Prognostic value of preoperative systemic immune-inflammation index/albumin for patients with hepatocellular carcinoma undergoing curative resection.

Hepatocellular carcinoma (HCC) is a major factor for cancer-associated mortality globally. Although the systemic immune-inflammation index (SII) and albumin (ALB) show individual prognostic value for various cancers, their combined significance (SII/ALB) in HCC patients undergoing curative hepatectomy is still unknown. It is hypothesized that a higher SII/ALB ratio correlates with poorer outcomes with regard to overall survival (OS) and recurrence-free survival (RFS). To investigate the effect of preoperative SII/ALB in predicting the prognosis of HCC patients undergoing hepatectomy. Patients who received curative surgery for HCC at a single institution between 2014 and 2019 were retrospectively analyzed. Cox proportional hazards models and Kaplan-Meier curves were utilized to estimate OS and RFS. A nomogram was created using prognostic factors determined by the least absolute shrinkage and selection operator method and analyzed using multivariate Cox regression. This nomogram was assessed internally through the calibration plots, receiver operating characteristic (ROC) analysis, decision curve analysis (DCA) and the concordance index (C-index). This study enrolled 1653 HCC patients. Multivariate analyses demonstrated that SII/ALB independently predicted OS [hazard ratio (HR) = 1.22, 95%CI: 1.03-1.46, P = 0.025] and RFS (HR = 1.19, 95%CI: 1.03-1.38, P = 0.022). Age, alpha-fetoprotein, hepatitis B surface antigen, albumin-bilirubin grade, tumor diameter, portal vein tumor thrombus, tumor number, and SII/ALB were incorporated into the nomogram to predict OS. The nomogram had a C-index of 0.73 (95%CI: 0.71-0.76) and 0.71 (95%CI: 0.67-0.74) for the training and validation cohorts, respectively. The area under the ROC curve, DCA and calibration curves demonstrated high accuracy and clinical benefits. The SII/ALB may independently predict outcomes in HCC patients who receive curative surgical treatment. In addition, the nomogram can be used in HCC treatment decision-making.

Chen KL ，Qiu YW ，Yang M ，Wang T ，Yang Y ，Qiu HZ ，Sun T ，Wang WT ... -  《-》

Construction of a random survival forest model based on a machine learning algorithm to predict early recurrence after hepatectomy for adult hepatocellular carcinoma.

Zhang J ，Chen Q ，Zhang Y ，Zhou J ... -  《BMC CANCER》

Clinical significance of the combined systemic immune-inflammatory index and prognostic nutritional index in predicting the prognosis of patients with extensive-stage small-cell lung cancer receiving immune-combination chemotherapy.

The therapeutic efficacy and prognosis of various tumors can be assessed using the systemic immune-inflammatory index (SII) and prognostic nutritional index (PNI). Despite their potential, no studies have investigated the prognostic value of the combined SII-PNI score for outcomes in patients with extensive small cell lung cancer (ES-SCLC) treated with chemotherapy and immune checkpoint inhibitors (ICIs). Our study retrospectively examined 213 ES-SCLC patients treated with chemotherapy and ICIs across two institutions. The patients were divided into three groups based on their SII-PNI scores. Cox regression analysis was employed to identify independent prognostic factors. A nomogram was constructed based on these independent factors. With 1000 repeated samples, the bootstrap method was used to validate the nomogram model internally. The model's performance was assessed using calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Before and after chemotherapy with immune checkpoint inhibitors (ICIs), SII was significantly higher in the PD group compared with the PR group (both p < 0.05). In the meantime, PNI was considerably lower in the PD group than in the PR group (both p < 0.01). Kaplan-Meier curves demonstrated that patients with a low SII-PNI had prolonged progression-free survival (PFS) and overall survival (OS) compared to those with a high SII-PNI (all p < 0.01). Multivariate Cox analysis showed that PS = 1, bone metastasis, brain metastasis, and SII-PNI = 1,2 after four treatment cycles were independent risk factors for shorter OS and were included in the nomogram model. The ROC curves, C-index, and DCA curves confirm that the SII-PNI scores-based nomograms have strong predictive accuracy for OS. There was a significant correlation between pre- and post-treatment SII-PNI and treatment effect in ES-SCLC. The SII-PNI score after four treatment cycles is a useful prognostic indicator for ES-SCLC patients receiving chemotherapy combined with immune checkpoint inhibitors (ICIs).

Wang B ，Zhang J ，Shi Y ，Wang Y ... -  《BMC CANCER》

Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.

Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided. (1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS? Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses. Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS. Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments. Level III, diagnostic study.

Lee CC ，Chen CW ，Yen HK ，Lin YP ，Lai CY ，Wang JL ，Groot OQ ，Janssen SJ ，Schwab JH ，Hsu FM ，Lin WH ... -  《-》

Development and validation of a prognostic nomogram including inflammatory indicators for overall survival in hepatocellular carcinoma patients treated primarily with surgery or loco-regional therapy: A single-center retrospective study.

Hepatocellular carcinoma (HCC) is among the most prevalent malignant tumors, but the current staging system has limited efficacy in predicting HCC prognosis. The authors sought to develop and validate a nomogram model for predicting overall survival (OS) in HCC patients primarily undergoing surgery or loco-regional therapy. Patients diagnosed with HCC from January 2017 to June 2023 were enrolled in the study. The data were randomly split into a training cohort and a validation cohort. Utilizing univariate and multivariate Cox regression analyses, independent risk factors for OS were identified, and a nomogram model was constructed to predict patient survival. Therapy, body mass index, portal vein tumor thrombus, leukocyte, γ-glutamyl transpeptidase to platelet ratio, monocyte to lymphocyte ratio, and prognostic nutritional index were used to build the nomogram for OS. The nomogram demonstrated strong predictive ability, with high C-index values (0.745 for the training cohort and 0.650 for the validation cohort). ROC curves, calibration plots, and DCA curves all indicated satisfactory performance of the nomogram. Kaplan-Meier curve analysis showed a significant difference in prognosis between patients in the low- and high- risk groups. This nomogram provides precise survival predictions for HCC patients and helps identify individuals with varying prognostic risks, emphasizing the need for individualized follow-up and treatment plans.

Wang X ，Xu J ，Jia Z ，Sun G ... -  《-》