A retrospective single-centre analysis of the oncological impact of LI-RADS classification applied to Metroticket 2.0 calculator in liver transplantation: every nodule matters

Although the diagnostic value of Liver Imaging Reporting and Data System (LI-RADS) protocol is well recognized in clinical practice, its role in liver transplant (LT) setting is under-explored. We sought to evaluate the oncological impact of LI-RADS classification applied to Metroticket 2.0 calculator in a single-centre retrospective cohort of transplanted hepatocellular carcinoma (HCC) patients, exploring which LI-RADS subclasses need to be considered in order to grant the best Metroticket 2.0 performance. The most recent pre-LT imaging of 245 patients undergoing LT for HCC between 2005 and 2015 was retrospectively and blindly reviewed, classifying all nodules according to LI-RADS protocol. Metroticket 2.0 accuracy was subsequently tested incorporating all vital nodules identified during multi-disciplinary team (MDT) meetings attended before LI-RADS reclassification of the latest pre-LT imaging, LR-5 and LR-treatment-viable (LR-TR-V), LR-4/5 and LR-TR-V, and LR-3/4/5 and LR-TR-V nodules respectively. Considering their extremely low probability for harbouring HCC, LR-1 and LR-2 nodules were not considered in this analysis. Incorporation of all HCCs identified during MDT meetings attended before LI-RADS reclassification of the latest pre-LT imaging resulted in a Metroticket 2.0 c-index of 0.72, [95% confidence interval (CI) 0.64-0.80]. Metroticket 2.0 c-index dropped to 0.60 [95% CI: 0.48-0.72] when LI-RADS-5 and LI-RADS-TR-V (P = 0.0089) or LI-RADS-5, LI-RADS-4 and LI-RADS-TR-V (P = 0.0068) nodules were entered in the calculator. Conversely, addition of LI-RADS-3 HCCs raised the Metroticket 2.0 c-index to 0.65 [95% CI: 0.54-0.86], resulting in a not statistically significant diversion from the original performance (0.72 vs. 0.65; P = 0.08). Exclusion of LR-3 and LR-4 nodules from Metroticket 2.0 calculator resulted in a significant drop in its accuracy. Every nodule with an intermediate-to-high probability of harbouring HCC according to LI-RADS protocol seems to contribute to tumour burden and should be entered in the Metroticket 2.0 calculator in order to grant appropriate performance.

Inter-center agreement of mRECIST in transplanted patients for hepatocellular carcinoma

OBJECTIVES

To evaluate the inter-observer reliability of modified Response Evaluation Criteria In Solid Tumours (mRECIST) of patients with hepatocellular carcinoma (HCC) undergoing neo-adjuvant treatments before liver transplant (LT). The agreement of tumor number, size, transplant criteria, and the radiological-pathological concordance were also assessed.

METHODS

A total of 180 radiological studies before/after neo-adjuvant therapies performed on 90 patients prior to LT were reviewed from three expert centers. Kappa-statistic and intraclass correlation (ICC) were evaluated on mRECIST and on tumoral features. Complete radiological response (CR) was compared with complete pathological response (CPR).

RESULTS

Before neo-adjuvant therapies, the agreement on tumor number, size, and transplant criteria ranged from moderate (defined as ICC of 0.41-0.60) to almost perfect (ICC of 0.81-0.99), being higher with magnetic resonance imaging (MRI) than CT (0.657-0.899 and 0.422-0.776, respectively). After neo-adjuvant therapies, the agreement decreased, as ICCs ranged between 0.518 and 0.663 with MRI and between 0.508 and 0.677 with CT. Concordant mRECIST pairs were 201 of 270 reviews (76.3%) with a kappa of 0.648 indicating substantial agreement. When the three observers completely agreed on CR, the positive predictive value for CPR was 51.6%. The negative predictive value was 94.2% with a kappa of 0.512 indicating fair agreement between radiology and pathology.

CONCLUSIONS

mRECIST agreement was substantial among the three observers involved. The agreement on tumor number, size, and transplant criteria ranged from moderate to almost perfect, with the highest ICCs obtained with MRI before neo-adjuvant therapies. Finally, the predictive value of mRECIST in the diagnosis of CPR was only fair.

KEY POINTS

• The review of 180 radiological exams of patients with hepatocellular carcinoma before and after neo-adjuvant therapies showed that the concordance among three different raters on mRECIST diagnosis was substantial. • The inter-observer reliability on fulfilment of transplant criteria slightly decreased when evaluated through CT and after loco-regional therapies. • The radiological diagnosis of complete response after neo-adjuvant therapies was predictive of complete pathological response in only 51.6% of cases.

Machine Learning to Predict In-Hospital Mortality in COVID-19 Patients Using Computed Tomography-Derived Pulmonary and Vascular Features

Pulmonary parenchymal and vascular damage are frequently reported in COVID-19 patients and can be assessed with unenhanced chest computed tomography (CT), widely used as a triaging exam. Integrating clinical data, chest CT features, and CT-derived vascular metrics, we aimed to build a predictive model of in-hospital mortality using univariate analysis (Mann-Whitney U test) and machine learning models (support vectors machines (SVM) and multilayer perceptrons (MLP)). Patients with RT-PCR-confirmed SARS-CoV-2 infection and unenhanced chest CT performed on emergency department admission were included after retrieving their outcome (discharge or death), with an 85/15% training/test dataset split. Out of 897 patients, the 229 (26%) patients who died during hospitalization had higher median pulmonary artery diameter (29.0 mm) than patients who survived (27.0 mm, p < 0.001) and higher median ascending aortic diameter (36.6 mm versus 34.0 mm, p < 0.001). SVM and MLP best models considered the same ten input features, yielding a 0.747 (precision 0.522, recall 0.800) and 0.844 (precision 0.680, recall 0.567) area under the curve, respectively. In this model integrating clinical and radiological data, pulmonary artery diameter was the third most important predictor after age and parenchymal involvement extent, contributing to reliable in-hospital mortality prediction, highlighting the value of vascular metrics in improving patient stratification.

CT-derived Chest Muscle Metrics for Outcome Prediction in Patients with COVID-19

Background Lower muscle mass is a known predictor of unfavorable outcomes, but its prognostic impact on patients with COVID-19 is unknown. Purpose To investigate the contribution of CT-derived muscle status in predicting clinical outcomes in patients with COVID-19. Materials and Methods Clinical or laboratory data and outcomes (intensive care unit [ICU] admission and death) were retrospectively retrieved for patients with reverse transcriptase polymerase chain reaction-confirmed SARS-CoV-2 infection, who underwent chest CT on admission in four hospitals in Northern Italy from February 21 to April 30, 2020. The extent and type of pulmonary involvement, mediastinal lymphadenopathy, and pleural effusion were assessed. Cross-sectional areas and attenuation by paravertebral muscles were measured on axial CT images at the T5 and T12 vertebral level. Multivariable linear and binary logistic regression, including calculation of odds ratios (ORs) with 95% CIs, were used to build four models to predict ICU admission and death, which were tested and compared by using receiver operating characteristic curve analysis. Results A total of 552 patients (364 men and 188 women; median age, 65 years [interquartile range, 54-75 years]) were included. In a CT-based model, lower-than-median T5 paravertebral muscle areas showed the highest ORs for ICU admission (OR, 4.8; 95% CI: 2.7, 8.5; P < .001) and death (OR, 2.3; 95% CI: 1.0, 2.9; P = .03). When clinical variables were included in the model, lower-than-median T5 paravertebral muscle areas still showed the highest ORs for both ICU admission (OR, 4.3; 95%: CI: 2.5, 7.7; P < .001) and death (OR, 2.3; 95% CI: 1.3, 3.7; P = .001). At receiver operating characteristic analysis, the CT-based model and the model including clinical variables showed the same area under the receiver operating characteristic curve (AUC) for ICU admission prediction (AUC, 0.83; P = .38) and were not different in terms of predicting death (AUC, 0.86 vs AUC, 0.87, respectively; P = .28). Conclusion In hospitalized patients with COVID-19, lower muscle mass on CT images was independently associated with intensive care unit admission and in-hospital mortality. © RSNA, 2021 Online supplemental material is available for this article.

Post-surgical Achilles calcific tendinopathy treated with ultrasound-guided percutaneous irrigation

Calcific tendinopathy typically affects the shoulder rotator cuff tendons. Its management includes shock wave therapy and US-guided percutaneous irrigation, with surgery being less and less used. Extra-shoulder calcific tendinopathy is relatively infrequent and typically affects the hip. While the diagnostic techniques and the treatment options for shoulder calcific tendinopathy have been extensively described, there are only anecdotic reports on the other sites. In this paper, we have reported an unusual case of non-insertional Achilles calcific tendinopathy which occurred many years after Achilles surgical repair. This condition, which presented similar appearance to that of the rotator cuff calcific tendinopathy, is totally different from the well-known and more common insertional calcific Achilles tendinopathy in terms of pathophysiological, imaging, and clinical findings. Further, we have shown that US-guided percutaneous irrigation might be a safe, technically feasible, mini-invasive, and effective treatment also for Achilles calcific tendinopathy.

Including mRECIST in the Metroticket 2.0 criteria improves prediction of hepatocellular carcinoma-related death after liver transplant

BACKGROUND & AIMS

In the context of liver transplantation (LT) for hepatocellular carcinoma (HCC), prediction models are used to ensure that the risk of post-LT recurrence is acceptably low. However, the weighting that 'response to neoadjuvant therapies' should have in such models remains unclear. Herein, we aimed to incorporate radiological response into the Metroticket 2.0 model for post-LT prediction of "HCC-related death", to improve its clinical utility.

METHODS

Data from 859 transplanted patients (2000-2015) who received neoadjuvant therapies were included. The last radiological assessment before LT was reviewed according to the modified RECIST criteria. Competing-risk analysis was applied. The added value of including radiological response into the Metroticket 2.0 was explored through category-based net reclassification improvement (NRI) analysis.

RESULTS

At last radiological assessment prior to LT, complete response (CR) was diagnosed in 41.3%, partial response/stable disease (PR/SD) in 24.9% and progressive disease (PD) in 33.8% of patients. The 5-year rates of "HCC-related death" were 3.1%, 9.6% and 13.4% in those with CR, PR/SD, or PD, respectively (p <0.001). Log₁₀AFP (p <0.001) and the sum of number and diameter of the tumour/s (p <0.05) were determinants of "HCC-related death" for PR/SD and PD patients. To maintain the post-LT 5-year incidence of "HCC-related death" <30%, the Metroticket 2.0 criteria were restricted in some cases of PR/SD and in all cases with PD, correctly reclassifying 9.4% of patients with "HCC-related death", at the expense of 3.5% of patients who did not have the event. The overall/net NRI was 5.8.

CONCLUSION

Incorporating the modified RECIST criteria into the Metroticket 2.0 framework can improve its predictive ability. The additional information provided can be used to better judge the suitability of candidates for LT following neoadjuvant therapies.

LAY SUMMARY

In the context of liver transplantation for patients with hepatocellular carcinoma, prediction models are used to ensure that the risk of recurrence after transplantation is acceptably low. The Metroticket 2.0 model has been proposed as an accurate predictor of "tumour-related death" after liver transplantation. In the present study, we show that its accuracy can be improved by incorporating information relating to the radiological responses of patients to neoadjuvant therapies.