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》

Combined systemic immune-inflammatory index and prognostic nutritional index predicts the efficacy and prognosis of ES-SCLC patients receiving PD-L1 inhibitors combined with first-line chemotherapy.

There is a strong association between inflammation and the formation, progression, and metastasis of malignant tumors, according to earlier studies. Some composite inflammation-nutritional indicators, such as the systemic immune-inflammation index (SII) and the prognostic nutritional index (PNI), have a certain predictive effect on the prognosis of patients with small cell lung cancer (SCLC). However, the relationship between these indicators and the efficacy of immunotherapy in SCLC patients is still not well understood. Therefore, the purpose of this study was to explore how the pre-treatment SII-PNI score can predict the tumor response and prognosis of extensive-stage SCLC patients treated with PD-L1 inhibitors and first-line chemotherapy. This research conducted a retrospective review of 70 ES- SCLC patients from December 2019 to January 2023. According to the SII-PNI score, all patients were categorized into three groups. Overall survival (OS) was assessed by implementing the Kaplan Meier and Cox regression models. In addition, we devised a nomogram and scrutinized its accuracy in prediction through receiver operating characteristic (ROC) curve analysis and visualized it by calibration plots. Subsequently, a risk classification system was established. Patients with higher SII-PNI scores exhibited notably poorer survival outcomes compared to their counterpart with low SII-PNI score (p=0.008), as well as poorer short-term curative effects (p=0.004). The results of the multivariate analysis revealed that the SII-PNI score (p=0.036) had an independent association with a less favorable OS. The nomogram has been demonstrated to be a reliable prognostic tool for ES-SCLC patients. A notable difference was identified between the two different levels of risk. The baseline SII-PNI score can serve as a reliable prognostic indicator for ES-SCLC patients receiving immunotherapy. Higher SII-PNI scores imply a worse prognosis.

Ge Y ，Liu X ，Xu Y ，Su Y ，Li Y ，Wang L ... -  《Frontiers in Oncology》

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 ... -  《-》

Combined systemic inflammatory immune index and prognostic nutrition index as chemosensitivity and prognostic markers for locally advanced gastric cancer receiving neoadjuvant chemotherapy: a retrospective study.

The prognosis nutritional index (PNI) and the systemic inflammatory immunological index (SII) are characteristic indicators of the nutritional state and the systemic inflammatory response, respectively. However, there is an unknown combined effect of these indicators in the clinic. Therefore, the practicality of using the SII-PNI score to predict prognosis and tumor response of locally advanced gastric cancer (LAGC) following chemotherapy was the main focus of this investigation. We retrospectively analyzed 181 patients with LAGC who underwent curative resection after neoadjuvant chemotherapy in a prospective study (NCT01516944). We divided these patients into tumour regression grade(TRG) 3 and non-TRG3 groups based on tumor response (AJCC/CAP guidelines). The SII and PNI were assessed and confirmed the cut-off values before treatment. The SII-PNI values varied from 0 to 2, with 2 being the high SII (≥ 471.5) as well as low PNI (≤ 48.6), a high SII or low PNI is represented by a 1 and neither is represented by a 0, respectively. 51 and 130 samples had TRG3 and non-TRG3 tumor responses respectively. Patients with TRG3 had substantially higher SII-PNI scores than those without TRG3 (p < 0.0001). Patients with greater SII-PNI scores had a poorer prognosis (p < 0.0001). The SII-PNI score was found to be an independent predictor of both overall survival (HR = 4.982, 95%CI: 1.890-10.234, p = 0.001) and disease-free survival (HR = 4.763, 95%CI: 1.994-13.903, p = 0.001) in a multivariate analysis. The clinical potential and accuracy of low-cost stratification based on SII-PNI score in forecasting tumor response and prognosis in LAGC is satisfactory.

Ding P ，Yang J ，Wu J ，Wu H ，Sun C ，Chen S ，Yang P ，Tian Y ，Guo H ，Liu Y ，Meng L ，Zhao Q ... -  《BMC CANCER》

A comprehensive nomogram for assessing the prognosis of non-small cell lung cancer patients receiving immunotherapy: a prospective cohort study in China.

In China, lung cancer ranks first in both incidence and mortality among all malignant tumors. Non-small cell lung cancer (NSCLC) constitutes the vast majority of cases, accounting for 80% to 85% of cases. Immune checkpoint inhibitors (ICIs), either as monotherapies or combined with other treatments, have become the standard first-line therapy for NSCLC patients. This study aimed to establish a nomogram model for NSCLC patients receiving immunotherapy incorporating demographic information, clinical characteristics, and laboratory indicators. From January 1, 2019, to December 31, 2022, a prospective longitudinal cohort study involving 1321 patients with NSCLC undergoing immunotherapy was conducted at Chongqing University Cancer Hospital. Clinical and pathological characteristics, as well as follow-up data, were collected and analyzed. To explore prognostic factors affecting overall survival (OS), a Cox regression model was used to test the significance of various variables. Independent prognostic indicators were identified through multivariate analysis and then used to construct a nomogram prediction model. To validate the accuracy and practicality of this model, the concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to assess the predictive performance of the nomogram. In the final model, 11 variables from the training cohort were identified as independent risk factors for patients with NSCLC: age, KPS score, BMI, diabetes, targeted therapy, Hb, WBC, LDH, CRP, PLR, and LMR. The C-index for OS in the training cohort was 0.717 (95% CI, 0.689-0.745) and 0.704 (95% CI, 0.660-0.750) in the validation cohort. Calibration curves for survival probability showed good concordance between the nomogram predictions and actual observations. The AUCs for 1-year, 2-year, and 3-year OS in the training cohort were 0.724, 0.764, and 0.79, respectively, and 0.725, 0.736, and 0.818 in the validation cohort. DCA demonstrated that the nomogram model had a greater overall net benefit. A prognostic model for OS in NSCLC patients receiving immunotherapy was established, providing a simple and reliable tool for predicting patient survival (https://icisnsclc.shinyapps.io/DynNomapp/). This model offers valuable guidance for clinicians in making treatment decisions and recommendations.

Li H ，Yuan Y ，Xu Q ，Liang G ，Hu Z ，Li X ，Zhang W ，Lei H ... -  《Frontiers in Immunology》