Sarcopenia and Patient's Body Composition: New Morphometric Tools to Predict Clinical Outcome After Ivor Lewis Esophagectomy: a Multicenter Study

Early regression index (ERI) on MR images as response predictor in esophageal cancer treated with neoadjuvant chemo-radiotherapy: Interim analysis of the prospective ESCAPE trial

PURPOSE

The early regression index (ERI) predicts treatment response in rectal cancer patients. Aim of current study was to prospectively assess tumor response to neoadjuvant chemo-radiotherapy (nCRT) of locally advanced esophageal cancer using ERI, based on MRI.

MATERIAL AND METHODS

From January 2020 to May 2023, 30 patients with esophageal cancer were enrolled in a prospective study (ESCAPE). PET-MRI was performed: i) before nCRT (tpre); ii) at mid-radiotherapy, tmid; iii) after nCRT, 2-6 weeks before surgery (tpost); nCRT delivered 41.4 Gy/23fr with concurrent carboplatin and paclitaxel. For patients that skipped surgery, complete clinical response (cCR) was assessed if patients showed no local relapse after 18 months; patients with pathological complete response (pCR) or with cCR were considered as complete responders (pCR + cCR). GTV volumes were delineated by two observers (Vpre, Vmid, Vpost) on T2w MRI: ERI and other volume regression parameters at tmid and tpost were tested as predictors of pCR + cCR.

RESULTS

Complete data of 25 patients were available at the time of the analysis: 3/25 with complete response at imaging refused surgery and 2/3 were cCR; in total, 10/25 patients showed pCR + cCR (pCR = 8/22). Both ERImid and ERIpost classified pCR + cCR patients, with ERImid showing better performance (AUC:0.78, p = 0.014): A two-variable logistic model combining ERImid and Vpre improved performances (AUC:0.93, p < 0.0001). Inter-observer variability in contouring GTV did not affect the results.

CONCLUSIONS

Despite the limited numbers, interim analysis of ESCAPE study suggests ERI as a potential predictor of complete response after nCRT for esophageal cancer. Further validation on larger populations is warranted.

Correlation between sarcopenia and esophageal cancer: a narrative review

BACKGROUND

In recent years, the research on the relationship between sarcopenia before and after the treatment of esophageal cancer, as well as its impact on prognosis of esophageal cancer, has increased rapidly, which has aroused people's attention to the disease of patients with esophageal cancer complicated with sarcopenia. This review examines the prevalence of sarcopenia in patients with esophageal cancer, as well as the relationship between sarcopenia (before and after surgery or chemotherapy) and prognosis in patients with esophageal cancer. Moreover, we summarized the potential pathogenesis of sarcopenia and pharmacologic and non-pharmacologic therapies.

METHODS

A narrative review was performed in PubMed and Web of Science using the keywords ("esophageal cancer" or "esophageal neoplasm" or "neoplasm, esophageal" or "esophagus neoplasm" or "esophagus neoplasms" or "neoplasm, esophagus" or "neoplasms, esophagus" or "neoplasms, esophageal" or "cancer of esophagus" or "cancer of the esophagus" or "esophagus cancer" or "cancer, esophagus" or "cancers, esophagus" or "esophagus cancers" or "esophageal cancer" or "cancer, esophageal" or "cancers, esophageal" or "esophageal cancers") and ("sarcopenia" or "muscular atrophy" or "aging" or "senescence" or "biological aging" or "aging, biological" or "atrophies, muscular" or "atrophy, muscular" or "muscular atrophies" or "atrophy, muscle" or "atrophies, muscle" or "muscle atrophies"). Studies reporting relationship between sarcopenia and esophageal cancer were analyzed.

RESULTS

The results of the review suggest that the average prevalence of sarcopenia in esophageal cancer was 46.3% ± 19.6% ranging from 14.4 to 81% and sarcopenia can be an important predictor of poor prognosis in patients with esophageal cancer. Patients with esophageal cancer can suffer from sarcopenia due to their nutritional deficiencies, reduced physical activity, chemotherapy, and the effects of certain inflammatory factors and pathways. When classic diagnostic values for sarcopenia such as skeletal muscle index (SMI) are not available clinically, it is also feasible to predict esophageal cancer prognosis using simpler metrics, such as calf circumference (CC), five-count sit-up test (5-CST), and six-minute walk distance (6MWD).

CONCLUSIONS

Identifying the potential mechanism of sarcopenia in patients with esophageal cancer and implementing appropriate interventions may hold the key to improving the prognosis of these patients.

Chest CT opportunistic biomarkers for phenotyping high-risk COVID-19 patients: a retrospective multicentre study

OBJECTIVE

To assess the value of opportunistic biomarkers derived from chest CT performed at hospital admission of COVID-19 patients for the phenotypization of high-risk patients.

METHODS

In this multicentre retrospective study, 1845 consecutive COVID-19 patients with chest CT performed within 72 h from hospital admission were analysed. Clinical and outcome data were collected by each center 30 and 80 days after hospital admission. Patients with unknown outcomes were excluded. Chest CT was analysed in a single core lab and behind pneumonia CT scores were extracted opportunistic data about atherosclerotic profile (calcium score according to Agatston method), liver steatosis (≤ 40 HU), myosteatosis (paraspinal muscle F < 31.3 HU, M < 37.5 HU), and osteoporosis (D12 bone attenuation < 134 HU). Differences according to treatment and outcome were assessed with ANOVA. Prediction models were obtained using multivariate binary logistic regression and their AUCs were compared with the DeLong test.

RESULTS

The final cohort included 1669 patients (age 67.5 [58.5-77.4] yo) mainly men 1105/1669, 66.2%) and with reduced oxygen saturation (92% [88-95%]). Pneumonia severity, high Agatston score, myosteatosis, liver steatosis, and osteoporosis derived from CT were more prevalent in patients with more aggressive treatment, access to ICU, and in-hospital death (always p < 0.05). A multivariable model including clinical and CT variables improved the capability to predict non-critical pneumonia compared to a model including only clinical variables (AUC 0.801 vs 0.789; p = 0.0198) to predict patient death (AUC 0.815 vs 0.800; p = 0.001).

CONCLUSION

Opportunistic biomarkers derived from chest CT can improve the characterization of COVID-19 high-risk patients.

CLINICAL RELEVANCE STATEMENT

In COVID-19 patients, opportunistic biomarkers of cardiometabolic risk extracted from chest CT improve patient risk stratification.

KEY POINTS

• In COVID-19 patients, several information about patient comorbidities can be quantitatively extracted from chest CT, resulting associated with the severity of oxygen treatment, access to ICU, and death. • A prediction model based on multiparametric opportunistic biomarkers derived from chest CT resulted superior to a model including only clinical variables in a large cohort of 1669 patients suffering from SARS- CoV2 infection. • Opportunistic biomarkers of cardiometabolic comorbidities derived from chest CT may improve COVID-19 patients' risk stratification also in absence of detailed clinical data and laboratory tests identifying subclinical and previously unknown conditions.