Chemotherapy-Associated Liver Injuries: Unmet Needs and New Insights for Surgical Oncologists

Preoperative chemotherapy in upfront resectable colorectal liver metastases: New elements for an old dilemma?

The use of preoperative or "neoadjuvant" chemotherapy (NAC) has long been controversial for resectable colorectal liver metastases (CRLM). The European Society of Medical Oncology (ESMO) 2023 guidelines on metastatic colorectal cancer (CRC) indicate a combination of surgical/technical and oncologic/prognostic criteria as the two determinants for allocating patients to NAC or upfront hepatectomy. However, surgical and technical criteria have evolved, and oncologic prognostic criteria date from the pre-modern chemotherapy era and lack prospective validation. The traditional literature is interpreted as not supporting the use of NAC because several studies fail to demonstrate a benefit in overall survival (OS) compared to upfront surgery; however, OS may not be the most appropriate endpoint to consider. Moreover, the commonly quoted studies against NAC contain many limitations that may explain why NAC failed to demonstrate its value. The query of the recent literature focused primarily on other aspects than OS, such as surgical technique, the impact of side effects of chemotherapy, the histological growth pattern of metastases, or the detection of circulating tumor DNA, shows data that support a more widespread use of NAC. These should prompt a critical reappraisal of the use of NAC, leading to a more precise selection of patients who could benefit from it.

A virtual biopsy of liver parenchyma to predict the outcome of liver resection

The preoperative risk assessment of liver resections (LR) is still an open issue. Liver parenchyma characteristics influence the outcome but cannot be adequately evaluated in the preoperative setting. The present study aims to elucidate the contribution of the radiomic analysis of non-tumoral parenchyma to the prediction of complications after elective LR. All consecutive patients undergoing LR between 2017 and 2021 having a preoperative computed tomography (CT) were included. Patients with associated biliary/colorectal resection were excluded. Radiomic features were extracted from a virtual biopsy of non-tumoral liver parenchyma (a 2 mL cylinder) outlined in the portal phase of preoperative CT. Data were internally validated. Overall, 378 patients were analyzed (245 males/133 females-median age 67 years-39 cirrhotics). Radiomics increased the performances of the preoperative clinical models for both liver dysfunction (at internal validaton, AUC = 0.727 vs. 0.678) and bile leak (AUC = 0.744 vs. 0.614). The final predictive model combined clinical and radiomic variables: for bile leak, segment 1 resection, exposure of Glissonean pedicles, HU-related indices, NGLDM_Contrast, GLRLM indices, and GLZLM_ZLNU; for liver dysfunction, cirrhosis, liver function tests, major hepatectomy, segment 1 resection, and NGLDM_Contrast. The combined clinical-radiomic model for bile leak based on preoperative data performed even better than the model including the intraoperative data (AUC = 0.629). The textural features extracted from a virtual biopsy of non-tumoral liver parenchyma improved the prediction of postoperative liver dysfunction and bile leak, implementing information given by standard clinical data. Radiomics should become part of the preoperative assessment of candidates to LR.

The Histopathological Growth Pattern of Colorectal Liver Metastases Impacts Local Recurrence Risk and the Adequate Width of the Surgical Margin

BACKGROUND

The histopathological growth pattern (HGP) of colorectal liver metastases (CLM) has been associated with prognosis. This study was designed to elucidate if the HGP is associated with local recurrence risk and impacts the adequate width of surgical margin.

METHODS

All consecutive patients resected for CLM in 2018-2019 were considered. HGP was prospectively classified as follows: desmoplastic, pushing, and replacement. Surgical margin was classified as follows: R0 (margin ≥ 1 mm), R1vasc (0-mm margin, tumor detachment from intrahepatic vessels), and R1par (tumor exposure along transection plane). R0 resections were further distinguished in R0min (1-mm margin) and R0wide (> 1-mm margin).

RESULTS

A total of 340 resection areas in 136 patients were analyzed (70 R0min, 143 R0wide, 31 R1vasc, 96 R1par). HGP was desmoplastic in 26 cases, pushing in 221, and replacement in 93. Thirty-six local recurrences occurred (11%, median follow-up 21 months): 1 after R0wide, 4 after R0min, 3 after R1vasc, and 28 after R1par resection. In R1par group, local recurrence rate was high independently of HGP (29%). In R1vasc and R0min groups, local recurrence risk was higher in the replacement group (R1vasc: 29% vs. 4% if pushing/desmoplastic; R0min: 11% vs. 4%). In R0wide group, local recurrence risk was low for all HGP ( < 1%). Independent predictors of local recurrence were replacement HGP (odds ratio = 1.654, P = 0.036), and R1par resection (odds ratio = 57.209, P < 0.001 vs. R0).

CONCLUSIONS

Replacement HGP is associated with an increased risk of local recurrence. In these patients, a wide surgical margin should be pursued, because R1vasc and R0min resections could be insufficient. R1par resection is inadequate, independently of the HGP.

Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence

The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.

Virtual Biopsy for Diagnosis of Chemotherapy-Associated Liver Injuries and Steatohepatitis: A Combined Radiomic and Clinical Model in Patients with Colorectal Liver Metastases

Non-invasive diagnosis of chemotherapy-associated liver injuries (CALI) is still an unmet need. The present study aims to elucidate the contribution of radiomics to the diagnosis of sinusoidal dilatation (SinDil), nodular regenerative hyperplasia (NRH), and non-alcoholic steatohepatitis (NASH). Patients undergoing hepatectomy for colorectal metastases after chemotherapy (January 2018-February 2020) were retrospectively analyzed. Radiomic features were extracted from a standardized volume of non-tumoral liver parenchyma outlined in the portal phase of preoperative post-chemotherapy computed tomography. Seventy-eight patients were analyzed: 25 had grade 2-3 SinDil, 27 NRH, and 14 NASH. Three radiomic fingerprints independently predicted SinDil: GLRLM_f3 (OR = 12.25), NGLDM_f1 (OR = 7.77), and GLZLM_f2 (OR = 0.53). Combining clinical, laboratory, and radiomic data, the predictive model had accuracy = 82%, sensitivity = 64%, and specificity = 91% (AUC = 0.87 vs. AUC = 0.77 of the model without radiomics). Three radiomic parameters predicted NRH: conventional_HUQ2 (OR = 0.76), GLZLM_f2 (OR = 0.05), and GLZLM_f3 (OR = 7.97). The combined clinical/laboratory/radiomic model had accuracy = 85%, sensitivity = 81%, and specificity = 86% (AUC = 0.91 vs. AUC = 0.85 without radiomics). NASH was predicted by conventional_HUQ2 (OR = 0.79) with accuracy = 91%, sensitivity = 86%, and specificity = 92% (AUC = 0.93 vs. AUC = 0.83 without radiomics). In the validation set, accuracy was 72%, 71%, and 91% for SinDil, NRH, and NASH. Radiomic analysis of liver parenchyma may provide a signature that, in combination with clinical and laboratory data, improves the diagnosis of CALI.