy of T-staging by DECT was 76.9%. The DECT parameters were significantly different between the T3 pericolic fat stranding, T4a pericolic fat stranding, and normal pericolic fat stranding. Arterial phase λHU had the best diagnostic performance with a cut-off value of ≥0.967, resulting in a 70.6% sensitivity and a 100% specificity in differentiating between T3 and T4a stages of CRC. CONCLUSION: DECT has high accuracy in the T-staging of CRC. Arterial phase λHU has the best diagnostic performance in differentiating between T3 and T4a stages of CRC.

Analyzing the value of delayed 18 F-FDG PET/CT images in diagnosing small colorectal cancer liver metastases in patients with hypothyroidism based on diagnostic accuracy and image standardized uptake value.

PURPOSE: The objective of this study was to investigate the value of delayed 18F fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) images in patients with small colorectal cancer liver metastases (CRLMs) with hypothyroidism. METHOD: We performed a retrospective analysis of 66 small-CRLM patients with hypothyroidism and 66 small-CRLM patients with euthyroidism, all of whom underwent dual-time-point 18 F-FDG PET/CT imaging. First, the diagnostic accuracy of PET/CT early imaging and PET/CT delayed imaging on lesions was analyzed. Next, the correlation of metabolic parameters between PET/CT early imaging and PET/CT delayed imaging was analyzed according to the grouping of all lesions. Finally, PET/CT parameters were analyzed for correlation with thyroid hormones. RESULTS: The diagnostic accuracy of delayed imaging in small-CRLM patients with hypothyroidism is not as good as that in small-CRLM patients with euthyroidism; PET/CT metabolic parameters are also unfavorable for the diagnosis of small-CRLM. For small-CRLM patients with hypothyroidism, the greater the thyroid-stimulating hormone level, the greater the uptake of 18 F-FDG in normal liver tissue, and the smaller the ratio of tumor lesion uptake to normal liver tissue uptake. CONCLUSION: PET/CT-delayed imaging has better performance than early imaging in small-CRLM patients with euthyroidism. However, the more severe the hypothyroidism, the worse the diagnostic delayed imaging performance. The scan time can be extended appropriately to optimize the imaging efficacy.

Incidental Ring-hyperenhancing Liver Micronodules at CT Hepatic Arteriography-guided Percutaneous Thermal Ablation of Colorectal Liver Metastases.

CT during hepatic arteriography (CTHA) is a highly sensitive imaging method for detecting colorectal liver metastases (CLMs), which supports its use during percutaneous thermal liver ablation. In contrast to its high sensitivity, its specificity for incidental small CLMs not detected at preablation cross-sectional imaging is believed to be low given the absence of specific imaging signatures and the common presence of pseudolesions. In this retrospective study of 22 patients (mean age, 55 years ± 10.6 [SD]; 63.6% male, 36.4% female) with CLMs undergoing CTHA-guided microwave percutaneous thermal ablation between November 2017 and October 2022, the authors provided a definition of incidental ring-hyperenhancing liver micronodules (RHLMs) and investigated whether there is a correlation of RHLMs with histologic analysis or intrahepatic tumor progression at imaging follow-up after applying a biomechanical deformable image registration method. The analysis revealed 25 incidental RHLMs in 41.7% (10 of 24) of the CTHA images from the respective guided ablation sessions. Of those, four RHLMs were ablated. Among the remaining 21 RHLMs, 71.4% (15 of 21) were confirmed to be CLM with either histology (n = 3) or imaging follow-up (n = 12). The remaining 28.6% (six of 21) of RHLMs were not observed at follow-up imaging. This suggests that RHLMs at CTHA may be an early indicator of incidental small CLMs. Keywords: Colorectal Neoplasms, Liver, Angiography, CT, Incidental Findings, Ablation Supplemental material is available for this article. © RSNA, 2024.

Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?

PURPOSE: To assess the inter-reader reproducibility of radiomics features on multiple MRI sequences after segmentations of colorectal liver metastases (CRLM). METHOD: 30 CRLM (in 23 patients) were manually delineated by three readers on MRI before the start of chemotherapy on the contrast enhanced T1-weighted images (CE-T1W) in the portal venous phase, T2-weighted images (T2W) and b800 diffusion weighted images (DWI). DWI delineations were copied to the ADC-maps. 107 radiomics features were extracted per sequence. The intraclass correlation coefficient (ICC) was calculated per feature. Features were considered reproducible if ICC > 0.9. RESULTS: 90% of CE-T1W features were reproducible with a median ICC of 0.98 (range 0.76-1.00). 81% of DWI features were robust with median ICC = 0.97 (range 0.38-1.00). The T2W features had a median ICC of 0.96 (range 0.55-0.99) and were reproducible in 80%. ADC showed the lowest number of reproducible features with 58% and median ICC = 0.91 (range 0.38-0.99) When considering the lower bound of the ICC 95% confidence intervals, 58%, 66%, 54% and 29% reached 0.9 for the CE-T1W, DWI, T2W and ADC features, respectively. The feature class with the best reproducibility differed per sequence. CONCLUSIONS: The majority of MRI radiomics features from CE-T1W, T2W, DWI and ADC in colorectal liver metastases were robust for segmentation variability between readers. The CE-T1W yielded slightly better reproducibility results compared to DWI and T2W. The ADC features seem more susceptible to reader differences compared to the other three sequences.

Bowel Preparation Burden, Rectal Pain and Abdominal Discomfort: Perspective of Participants Undergoing CT Colonography and Colonoscopy.

OBJECTIVE: This study aimed to evaluate bowel preparation burden, rectal pain and abdominal discomfort levels and to determine the association between demographic characteristics and those levels among participants undergoing CT colonography and colonoscopy. METHODS: A cross-sectional survey was conducted in eligible Thai citizens who consented to participate all four visits of a free colorectal cancer screening protocol. Three levels (mild, moderate and severe) of burden, pain and discomfort were used to ask the perspective of participants at the final visit, one week after undergoing those two procedures. RESULTS: Data from 1,271 participants completed for analyses - females 815 (64.1%), males 456 (35.9%). The majority of participants experienced mild burden, pain and discomfort. Association between characteristic groups and burden levels differed regarding own income, chronic disease and laxative. Between characteristic groups and pain and discomfort levels differed regarding own income and chronic disease. Participants without their own income rated severe burden lower than those who had (p<0.001), but those without chronic disease rated moderate burden lower than who had (p=0.003). Participants prepared bowel with spilt-dose of PEG rated moderate burden higher than those who prepared with NaP (p<0.001). Participants undergoing CT colonography without their own income and presenting no chronic disease faced severe rectal pain lower than those who had (p<0.001 and p=0.04). Participants without their own income rated moderate and severe abdominal discomfort lower than those who had (p<0.01 and p=0.008). Participants undergoing colonoscopy without their own income and no chronic diseases faced severe rectal pain lower than those who had (p<0.001 and p=0.007). Participants without their own income and no chronic disease rated severe abdominal discomfort lower than those who had (p<0.001 and p=0.005). CONCLUSION: Evaluating the perspectives of customers alongside quality improvement and innovation to reduce unpleasant experiences remains needed in CT colonography and colonoscopy to promote CRC screening.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients.

BACKGROUND: About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection. AIM: To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery. METHODS: In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient. RESULTS: Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability. CONCLUSION: The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

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.