Imaging Characteristics of Liver Metastases Overlooked at Contrast-Enhanced CT

Factors associated with radiological misstaging of pancreatic ductal adenocarcinoma: A retrospective observational study

PURPOSE

Accurate staging of disease is vital in determining appropriate care for patients with pancreatic ductal adenocarcinoma (PDAC). It has been shown that the quality of scans and the experience of a radiologist can impact computed tomography (CT) based assessment of disease. The aim of the current study was to evaluate the impact of the rereading of outside hospital (OH) CT by an expert radiologist and a repeat pancreatic protocol CT (PPCT) on staging of disease.

METHODS

Patients evaluated at the our institute's pancreatic multidisciplinary clinic (2006 to 2014) with OH scan and repeat PPCT performed within 30 days were included. In-house radiologists staged disease using OH scans and repeat PPCT, and factors associated with misstaging were determined.

RESULTS

The study included 100 patients, with a median time between OH scan and PPCT of 19 days (IQR: 13-23 days.) Stage migration was mostly accounted for by upstaging of disease (58.8 % to 83.3 %) in all comparison groups. When OH scans were rereviewed, 21.5 % of the misstaging was due to missed metastases, however, when rereads were compared to the PPCT, occult metastases accounted for the majority of misstaged patients (62.5 %). Potential factors associated with misstaging were primarily related to imaging technique.

CONCLUSION

A repeat PPCT results in increased detection of metastatic disease that rereviews of OH scans may otherwise miss. Accessible insurance coverage for repeat PPCT imaging even within 30 days of an OH scan could help optimize delivery of care and alleviate burdens associated with misstaging.

Distinct CT imaging features of new liver metastases from primary genitourinary cancers

PURPOSE

To apply natural language processing (NLP) to a large volume of structured radiology reports in the investigation of CT imaging features of new liver metastases from primary genitourinary cancers.

METHODS

In this retrospective study, a previously reported NLP model was applied to consecutive structured CT reports from 2016 to 2022 to predict those patients with primary genitourinary cancer who developed liver metastasis. Pathology or imaging follow-up served as the reference standard for validating NLP predictions. Subsequently, diagnostic CTs of the identified patients were qualitatively assessed by two radiologists, whereby several imaging features of new liver metastasis were assessed. Proportions of the assessed imaging features were compared between primary genitourinary cancers using the Chi-square or Fisher's exact test.

RESULTS

In 112 patients (mean age = 72 years; 83 males), the majority of new liver metastases were hypovascular (73.2%), well defined (76.6%), homogenous (66.9%), and without necrotic/cystic component (73.2%). There was a higher proportion of iso- to hyperdense liver metastases for primary kidney cancer vs other primary genitourinary cancers (42.5% in kidney cancer; 2.3% in ureter/bladder cancer, 8% in prostate cancer, and 0% in testicular cancer; p < 0.05) and a higher proportion of new liver metastases with ill-defined margin for primary prostate cancer vs other primary genitourinary cancers (44.0% in prostate cancer, 15.0% in kidney cancer, 18.6% in ureter/bladder cancer, and 25.0% in testicular cancer; p < 0.05).

CONCLUSION

New liver metastases from primary genitourinary cancers tend to be hypovascular and show several distinct imaging features between different primary genitourinary cancers.

Detection of Moderate Hepatic Steatosis on Portal Venous Phase Contrast-Enhanced CT: Evaluation Using an Automated Artificial Intelligence Tool

BACKGROUND. Precontrast CT is an established means of evaluating for hepatic steatosis; postcontrast CT has historically been limited for this purpose. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of portal venous phase postcontrast CT in detecting at least moderate hepatic steatosis using liver and spleen attenuation measurements determined by an automated artificial intelligence (AI) tool. METHODS. This retrospective study included 2917 patients (1381 men, 1536 women; mean age, 56.8 years) who underwent a CT examination that included at least two series through the liver. Examinations were obtained from an AI vendor's data lake of data from 24 centers in one U.S. health care network and 29 centers in one Israeli health care network. An automated deep learning tool extracted liver and spleen attenuation measurements. The reference for at least moderate steatosis was precontrast liver attenuation of less than 40 HU (i.e., estimated liver fat > 15%). A radiologist manually reviewed examinations with outlier AI results to confirm portal venous timing and identify issues impacting attenuation measurements. RESULTS. After outlier review, analysis included 2777 patients with portal venous phase images. Prevalence of at least moderate steatosis was 13.9% (387/2777). Patients without and with at least moderate steatosis, respectively, had mean postcontrast liver attenuation of 104.5 ± 18.1 (SD) HU and 67.1 ± 18.6 HU (p < .001); a mean difference in postcontrast attenuation between the liver and the spleen (hereafter, postcontrast liver-spleen attenuation difference) of -7.6 ± 16.4 (SD) HU and -31.8 ± 20.3 HU (p < .001); and mean liver enhancement of 49.3 ± 15.9 (SD) HU versus 38.6 ± 13.6 HU (p < .001). Diagnostic performance for the detection of at least moderate steatosis was higher for postcontrast liver attenuation (AUC = 0.938) than for the postcontrast liver-spleen attenuation difference (AUC = 0.832) (p < .001). For detection of at least moderate steatosis, postcontrast liver attenuation had sensitivity and specificity of 77.8% and 93.2%, respectively, at less than 80 HU and 90.5% and 78.4%, respectively, at less than 90 HU; the postcontrast liver-spleen attenuation difference had sensitivity and specificity of 71.4% and 79.3%, respectively, at less than -20 HU and 87.0% and 62.1%, respectively, at less than -10 HU. CONCLUSION. Postcontrast liver attenuation outperformed the postcontrast liver-spleen attenuation difference for detecting at least moderate steatosis in a heterogeneous patient sample, as evaluated using an automated AI tool. Splenic attenuation likely is not needed to assess for at least moderate steatosis on postcontrast images. CLINICAL IMPACT. The technique could promote early detection of clinically significant nonalcoholic fatty liver disease through individualized or large-scale opportunistic evaluation.

Liver metastases: The role of magnetic resonance imaging

The liver is one of the organs most commonly involved in metastatic disease, especially due to its unique vascularization. It's well known that liver metastases represent the most common hepatic malignant tumors. From a practical point of view, it's of utmost importance to evaluate the presence of liver metastases when staging oncologic patients, to select the best treatment possible, and finally to predict the overall prognosis. In the past few years, imaging techniques have gained a central role in identifying liver metastases, thanks to ultrasonography, contrast-enhanced computed tomography (CT), and magnetic resonance imaging (MRI). All these techniques, especially CT and MRI, can be considered the non-invasive reference standard techniques for the assessment of liver involvement by metastases. On the other hand, the liver can be affected by different focal lesions, sometimes benign, and sometimes malignant. On these bases, radiologists should face the differential diagnosis between benign and secondary lesions to correctly allocate patients to the best management. Considering the above-mentioned principles, it's extremely important to underline and refresh the broad spectrum of liver metastases features that can occur in everyday clinical practice. This review aims to summarize the most common imaging features of liver metastases, with a special focus on typical and atypical appearance, by using MRI.

Exploiting the Potential of Photon-Counting CT in Abdominal Imaging

ABSTRACT

Photon-counting computed tomography (PCCT) imaging uses a new detector technology to provide added information beyond what can already be obtained with current CT and MR technologies. This review provides an overview of PCCT of the abdomen and focuses specifically on applications that benefit the most from this new imaging technique. We describe the requirements for a successful abdominal PCCT acquisition and the challenges for clinical translation. The review highlights work done within the last year with an emphasis on new protocols that have been tested in clinical practice. Applications of PCCT include imaging of cystic lesions, sources of bleeding, and cancers. Photon-counting CT is positioned to move beyond detection of disease to better quantitative staging of disease and measurement of treatment response.

Postoperative liver dysfunction is associated with poor long-term outcomes in patients with colorectal cancer: a retrospective cohort study

BACKGROUND

Postoperative hepatobiliary enzyme abnormalities often present as postoperative liver dysfunction in patients with colorectal cancer. This study aimed to clarify the risk factors of postoperative liver dysfunction and its prognostic impact following colorectal cancer surgery.

METHODS

We retrospectively analyzed data from 360 consecutive patients who underwent radical resection for Stage I-IV colorectal cancer between 2015 and 2019. A subset of 249 patients with Stage III colorectal cancer were examined to assess the prognostic impact of liver dysfunction.

RESULTS

Forty-eight (13.3%) colorectal cancer patients (Stages I-IV) developed postoperative liver dysfunction (Common Terminology Criteria for Adverse Events version 5.0 CTCAE v5.0 ≥ Grade 2). Univariate and multivariate analyses identified the liver-to-spleen ratio on preoperative plain computed tomography (L/S ratio; P = 0.002, Odds ratio 2.66) as an independent risk factor for liver dysfunction. Patients with postoperative liver dysfunction showed significantly poorer disease-free survival than patients without liver dysfunction (P < 0.001). Univariate and multivariate analyses using Cox's proportional hazards model revealed that postoperative liver dysfunction independently was a poor prognostic factor (P = 0.001, Hazard ratio 2.75, 95% CI: 1.54-4.73).

CONCLUSIONS

Postoperative liver dysfunction was associated with poor long-term outcomes in patients with Stage III colorectal cancer. A low liver-to-spleen ratio on preoperative plain computed tomography images was an independent risk factor of postoperative liver dysfunction.

Artificial intelligence-powered software detected more than half of the liver metastases overlooked by radiologists on contrast-enhanced CT

PURPOSE

To evaluate the sensitivity of artificial intelligence (AI)-powered software in detecting liver metastases, especially those overlooked by radiologists.

METHODS

Records of 746 patients diagnosed with liver metastases (November 2010-September 2017) were reviewed. Images from when radiologists first diagnosed liver metastases were reviewed, and prior contrast-enhanced CT (CECT) images were checked for availability. Two abdominal radiologists classified the lesions into overlooked lesions (all metastases missed by radiologists on prior CECT) and detected lesions (all metastases if any of them were correctly identified and invisible on prior CECT or those with no prior CECT). Finally, images from 137 patients were identified, 68 of which were classified as "overlooked cases." The same radiologists created the ground truth for these lesions and compared them with the software's output at 2-month intervals. The primary endpoint was the sensitivity in detecting all liver lesion types, liver metastases, and liver metastases overlooked by radiologists.

RESULTS

The software successfully processed images from 135 patients. The per-lesion sensitivity for all liver lesion types, liver metastases, and liver metastases overlooked by radiologists was 70.1%, 70.8%, and 55.0%, respectively. The software detected liver metastases in 92.7% and 53.7% of patients in detected and overlooked cases, respectively. The average number of false positives was 0.48 per patient.

CONCLUSION

The AI-powered software detected more than half of liver metastases overlooked by radiologists while maintaining a relatively low number of false positives. Our results suggest the potential of AI-powered software in reducing the frequency of overlooked liver metastases when used in conjunction with the radiologists' clinical interpretation.

Original research: utilization of a convolutional neural network for automated detection of lytic spinal lesions on body CTs

OBJECTIVE

To develop, train, and test a convolutional neural network (CNN) for detection of spinal lytic lesions in chest, abdomen, and pelvis CT scans.

MATERIALS AND METHODS

Cases of malignant spinal lytic lesions in CT scans were identified. Images were manually segmented for the following classes: (i) lesion, (ii) normal bone, (iii) background. If more than one lesion was on a single slice, all lesions were segmented. Images were stored as 128×128 pixel grayscale, with 10% segregated for testing. The training pipeline of the dataset included histogram equalization and data augmentation. A model was trained on Keras/Tensorflow using an 80/20 training/validation split, based on U-Net architecture. Additional testing of the model was performed on 1106 images of healthy controls. Global sensitivity measured detection of any lesion on a single image. Local sensitivity and positive predictive value (PPV) measured detection of all lesions on an image. Global specificity measured false positive rate in non-pathologic bone.

RESULTS

Six hundred images were obtained for model creation. The training set consisted of 540 images, which was augmented to 20,000. The test set consisted of 60 images. Model training was performed in triplicate. Mean Dice scores were 0.61 for lytic lesion, 0.95 for normal bone, and 0.99 for background. Mean global sensitivity was 90.6%, local sensitivity was 74.0%, local PPV was 78.3%, and global specificity was 63.3%. At least one false positive lesion was noted in 28.8-44.9% of control images.

CONCLUSION

A task-trained CNN showed good sensitivity in detecting spinal lytic lesions in axial CT images.

Analysis of "visible in retrospect" to monitor false-negative findings in radiological reports

PURPOSE

False-negative findings in radiological reports can lead to serious adverse patient outcomes. We determined the frequency and tendency of false-negative findings in radiological reports by searching for words related to "visible in retrospect".

METHODS

In the period of 34 months, we extracted radiological reports containing words related to "visible in retrospect". Of these reports, we extracted false-negative findings that were not described in past reports and were first detected retrospectively. Misinterpretations were excluded. The occurrences of the terms that we identified were analyzed by all examinations, modality, month, and anatomical and lesion classifications were analyzed.

RESULTS

Of the 135,251 examinations, 941 reports (0.71%) with 962 findings were detected, with an average of 1.4 findings per business day. By modality, 713 of 81,899 (0.87%) CT examinations, 208 of 36,174 (0.57%) MR, 34 of 9,585 (0.35%) FDG-PET-CT, 2 of 2,258 (0.09%) digital radiography, and 5 of 5,335 (0.09%) other nuclear medicine examinations were found. By anatomical classification, there were 383 (40%) in chest, 353 (37%) in abdomen, 162 (17%) in head, 42 (4.4%) in face and neck, 9 (0.93%) in extremity, and 13 (1.4%) in others. By lesion classification, we identified 665 (69%) for localized lesion, 170 (18%) for vascular lesion, 83 (8.6%) for inflammatory lesion, 14 (1.5%) for traumatic lesion, 12 (1.2%) for organ dysfunction, 11 (1.1%) for degenerative lesion, and 7 (0.7%) for the others. Notable high-frequency specific site diseases by modality were 210 (22%) of localized lesions in lung on CT.

CONCLUSION

Our results demonstrated that missed lung localized lesions on CT, which account for about a fifth of false-negative findings, were the most common false-negative finding.

Individualized and generalized models for predicting observer performance on liver metastasis detection using CT

Purpose: Radiologists exhibit wide inter-reader variability in diagnostic performance. This work aimed to compare different feature sets to predict if a radiologist could detect a specific liver metastasis in contrast-enhanced computed tomography (CT) images and to evaluate possible improvements in individualizing models to specific radiologists. Approach: Abdominal CT images from 102 patients, including 124 liver metastases in 51 patients were reconstructed at five different kernels/doses using projection domain noise insertion to yield 510 image sets. Ten abdominal radiologists marked suspected metastases in all image sets. Potentially salient features predicting metastasis detection were identified in three ways: (i) logistic regression based on human annotations (semantic), (ii) random forests based on radiologic features (radiomic), and (iii) inductive derivation using convolutional neural networks (CNN). For all three approaches, generalized models were trained using metastases that were detected by at least two radiologists. Conversely, individualized models were trained using each radiologist's markings to predict reader-specific metastases detection. Results: In fivefold cross-validation, both individualized and generalized CNN models achieved higher area under the receiver operating characteristic curves (AUCs) than semantic and radiomic models in predicting reader-specific metastases detection ability ( p < 0.001 ). The individualized CNN with an AUC of mean (SD) 0.85(0.04) outperformed the generalized one [ AUC = 0.78 ( 0.06 ) , p = 0.004 ]. The individualized semantic [ AUC = 0.70 ( 0.05 ) ] and radiomic models [ AUC = 0.68 ( 0.06 ) ] outperformed the respective generalized versions [semantic AUC = 0.66 ( 0.03 ) , p = 0.009 ; radiomic AUC = 0.64 ( 0.06 ) , p = 0.03 ]. Conclusions: Individualized models slightly outperformed generalized models for all three feature sets. Inductive CNNs were better at predicting metastases detection than semantic or radiomic features. Generalized models have implementation advantages when individualized data are unavailable.

Pancreatic Adenocarcinoma: Diagnostic Errors, Contributing Factors and Solutions

The purpose of this article is to review diagnostic errors in preoperative and post-operative imaging for pancreatic ductal adenocarcinoma (PDAC), discuss contributing factors, and provide solutions that minimize errors. Accurate radiological staging and restaging of PDAC dictates surgical management and errors can have significant negative effects on patient care, such as missed vessel involvement or metastatic disease that would preclude surgery. Familiarity with these errors and their contributing factors improves diagnostic accuracy and ultimately leads to improved patient outcomes.

Clinical characteristics and CT features of hepatic epithelioid haemangioendothelioma and comparison with those of liver metastases

BACKGROUND

To analyse clinical characteristics and computer tomography (CT) findings of hepatic epithelioid haemangioendothelioma (HEH) and to determine differential features compared with liver metastasis (LM).

METHODS

This retrospective study included 80 patients with histopathologically confirmed HEH (n = 20) and LM (n = 60) of different primary tumours who underwent dynamic contrast-enhanced CT scans. CT findings included the location, contour, size, number, margin, and density of lesions, the patterns and degree of contrast enhancement of lesions, vascular invasion and changes in other organs. The enhancement ratio (ER) and tumour-to-normal parenchyma ratio (TNR) were calculated. Receiver operating characteristic curves (ROCs) were used to determine areas under the curve (AUCs).

RESULTS

About 65% of HEH lesions were located in submarginal areas. Significant differences were observed between HEH and LM patients in age, sex, and tumour marker positivity (p < 0.05). HEH showed minimal to slight enhancement, thin ring-like enhancement in arterial phase, and slight, homogeneous, progressive enhancement in the portal phase. HEH presented capsule retraction, and the "target" sign and the "lollipop" sign were significantly more frequent than in LM (p < 0.05). The ER and TNR in the arterial phase of HEH were lower than those of LM (p < 0.05). AUCs of ER and TNR in the arterial phase were 0.74 and 0.73, respectively.

CONCLUSION

Lesions in subcapsular locations, capsular retraction, slight and thin ring-like enhancement, "target" and "lollipop" signs and lower ER and TNR in the arterial phase may represent important features of HEH compared with LM.

Deep-learning model observer for a low-contrast hepatic metastases localization task in computed tomography

PURPOSE

Conventional model observers (MO) in CT are often limited to a uniform background or varying background that is random and can be modeled in an analytical form. It is unclear if these conventional MOs can be readily generalized to predict human observer performance in clinical CT tasks that involve realistic anatomical background. Deep-learning-based model observers (DL-MO) have recently been developed, but have not been validated for challenging low contrast diagnostic tasks in abdominal CT. We consequently sought to validate a DL-MO for a low-contrast hepatic metastases localization task.

METHODS

We adapted our recently developed DL-MO framework for the liver metastases localization task. Our previously-validated projection-domain lesion-/noise-insertion techniques were used to synthesize realistic positive and low-dose abdominal CT exams, using the archived patient projection data. Ten experimental conditions were generated, which involved different lesion sizes/contrasts, radiation dose levels, and image reconstruction types. Each condition included 100 trials generated from a patient cohort of 7 cases. Each trial was presented as liver image patches (160×160×5 voxels). The DL-MO performance was calculated for each condition and was compared with human observer performance, which was obtained by three sub-specialized radiologists in an observer study. The performance of DL-MO and radiologists was gauged by the area under localization receiver-operating-characteristic curves. The generalization performance of the DL-MO was estimated with the repeated twofold cross-validation method over the same set of trials used in the human observer study. A multi-slice Channelized Hoteling Observers (CHO) was compared with the DL-MO across the same experimental conditions.

RESULTS

The performance of DL-MO was highly correlated to that of radiologists (Pearson's correlation coefficient: 0.987; 95% CI: [0.942, 0.997]). The performance level of DL-MO was comparable to that of the grouped radiologists, that is, the mean performance difference was -3.3%. The CHO performance was poorer than the grouped radiologist performance, before internal noise could be added. The correlation between CHO and radiologists was weaker (Pearson's correlation coefficient: 0.812, and 95% CI: [0.378, 0.955]), and the corresponding performance bias (-29.5%) was statistically significant.

CONCLUSION

The presented study demonstrated the potential of using the DL-MO for image quality assessment in patient abdominal CT tasks.

Imaging evaluation of the liver in oncology patients: A comparison of techniques

The liver is commonly affected by metastatic disease. Therefore, it is essential to detect and characterize liver metastases, assuming that patient management and prognosis rely on it. The imaging techniques that allow non-invasive assessment of liver metastases include ultrasonography, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET)/CT, and PET/MRI. In this paper, we review the imaging findings of liver metastases, focusing on each imaging modality’s advantages and potential limitations. We also assess the importance of different imaging modalities for the management, follow-up, and therapy response of liver metastases. To date, both CT and MRI are the most appropriate imaging methods for initial lesion detection, follow-up, and assessment of treatment response. Multiparametric MRI is frequently used as a problem-solving technique for liver lesions and has evolved substantially over the past decade, including hardware and software developments and specific intravenous contrast agents. Several studies have shown that MRI performs better in small-sized metastases and moderate to severe liver steatosis cases. Although state-of-the-art MRI shows a greater sensitivity for detecting and characterizing liver metastases, CT remains the chosen method. We also present the controversial subject of the "economic implication" to use CT over MRI.

Liver Enhancement on Computed Tomography Is Suboptimal in Patients with Liver Steatosis

This study's aim was twofold. Firstly, to assess liver enhancement quantitatively and qualitatively in steatotic livers compared to non-steatotic livers on portal venous computed tomography (CT). Secondly, to determine the injection volume of contrast medium in patients with severe hepatic steatosis to improve the image quality of the portal venous phase. We retrospectively included patients with non-steatotic (n = 70), the control group, and steatotic livers (n = 35) who underwent multiphase computed tomography between March 2016 and September 2020. Liver enhancement was determined by the difference in attenuation in Hounsfield units (HU) between the pre-contrast and the portal venous phase, using region of interests during in three different segments. Liver steatosis was determined by a mean attenuation of ≤40 HU on unenhanced CT. Adequate enhancement was objectively defined as ≥50 ΔHU and subjectively using a three-point Likert scale. Enhancement of non-steatotic and steatotic livers were compared and associations between enhancement and patient- and scan characteristics were analysed. Enhancement was significantly higher among the control group (mean 51.9 ± standard deviation 11.5 HU) compared to the steatosis group (40.6 ± 8.4 HU p for difference < 0.001). Qualitative analysis indicated less adequate enhancement in the steatosis group: 65.7% of the control group was rated as good vs. 8.6% of the steatosis group. We observed a significant correlation between enhancement, and presence/absence of steatosis and grams of iodine per total body weight (TBW) (p < 0.001; adjusted R2 = 0.303). Deduced from this correlation, theoretical contrast dosing in grams of Iodine (g I) can be calculated: g I = 0.502 × TBW for non-steatotic livers and g I = 0.658 × TBW for steatotic livers. Objective and subjective enhancement during CT portal phase were significantly lower in steatotic livers compared to non-steatotic livers, which may have consequences for detectability and contrast dosing.

Follow-up of colorectal cancer and patterns of recurrence

Colorectal cancer is one of the commonest cancers detected as also amongst the most common causes of cancer death. Survival has improved due to better disease understanding and treatment; however, a substantial proportion of patients recur after curative intent therapy. In this article, we will discuss the imaging features of recurrent colorectal cancer and the role of the radiologist in its management.

CT Imaging Characteristics and Influence Factors of Renal Dialysis-Associated Peritoneal Injury

Peritoneal dialysis (PD), as one of the main renal replacement modalities for end-stage renal disease, gets the advantages of better protection of residual renal function and better quality of survival. However, ultrafiltration failure after peritoneal injury is an important reason for patients to withdraw from PD treatment. Peritonitis is a major complication of peritoneal dialysis, which results in an accelerated process of peritoneal injury due to direct damage from acute inflammation and local release of cytokine TGF-β. In this paper, the application of ultrasound to examine the peritoneum revealed a positive correlation between peritoneal thickness and the development of peritonitis. The results of this study also further confirmed the effect of peritonitis on peritoneal thickening. A multifactorial regression analysis also revealed that peritonitis and its severity were independent risk factors for peritoneal thickening and omental structural abnormalities. This paper reported a correlation between mural peritoneal thickness and peritoneal transit function. In this study, patients with high peritoneal transit and high mean transit were found to be more prone to omental structural abnormalities than patients with low mean and low transit and a higher proportion of patients with mural peritoneal thickening, but this did not reach statistical significance, which may be related to the still small number of cases.

Baseline liver steatosis has no impact on liver metastases and overall survival in rectal cancer patients

Background

The liver is one of the most frequent sites of metastases in rectal cancer. This study aimed to evaluate how the development of synchronous or metachronous liver metastasis and overall survival are impacted by baseline liver steatosis and chemotherapy-induced liver damage in rectal cancer patients.

Methods

Patients diagnosed with stage II to IV rectal cancer between 2010 and 2016 in our province with suitable baseline CT scan were included. Data on cancer diagnosis, staging, therapy, outcomes and liver function were collected. CT scans were retrospectively reviewed to assess baseline steatosis (liver density < 48 HU and/or liver-to-spleen ratio < 1.1). Among patients without baseline steatosis and treated with neoadjuvant chemotherapy, chemotherapy-induced liver damage was defined as steatosis appearance, ≥ 10% liver volume increase, or significant increase in liver function tests.

Results

We included 283 stage II to IV rectal cancer patients with suitable CT scan (41% females; mean age 68 ± 14 years). Steatosis was present at baseline in 90 (31.8%) patients, synchronous liver metastasis in 42 (15%) patients and metachronous liver metastasis in 26 (11%); 152 (54%) deaths were registered. The prevalence of synchronous liver metastasis was higher in patients with steatosis (19% vs 13%), while the incidence of metachronous liver metastasis was similar. After correcting for age, sex, stage, and year of diagnosis, steatosis was not associated with metachronous liver metastasis nor with overall survival. In a small analysis of 63 patients without baseline steatosis and treated with neoadjuvant chemotherapy, chemotherapy-induced liver damage was associated with higher incidence of metachronous liver metastasis and worse survival, results which need to be confirmed by larger studies.

Conclusions

Our data suggest that rectal cancer patients with steatosis had a similar occurrence of metastases during follow-up, even if the burden of liver metastases at diagnosis was slightly higher, compatible with chance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07980-9.

Virtual unenhanced imaging of the liver derived from 160-mm rapid-switching dual-energy CT (rsDECT): Comparison of the accuracy of attenuation values and solid liver lesion conspicuity with native unenhanced images

OBJECTIVES

To evaluate the reliability of attenuation values of the liver parenchyma and focal liver lesions on virtual unenhanced images from arterial (VUEart) and portal venous phases (VUEport) compared to native unenhanced (NU) attenuation values in patients referred for assessment of malignant liver lesions.

METHODS

Seventy-three patients with confirmed primary or metastatic liver tumors who underwent a multiphase contrast-enhanced rapid-switching kVp dual-energy CT (rsDECT) were included in this IRB-approved retrospective study. Both qualitative and quantitative analyses - including the lesion-to-liver contrast-to-noise ratio (LL-CNR) - were performed and compared between NU and both VUEart and VUEport images.

RESULTS

The mean liver attenuation values were significantly lower in VUEart images (56.7 ± 6.7 HU) than in NU images (59.6 ± 7.5 HU, p = 0.008), and were comparable between VUEart and VUEport images (57.9 ± 6 UH, p = 0.38) and between VUEport and NU images (p = 0.051). The mean liver lesions attenuation values were comparable between NU, VUEart and VUEport images (p = 0.60). Strong and significant correlations values were found both in liver lesions and tumor-free parenchyma (r = 0.82-0.91, p < 0.01). The mean LL-CNR was significantly higher in VUEart and VUEport images than in NU images (1.7 ± 1 and 1.6 ± 1.1 vs 0.9 ± 0.6; p < 0.001), but was comparable between VUEart and VUEport images (p > 0.9). Lesion conspicuity was significantly higher in VUEport images than in NU images (p < 0.001).

CONCLUSION

VUEport images derived from 3rd generation rsDECT could confidently replace NU images in patients undergoing assessment for malignant liver lesions. These images provide comparable attenuation values in both liver lesions and liver parenchyma while reducing the radiation dose and scanning time.

Development and assessment of an individualized nomogram to predict colorectal cancer liver metastases

Background

This article aims to develop and assess the radiomics paradigm for predicting colorectal cancer liver metastasis (CRLM) from the primary tumor.

Methods

This retrospective study included 100 patients from the First Hospital of Jilin University from June 2017 to December 2017. The 100 patients comprised 50 patients with and 50 without CRLM. The maximum-level enhanced computed tomography (CT) image of primary cancer in the portal venous phase of each patient was selected as the original image data. To automatically implement radiomics-related paradigms, we developed a toolkit called Radiomics Intelligent Analysis Toolkit (RIAT).

Results

With RIAT, the model based on logistic regression (LR) using both the radiomics and clinical information signatures showed the maximum net benefit. The area under the curve (AUC) value was 0.90±0.02 (sensitivity =0.85±0.02, specificity =0.79±0.04) for the training set, 0.86±0.11 (sensitivity =0.85±0.09, specificity =0.75±0.19) for the verification set, 0.906 (95% CI, 0.840-0.971; sensitivity =0.81, specificity =0.84) for the cross-validation set, and 0.899 (95% CI, 0.761-1.000; sensitivity =0.78, specificity =0.91) for the test set.

Conclusions

The radiomics nomogram-based LR with clinical risk and radiomics features allows for a more accurate classification of CRLM using CT images with RIAT.