Image-Guided Intraoperative Assessment of Surgical Margins in Oral Cavity Squamous Cell Cancer: A Diagnostic Test Accuracy Review.

(1) Background: The assessment of resection margins during surgery of oral cavity squamous cell cancer (OCSCC) dramatically impacts the prognosis of the patient as well as the need for adjuvant treatment in the future. Currently there is an unmet need to improve OCSCC surgical margins which appear to be involved in around 45% cases. Intraoperative imaging techniques, magnetic resonance imaging (MRI) and intraoral ultrasound (ioUS), have emerged as promising tools in guiding surgical resection, although the number of studies available on this subject is still low. The aim of this diagnostic test accuracy (DTA) review is to investigate the accuracy of intraoperative imaging in the assessment of OCSCC margins. (2) Methods: By using the Cochrane-supported platform Review Manager version 5.4, a systematic search was performed on the online databases MEDLINE-EMBASE-CENTRAL using the keywords "oral cavity cancer, squamous cell carcinoma, tongue cancer, surgical margins, magnetic resonance imaging, intraoperative, intra-oral ultrasound". (3) Results: Ten papers were identified for full-text analysis. The negative predictive value (cutoff < 5 mm) for ioUS ranged from 0.55 to 0.91, that of MRI ranged from 0.5 to 0.91; accuracy analysis performed on four selected studies showed a sensitivity ranging from 0.07 to 0.75 and specificity ranging from 0.81 to 1. Image guidance allowed for a mean improvement in free margin resection of 35%. (4) Conclusions: IoUS shows comparable accuracy to that of ex vivo MRI for the assessment of close and involved surgical margins, and should be preferred as the more affordable and reproducible technique. Both techniques showed higher diagnostic yield if applied to early OCSCC (T1-T2 stages), and when histology is favorable.

Mapping Tumor Heterogeneity via Local Entropy Assessment: Making Biomarkers Visible.

Advanced imaging and analysis improve prediction of pathology data and outcomes in several tumors, with entropy-based measures being among the most promising biomarkers. However, entropy is often perceived as statistical data lacking clinical significance. We aimed to generate a voxel-by-voxel visual map of local tumor entropy, thus allowing to (1) make entropy explainable and accessible to clinicians; (2) disclose and quantitively characterize any intra-tumoral entropy heterogeneity; (3) evaluate associations between entropy and pathology data. We analyzed the portal phase of preoperative CT of 20 patients undergoing liver surgery for colorectal metastases. A three-dimensional core kernel (5 × 5 × 5 voxels) was created and used to compute the local entropy value for each voxel of the tumor. The map was encoded with a color palette. We performed two analyses: (a) qualitative assessment of tumors' detectability and pattern of entropy distribution; (b) quantitative analysis of the entropy values distribution. The latter data were compared with standard Hounsfield data as predictors of post-chemotherapy tumor regression grade (TRG). Entropy maps were successfully built for all tumors. Metastases were qualitatively hyper-entropic compared to surrounding parenchyma. In four cases hyper-entropic areas exceeded the tumor margin visible at CT. We identified four "entropic" patterns: homogeneous, inhomogeneous, peripheral rim, and mixed. At quantitative analysis, entropy-derived data (percentiles/mean/median/root mean square) predicted TRG (p < 0.05) better than Hounsfield-derived ones (p = n.s.). We present a standardized imaging technique to visualize tumor heterogeneity built on a voxel-by-voxel entropy assessment. The association of local entropy with pathology data supports its role as a biomarker.

Upfront Surgery versus Neoadjuvant Perioperative Chemotherapy for Resectable Colorectal Liver Metastases: A Machine-Learning Decision Tree to Identify the Best Potential Candidates under a Parenchyma-Sparing Policy.

Addressing patients to neoadjuvant systemic chemotherapy followed by surgery rather than surgical resection upfront is controversial in the case of resectable colorectal -liver metastases (CLM). The aim of this study was to develop a machine-learning model to identify the best potential candidates for upfront surgery (UPS) versus neoadjuvant perioperative chemotherapy followed by surgery (NEOS). Patients at first liver resection for CLM were consecutively enrolled and collected into two groups, regardless of whether they had UPS or NEOS. An inverse -probability weighting (IPW) was performed to weight baseline differences; survival analyses; and risk predictions were estimated. A mortality risk model was built by Random-Forest (RF) to assess the best -potential treatment (BPT) for each patient. The characteristics of BPT-upfront and BPT-neoadjuvant candidates were automatically identified after developing a classification -and -regression tree (CART). A total of 448 patients were enrolled between 2008 and 2020: 95 UPS and 353 NEOS. After IPW, two balanced pseudo-populations were obtained: UPS = 432 and NEOS = 440. Neoadjuvant therapy did not significantly affect the risk of mortality (HR 1.44, 95% CI: 0.95-2.17, p = 0.07). A mortality prediction model was fitted by RF. The BPT was NEOS for 364 patients and UPS for 84. At CART, planning R1vasc surgery was the main factor determining the best candidates for NEOS and UPS, followed by primitive tumor localization, number of metastases, sex, and pre-operative CEA. Based on these results, a decision three was developed. The proposed treatment algorithm allows for better allocation according to the patient's tailored risk of mortality.

Assessment of the response of hepatocellular carcinoma to interventional radiology treatments.

According to Barcelona Clinic Liver Cancer (BCLC) guidelines, interventional radiology procedures are valuable treatment options for many hepatocellular carcinomas (HCCs) that are not amenable to resection or transplantation. Accurate assessment of the efficacy of therapies at earlier stages enables completion of treatment, optimal follow-up and to prevent potentially unnecessary treatments, side effects and costly failure. The goal of this review is to summarize and describe the radiological strategies that have been proposed to predict survival and to stratify HCC responses after interventional radiology therapies. New techniques currently in development are also described.

R1 Resection for Colorectal Liver Metastases: a Survey Questioning Surgeons about Its Incidence, Clinical Impact, and Management.

BACKGROUND: A ≥ 1-mm margin is standard for resection of colorectal liver metastases (CLM). However, R1 resection is not rare (10-30%), and chemotherapy could mitigate its impact. The possibility of detaching CLM from vessels (R1 vascular margin) has been described. A reappraisal of R1 resection is needed. METHODS: A 19-question survey regarding R1 resection for CLM was sent to hepatobiliary surgeons worldwide. Seven clinical cases were included. RESULTS: In total, 276 surgeons from 52 countries completed the survey. Ninety percent reported a negative impact of R1 resection (74% local recurrence, 31% hepatic recurrence, and 36% survival), but 50% considered it sometimes required for resectability. Ninety-one percent of responders suggested that the impact of R1 resection is modulated by the response to chemotherapy and/or CLM characteristics. Half considered the risk of R1 resection to be an indication for preoperative chemotherapy in patients who otherwise underwent upfront resection, and 40% modified the chemotherapy regimen when the tumor response did not guarantee R0 resection. Nevertheless, 80% scheduled R1 resection for multiple bilobar CLM that responded to chemotherapy. Forty-five percent considered the vascular margin equivalent to R0 resection. However, for lesions in contact with the right hepatic vein, right hepatectomy remained the standard. Detachment from the vein was rarely considered (10%), but 27% considered detachment in the presence of multiple bilobar CLM. CONCLUSIONS: A negative margin is still standard for CLM, but R1 resection is no longer just a technical error. R1 resection should be part of the modern multidisciplinary, aggressive approach to CLM.

Measurement of Total Liver Volume Using the Energy Expenditure: A New Formula.

BACKGROUND: The assessment of liver volume (LV) is important before surgical resection or transplantation to reduce the risk of hepatic insufficiency. LV is usually measured using computed tomography or with some formulas. The aim of this study was to develop a new dynamic formula to predict LV. METHODS: Using computed tomography, LV was calculated in 101 patients without liver disease. LV was correlated with patient metabolic status, calculated with the Harris-Benedict equation for basal energy expenditure (BEE). Activity energy expenditure (AEE) was also calculated. Using linear regression analysis, a new formula was derived and was compared with Heinmann's, Urata's, Emre's, Vauthey's, Yoshizumi's, Yu's, and Hashimoto's formulas. RESULTS: A new basal formula was established: LV = (0.789 × BEE) + 272. It was found to be the most accurate (R2 = 0.39, p < 0.001). Heinmann's, Emre's, and Vauthey's formulas tend to overestimate LV, while Urata's, Yoshizumi's, Yu's, and Hashimoto's formulas tend to underestimate LV. A new AEE formula was also established: LV = (0.789 × AEE) + 272. CONCLUSIONS: These formulas give a dynamic perspective of LV, which may be influenced by the patient's actual clinical status. Using these formulas, it is possible to estimate an increased value of LV, which may contribute to a reduction in the risk of postoperative hepatic insufficiency.