Predictor Of Progression-free Survival Research Articles

Efficacy and Prognostic Indicators of Isatuximab, Pomalidomide, and Dexamethasone (IsaPd) in Daratumumab-Refractory Multiple Myeloma Patients: A Multicenter Real-World Study.

This multicenter real-world analysis evaluated the efficacy of isatuximab, pomalidomide, and dexamethasone (IsaPd) in 51 patients with multiple myeloma (MM) who were refractory to daratumumab (Dara-R). The majority were under 70years old (60.8%), predominantly female (56.9%), and heavily pretreated, with 74.5% being triple-class refractory (TCR); 32.1% of the 28 patients with cytogenetic data had high-risk abnormalities. The overall response rate (ORR) was 56.9%, including 3 patients with stringent complete response (sCR), 4 with CR, and 7 with very good partial response (VGPR). Neither age, number of prior therapies, TCR status, nor time from Dara refractoriness to IsaPd initiation significantly affected response rates. Median progression-free survival (PFS) was 5.8months, with a 12-month PFS probability of 30.6%. Baseline hemoglobin (Hb) levels were a key predictor of PFS: patients with Hb<11.8g/L had a 3.5-fold increased risk of progression, with a median PFS of 4.6months compared to 22months in those with higher Hb. Median overall survival (OS) was 21.0months, with a 12-month OS probability of 63.4%. Lower Hb levels (<11g/L) were associated with a tenfold increased risk of mortality. Among the 28 patients who underwent FISH analysis, while no significant difference in mortality risk was observed, those with high-risk cytogenetic abnormalities exhibited a nearly tenfold increased risk of disease progression. These results suggest that IsaPd offers a meaningful option for Dara-R patients, with Hb levels serving as a critical predictor of both PFS and OS. However, PFS remains modest, underscoring the need for novel combination therapies.

A Deep Dive into the RAISE Project Methodology

This animation explains the methodology used in the Research for Artificial Intelligence-Based Surrogate Endpoint (RAISE) project, presented in two manuscripts recently published in Future Oncology. This exploratory work assessed whether deep learning techniques could improve early progression-free survival (PFS) in patients with neuroendocrine tumors (NETs), using features extracted from computerized tomography scans from patients in the CLARINET phase 3 trial. As part of the RAISE project, response heterogeneity (the coexistence of responding and non-responding lesions in the same patient) was also investigated to assess whether it could be used as a biomarker to allow earlier prediction of PFS in patients with NETs. Previous definitions of response heterogeneity are not suitable for assessing slow-growing tumors, such as NETs, so the definition of response heterogeneity was adapted in this study and is described in this animation.

Efficacy of first-line radiofrequency ablation combined with systemic chemotherapy plus targeted therapy for initially unresectable colorectal liver metastases

Background/Objective The optimal strategy for patients with colorectal liver metastases is still controversially discussed. This study aimed to evaluate the efficacy of radiofrequency ablation (RFA) combined with systemic chemotherapy plus targeted therapy as first-line treatment in patients with initially unresectable colorectal liver metastases (CRLM), to identify prognostic factors and construct nomograms predicting survival. Methods This retrospective study included patients with initially unresectable CRLM treated with (study group n = 74) or without (control group n = 83) RFA at the National Cancer Center from January 2018 to January 2021. Survival curves were assessed using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox regression analyses were used to determine prognostic factors and include these factors in the nomograms to predict progression-free survival (PFS) and overall survival (OS). Results The study group had significantly better median PFS (17.16 months vs. 8.35 months, p < 0.01) and OS (34.9 months vs. 21.1 months, p < 0.01) than the control group after propensity score matching. Cox regression analyses identified RFA treatment and clinical risk score (CRS) as independent prognostic factors for PFS. The largest diameter of liver metastases, RFA treatment, and CRS were independent prognostic factors for OS. Based on this finding, nomograms with good discrimination and calibration were constructed. Conclusion RFA combined with systemic chemotherapy plus targeted therapy as first-line treatment could significantly prolong PFS and OS in patients with initially unresectable CRLM compared with systemic chemotherapy plus targeted therapy. The nomograms predicting PFS and OS might help clinicians select personalized treatment.

Real-world data from patients with gastric-type mucinous carcinoma of the cervix: a multicenter, retrospective study.

We herein retrospectively analyzed the clinicopathologic features from a large cohort of GAS patients to provide real-world evidence for optimizing the diagnosis and treatment. One hundred and fifty-seven GAS patients from three hospitals were recruited for analysis. We extracted clinical and pathologic information from patient medical records and performed regular follow-up. Logistic regression and Kaplan - Meier analyses were conducted to identify the prognostic factors. 31.2% exhibited stage I tumor, and 11.5%, 33.1%, and 24.2% manifested tumors at stages II, III, and IV, respectively. For the entire group, the median progression-free survival (PFS) and overall survival (OS) were 22 and 33 months, respectively. Multivariate analysis showed tumor stage and ovarian metastasis were predictors for PFS; and that ovarian metastasis and small tumor diameter were independent prognostic factors for OS. We determined an abnormal elevation of tumor abnormal protein (TAP) in 76% GAS patients, which could serve as a sensitive marker for tumor recurrence/metastasis. We demonstrated that ovarian metastasis and FIGO stage (including tumor diameter) were independent prognostic predictors for GAS patients. Moreover, we were the first to report that TAP constituted a potent marker for GAS surveillance, thus warranting further investigation.

Validation of PDACai v2.0 in predicting relative benefit from frontline FOLFIRINOX (FFX) and gemcitabine/nab-paclitaxel (GA) for patients (pts) with metastatic pancreatic cancer (mPDAC).

776 Background: Novel biomarkers beyond genomic alterations in DDR pathway genes (e.g. BRCA1/2 ) have the potential to optimize frontline and subsequent treatment decisions for mPDAC. This real-world evidence (RWE) study validates PDACai v2.0, a machine learning framework that leverages clinical NGS data to predict progression-free survival (PFS) on FFX vs GA in individual pts with mPDAC. Methods: We analyzed outcomes from 774 pts with mPDAC who underwent genomic profiling via the Know Your Tumor program or were referred to Perthera by treating oncologists. Independent training and validation cohorts receiving either 1st line FFX or GA were split (60:40). PDACai v2.0 integrates an updated set of clinical (age &lt; 63, sex) and lab-agnostic molecular features derived from genomic testing reports (specific KRAS variants, TP53 GOF vs LOF, DDR pathway redefined). Relative benefit scores predicted by FFX or GA models were evenly binned into three PDACai signature categories representing lower, middle, and upper tertiles for each independent cohort. Statistical differences in median PFS were evaluated using ordinal Cox regression. Results: Median PFS followed PDACai predicted trends for each therapy’s training and validation cohorts. The predictive utility of PDACai was confirmed in the independent validation cohorts when comparing PFS on FFX (p = 0.01637; HR = 0.73 [95% CI: 0.56-0.94]) and GA (p = 0.0007506; HR = 0.62 [95% CI: 0.47-0.82]) across tertiles. Conclusions: Using RWE, the PDACai v2.0 signature successfully predicted PFS differences in pts treated with 1st line FFX or GA in mPDAC. Prospective validation efforts and additional data-driven insights into 2nd line PFS on FOLFOX vs 5FU/nal-irinotecan are underway. Summary of actual PFS in months on 1st line therapies in pts assigned to lower, middle, and upper thirds based on relative PDACai v2.0 scores. Independent Cohort (# Pts) Lower TertilemPFS [95% CI] Middle TertilemPFS [95% CI] Upper TertilemPFS [95% CI] 1st line FFX Training (243) 6.5 [5.5-8.1] 9.3 [6.5-13.3] 15.8 [14.1-N/R] 1st line FFX Validation (165) 9.4 [7-13.3] 8.6 [5.6-11.4] 13.2 [9.9-39.6] 1st line GA Training (218) 5.6 [4.7-6.3] 6.9 [5.2-8.2] 11.2 [8.8-13.4] 1st line GA Validation (148) 5.6 [4.2-8.4] 7.5 [6.5-13.1] 8.5 [7.7-11.5]

Prognostic models for progression-free survival in atypical meningioma: Comparison of machine learning-based approach and the COX model in an Asian multicenter study.

Atypical meningiomas are prevalent intracranial tumors with varied prognoses and recurrence rates. The role of adjuvant radiotherapy (ART) in atypical meningiomas remains debated. This study aimed to develop and validate a prognostic model incorporating machine learning techniques and clinical factors to predict progression-free survival (PFS) in patients with atypical meningiomas and assess the impact of ART. A retrospective review of 669 patients from five institutions in Korea and China was conducted. Cox proportional hazards, gradient boosting machine, and random survival forest models were employed for comparative analysis, utilizing both internal and external validation sets. Model performance was assessed using Harrell's concordance index and permutation feature importance. Of 581 eligible patients, age, post-operative platelet count, performance status, Simpson grade, and ART were identified as significant prognostic factors across all models. In the ART subgroup, age and tumor size were the top prognostic indicators. The Cox model outperformed other methods, achieving a training C-index of 0.73 (95 % CI: 0.72-0.73) and an external validation C-index of 0.74 (95 % CI: 0.73-0.74). The model effectively stratified patients into risk categories, revealing a differential impact of ART: low-risk patients in the active surveillance group showed a 5.6 % improvement in 5-year PFS with predicted ART addition, compared to a 15.9 % improvement in the high-risk group. This multicenter study offers a validated prognostic model for atypical meningiomas, highlighting the need for tailored treatment plans. The model's ability to stratify patients into risk categories for PFS provides a valuable tool for clinical decision-making, potentially optimizing patient outcomes.

Prediction of immunotherapy response in deficient mismatch repair colorectal cancers using an immune-enhanced exome and transcriptome platform.

270 Background: While immune checkpoint blockade (ICB) is used to treat metastatic deficient mismatch repair (dMMR) colorectal cancers (CRCs), such tumors are heterogeneous and resistance to ICB is frequent. To date, biomarkers that can consistently predict patient response to ICB are lacking. We determined whether biomarkers that integrate tumor and immune-related molecular mechanisms could predict ICB response. Methods: Consecutive patients with metastatic dMMR CRCs (N=32) who had been treated with anti-PD-1 therapy were profiled using a validated immune-enhanced exome and transcriptome platform (ImmunoID NeXT, Personalis) using tumor and paired normal tissue. This platform provides antigen-specificity of T or B cells, indicated by the TCR or BCR respectively, neoantigen prediction, tumor mutation burden (TMB), immune profile, HLA allele-specific loss of heterogeneity (LOH) and a Neoantigen Presentation Score (NEOPS) that combines a neoantigen prediction tool with mechanisms of immune evasion. Tumor variables were analyzed in relationship to objective tumor response, and a Cox proportional hazards model was fit to predict progression-free survival (PFS) or overall survival (OS). Results: In CRCs, TCR and BCR repertoire diversity were each significantly increased in tumors from responders versus those with stable or progressive disease after anti-PD-1 treatment (p=0.015 and p=0.048, respectively). Neoantigen burden score, TMB, and HLA allele-specific LOH (reflects loss of antigen presentation machinery) were each unable to distinguish responders from nonresponders. An immune checkpoint gene expression signature was increased in nonresponders relative to responders (P= 0.005). Neoantigen presentation score (NEOPS) was prognostic when adjusted for tumor grade only for OS (p=0.048). Conclusions: The T-cell and B-cell receptor (TCR/BCR) repertoire, as well as NEOPS, were each significantly predictive of favorable response to anti-PD-1 therapy in dMMR tumors. In addition, an immune checkpoint gene expression signature was predictive of resistance to ICB.

Multicenter, retrospective, observational study of outcomes of treatment for unresectable neuroendocrine tumors G3 of gastroenteropancreatic or unknown primary origin.

660 Background: The 2017 edition of the WHO classification categorized neuroendocrine neoplasms (NEN) as neuroendocrine tumors (NET) G1, G2, G3, and neuroendocrine carcinoma (NEC). Prior to 2017, NET G3 was categorized as NEC in the WHO 2010 classification. Platinum-based chemotherapy, commonly used as first-line treatment for these tumors, shows suboptimal efficacy for NET G3. Additionally, large-scale studies and robust evidence on chemotherapy outcomes for NET G3 are lacking. We investigated chemotherapy regimens for NET G3 and evaluated their outcomes. Methods: We included 73 patients (pts) who were clinically and pathologically diagnosed with unresectable NET G3 of gastroenteropancreatic or unknown primary origin and who started chemotherapy between Jan 2011 and Dec 2019. Clinical data were collected retrospectively and analyzed for regimen selected, treatment efficacy, and prognostic factors. A central pathological review was conducted if tissue samples were available. Results: The median age of pts was 65 years (range 27-85 years), with 45 being male. Among the pts, 49 had pancreatic NETs, 10 had gastrointestinal NETs, and 14 had NETs of unknown primary origin. The median Ki-67 index was 30% (range 20-70%). Twenty-seven patients (37%) had received prior treatment, such as surgery or local therapy. A central pathological diagnosis was confirmed NET G3 in 42 out of 44 patients (95%). Primary treatment regimens were NET-based (somatostatin analogues, everolimus, sunitinib, streptozocin-based chemotherapy, and capecitabine and temozolomide [CAPTEM]) and NEC-based (platinum-based chemotherapy) in 54% and 44% of patients, respectively. NET-based regimens were selected in 36% and 78% of pts, respectively, before and after publication of the WHO 2017 classification. The overall response rate (ORR) was 16% for NEC-based regimens and 19% for NET-based regimens. However, when focusing on NET-based chemotherapy (streptozocin-based and CAPTEM), the ORR was 54%. Median progression-free survival (PFS) was 118 days (95% confidence interval [CI]: 60-337 days) for NET-based regimens and 229 days (95%CI: 133-426 days) for NEC-based regimens. Median overall survival (OS) was 841 days for NET-based regimens (95%CI: 480-989 days) and 658 days (95%CI: 455-1144 days) for NEC-based regimens. There were no significant differences in PFS or OS between the regimens. In multivariable analysis, hepatic tumor volume &gt;25% was a negative predictor of PFS, and NSE ≥16.3 ng/mL was a negative predictor of OS. Conclusions: Since the introduction of NET G3 in the WHO classification, NET-based regimens have become the preferred treatment choice. Although there were no significant differences in PFS or OS between the regimens, the NEC-based regimens was limited. Pts with high hepatic tumor volume or elevated NSE levels had a worse prognosis.

Utilizing explainable machine learning for progression-free survival prediction in high-grade serous ovarian cancer: insights from a prospective cohort study.

High-grade serous ovarian cancer (HGSOC) remains one of the most challenging gynecological malignancies, with over 70% of ovarian cancer patients ultimately experiencing disease progression. The current prognostic tools for progression-free survival (PFS) in HGSOC patients have limitations. This study aims to develop an explainable machine learning (ML) model for predicting PFS in HGSOC patients. Nine ML algorithms for PFS prediction were developed using a prospective cohort of 310 HGSOC patients consecutively enrolled from a large Chinese tertiary hospital between January 2017 and December 2020. The optimal model was internally validated using the 1000 bootstrap method. The SHapley Additive exPlanations (SHAP) method was employed to interpret the model in terms of feature importance and feature effects. The final model, constructed with the optimal feature subset, was deployed as an interactive web-based Shiny app. The random survival forest (RSF) model demonstrated superior predictive performance compared to other ML models, the RFS model constructed with an optimal feature subset in the optimal imputed dataset achieved a superior 1000 bootstrap C-index of 0.755 (95% CI: 0.750-0.780) and a Brier score of 0.183 (95% CI: 0.175-0.190). SHAP analysis identified tumor residual, HE4, FIGO stage, T stage, CA125, age, ascites volume, platelet counts, and BMI as the top nine contributing factors. It also revealed potential nonlinear relationships and important thresholds between HE4, CA125, age, ascites volume, platelet counts, the body mass index, and PFS risk. Additionally, interaction effects were found between tumor residual and age, HE4, and CA125. Finally, an interactive web-based Shiny app for the model was developed and accessible at https://rsfmodels.shinyapps.io/ocRSF/. An explainable ML model for PFS prediction in HGSOC patients was developed with superior results. The publicly accessible web tool based on the optimized model facilitates its utility in clinical settings, potentially improving individualized patient management and treatment decision-making in HGSOC.

Harnessing Machine Learning and Nomogram Models to Aid in Predicting Progression-Free Survival for Gastric Cancer Patients Post-Gastrectomy with Deficient Mismatch Repair(dMMR)

ObjectiveTo assess the effectiveness of a machine learning framework and nomogram in predicting progression-free survival (PFS) post-radical gastrectomy in patients with dMMR.MethodMachine learning models and nomograms to forecast PFS in patients undergoing radical gastrectomy for nonmetastatic gastric cancer with dMMR. Independent risk factors were identified using Cox regression analysis to develop the nomogram. The performance of the models was assessed through C-index, time receiver operating characteristic (T-ROC) curves, calibration curves, and decision curve analysis (DCA) curves. Subsequently, patients were categorized into high-risk and low-risk groups based on the nomogram's risk scores.ResultsAmong the 582 patients studied, machine learning models exhibited higher c-index values than the nomogram. Random Survival Forests (RSF) demonstrated the highest c-index (0.968), followed by Extreme Gradient Boosting (XG boosting, 0.945), Decision Survival Tree (DST, 0.924), the nomogram (0.808), and 8th TNM staging (0.757). All models showed good calibration with low integrated Brier scores (< 0.1), although there was calibration drift over time, particularly in the traditional nomogram model. DCA showed an incremental net benefit from all machine learning models compared with conventional models currently used in practice. Age, positive lymph nodes, neural invasion, and Ki67 were identified as key factors and integrated into the prognostic nomogram.ConclusionOur research has demonstrated the effectiveness of the RSF algorithm in accurately predicting progression-free survival (PFS) in dMMR gastric cancer patients after gastrectomy. The nomogram created from this algorithm has proven to be a valuable tool in identifying high-risk patients, providing clinicians with important information for postoperative monitoring and personalized treatment strategies.

Adult patients with Philadelphia chromosome positive acute lymphoblastic leukemia undergoing allogeneic hematopoietic stem cell transplantation and tyrosine kinase inhibitors: development and validation of a clinical prediction model based on cytogenetics, IKZF1 deletions and minimal residual disease.

The aim of this study was to develop and validate a nomogram predicting progression-free survival (PFS) for adult patients with positive acute lymphoblastic leukemia(Ph + ALL) who have undergone allogeneic hematopoietic stem cell transplantation(allo-HSCT) and tyrosine kinase inhibitor(TKI) treatment. Data were retrospectively collected from 176 adult patients diagnosed with Ph + ALL and treated with allo-HSCT and TKIs at The First Affiliated Hospital, Zhejiang University School of Medicine, between January 2015 and May 2023. 70% of the patients were randomly assigned to the training group(n = 124) and 30% of the patients were assigned to the validation group(n = 52). Univariate Cox regression analysis and Akaike Information Criterion(AIC) were utilized to identify significant predictive factors, leading to the development of a nomogram designed to forecast the probability of PFS at 6, 9, and 12 months post-transplantation. The final nomogram incorporated three key variables: presence of complex additional cytogenetic abnormalities (ACAs), minimal residual disease (MRD) status prior to allo-HSCT, and IKZF1 gene deletions. The calibration curves showed excellent consistency between the nomogram prediction and actual observation for 6-, 9- and 12-month PFS in the training set and validation set. The C-index of the training set was 0.726(95%CI: 0.635-0.816), which was no significantly different from the validation set(C-index = 0.774, 95%CI: 0.674-0.875, P > 0.05). This study may provide a simple and efficient prediction model for patients with Ph + ALL undergoing allo-HSCT and TKIs, which can accurately predict PFS subsequent to transplantation. This tool could potentially aid clinicians in decision-making processes and improve patient outcomes.

Prognostic significance of peripheral blood biomarkers in patients with advanced renal cell carcinoma treated with nivolumab and ipilimumab—a polish multicenter, observational study

Immune checkpoint inhibitors have improved the treatment of metastatic renal cell carcinoma (RCC), with the combination of nivolumab (NIVO) and ipilimumab (IPI) showing promising results. However, not all patients benefit from these therapies, emphasizing the need for reliable, easily assessable biomarkers. This multicenter study involved 116 advanced RCC patients treated with NIVO + IPI across nine oncology centers in Poland. Blood markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR), eosinophils, and monocytes were assessed at baseline, after three months, and before disease progression (PD). The prognostic significance of these parameters was analyzed using linear regression, Kaplan–Meier survival analysis, and Cox regression models. After a median follow-up of 11.8 months, the progression-free survival (PFS) was 12.8 months (95% confidence interval [CI] 5.7–28.1), while the overall survival (OS) was 27.3 months (95% CI 16-not reached). Patients with an NLR increase of ≥ 25% had a PFS of 8.2 (3.1–24.7) months compared to 17.5 (8.6–28.1) months in those with a rise in < 25% (p = 0.015). Similarly, a ≥ 25% increase in PLR was linked to a PFS of 6.8 (2.8–8.3) months compared to 17.4 (8.4–28.1) months (p < 0.001). Multivariate analysis confirmed PLR as an independent predictor of PFS (HR 2.9, 95% CI 1.5–5.6, p = 0.001), while elevated eosinophil levels were associated with a reduced risk of death (HR 0.2, 95% CI 0.04–0.9, p = 0.05). No other analysis was statistically significant. NLR, PLR, and eosinophil levels may serve as valuable biomarkers for predicting treatment response in RCC patients receiving NIVO + IPI.

Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors

ObjectiveTo develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs).MethodsThis retrospective study included 172 patients with uHCC who underwent combination therapy of TACE-HAIC with TKIs and PD-1 inhibitors. Among them, 122 were from the Interventional Department of the Harbin Medical University Cancer Hospital, with 92 randomly assigned to the training cohort and 30 cases randomly assigned to the testing cohort. The remaining 50 cases were from the Interventional Department of the Affiliated Fourth Hospital of Harbin Medical University and were used for external validation. All patients underwent liver enhanced CT examination before treatment. Residual convolutional neural network (ResNet) technology was used to extract image features. A predictive model for treatment response of combination therapy and PFS was established based on image features and clinical features. Model effectiveness was evaluated using metrics such as the area under the receiver operating characteristic (ROC) curve (AUC), concordance index (C-index), accuracy, precision, and F1-score.ResultsAll patients had a median follow-up of 25.2 months (95% CI 24.4–26.0), with a median PFS of 14.0 months (95% CI 8.5–19.4) and a median overall survival (OS) of 26.2 months (95% CI 15.9–36.4) achieved. Objective response rate (ORR) and disease control rate (DCR) was 41.0% and 55.7%, respectively. In the treatment response prediction model, the AUC for the training cohort reached 0.96, with an accuracy of 89.5%, precision of 85.6%, and F1-score of 0.896; the AUC for the testing cohort was 0.87, with an accuracy of 80.4%, precision of 74.5%, and F1-score of 0.802. The AUC of the external validation cohort was 0.85, with accuracy of 79.1%, precision of 73.6%, and f1-score of 0.784. In the PFS prediction model, the predicted AUC for 12 months, 18 months, and 24 months-PFS in the training cohort were 0.874, 0.809, 0.801, respectively. The AUC of testing cohort were 0.762, 0.804, 0.792. The AUC of external validation cohort were 0.764, 0.796, 0.773. The C-index of the combination model, radiomics model, and clinical model were 0.75, 0.591, and 0.655, respectively. The calibration curve demonstrated that the combination model was significantly superior to both the radiomics and clinical models.ConclusionsThe study provides a CT-based radiomics model that can predict PFS for patients with uHCC treated with TACE-HAIC combined with PD-1 and TKIs.

Nomogram models for predicting outcomes in thyroid cancer patients with distant metastasis receiving 131iodine therapy

This study aimed to establish and validate prognostic nomogram models for patients who underwent 131I therapy for thyroid cancer with distant metastases. The cohort was divided into training (70%) and validation (30%) sets for nomogram development. Univariate and multivariate Cox regression analyses were used to identify independent predictors for overall survival (OS) and progression-free survival (PFS). Nomograms were developed based on these predictors, and Kaplan-Meier curves were constructed for validation. Among 451 patients who were screened, 412 met the inclusion criteria and were followed-up for a median duration of 65.2 months. The training and validation sets included 288 and 124 patients, respectively. Pathological type, first 131I administrated activity, and lesion 131I uptake in lesions were independent predictors for PFS. For OS, predictors included gender, age, metastasis site, first 131I administrated activity, 131I uptake, pulmonary lesion size, and stimulated thyroglobulin levels. These predictors were used to construct nomograms for predicting PFS and OS. Low-risk patients had significantly longer PFS and OS compared to high-risk patients, with 10-year PFS rates of 81.1% vs. 51.9% and 10-year OS rates of 86.2% vs. 37.4%. These may aid individualized prognostic assessment and clinical decision-making, especially in determining the prescribed activity for the first 131I treatment.

The predictive power of 18F-FDG PET/CT two-lesions radiomics and conventional models in classical Hodgkin's Lymphoma: a comparative retrospectively-validated study.

In a previous preliminary study, radiomic features from the largest and the hottest lesion in baseline 18F-FDG PET/CT (bPET/CT) of classical Hodgkin's Lymphoma (cHL) predicted early response-to-treatment and prognosis. Aim of this large retrospectively-validated study is to evaluate the predictive role of two-lesions radiomics in comparison with other clinical and conventional PET/CT models. cHL patients with bPET/CT between 2010 and 2020 were retrospectively included and randomized into training-validation sets. Target lesions were: Lesion_A, with largest axial diameter (Dmax); Lesion_B, with highest SUVmax. Total-metabolic-tumor-volume (TMTV) was calculated and 212 radiomic features were extracted. PET/CT features were harmonized using ComBat across two scanners. Outcomes were progression-free-survival (PFS) and Deauville Score at interim PET/CT (DS). For each outcome, three predictive models and their combinations were trained and validated: - radiomic model "R"; - conventional PET/CT model "P"; - clinical model "C". 197 patients were included (training = 118; validation = 79): 38/197 (19%) patients had adverse events and 42/193 (22%) had DS ≥ 4. In the training phase, only one radiomic feature was selected for PFS prediction in model "R" (Lesion_B F_cm.corr, C-index 66.9%). Best "C" model combined stage and IPS (C-index 74.8%), while optimal "P" model combined TMTV and Dmax (C-index 63.3%). After internal validation, "C", "C + R", "R + P" and "C + R + P" significantly predicted PFS. The best validated model was "C + R" (C-index 66.3%). No model was validated for DS prediction. In this large retrospectively-validated study, a combination of baseline 18F-FDG PET/CT two-lesions radiomics and other conventional models showed an added prognostic power in patients with cHL. As single models, conventional clinical parameters maintain their prognostic power, while radiomics or conventional PET/CT alone seem to be sub-optimal to predict survival.

Comprehensive Evaluation of Inflammatory Biomarkers in Cervical Cancer Treated with Chemoradiotherapy.

Objective: Inflammatory biomarkers have been shown to possess both prognostic and predictive significance in various cancers. Among the emerging biomarkers, the pan-immune-inflammation value (PIV) has recently been introduced as a novel indicator representing both the immune response and the systemic inflammatory state. This study aims to comprehensively evaluate the predictive value of inflammatory biomarkers on survival outcomes in cervical cancer patients undergoing chemoradiotherapy. Methods: A total of 90 patients who had undergone chemoradiotherapy for cervical cancer were included. Data on demographics, treatment protocols, pre-treatment blood parameters, and survival outcomes were collected. The association between inflammatory biomarkers and survival outcomes was investigated through univariate and multivariate analyses. Results: The univariate analysis identified the following as predictors of progression-free survival (PFS): neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), systemic immune-inflammation index (SII), PIV, C-reactive protein (CRP), albumin, and tumor size. Multivariate analysis revealed that only the PIV significantly predicted PFS (HR 3.05, 95% CI 1.0 to 9.3, p = 0.04). In the univariate analysis, several variables were predictive of overall survival (OS), including NLR, PLR, MLR, SII, PIV, CRP, LDH, albumin, tumor size, and Eastern Cooperative Oncology Group Performance Status (ECOG PS). Multivariate analysis revealed CRP (HR 3.41, 95% CI 1.5 to 7.7, p = 0.003) and ECOG PS (HR 4.78, 95% CI 1.3 to 17.3, p = 0.01) predictive of OS, with PIV approaching statistical significance (HR 2.56, 95% CI 0.8 to 7.6, p = 0.09). Conclusions: This study provides the first comprehensive analysis of the association between cervical cancer and various inflammatory biomarkers. Many of these biomarkers have demonstrated predictive value for survival outcomes in patients with cervical cancer undergoing definitive chemoradiotherapy. Among the biomarkers evaluated, CRP and PIV were identified as the most predictive, warranting further exploration in future research.

Cell indices as predictors of progression-free survival in multiple myeloma: a retrospective cohort study

Cell indices as predictors of progression-free survival in multiple myeloma: a retrospective cohort study

Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome.

Colorectal cancer (CRC) is a histologically heterogeneous disease with variable clinical outcome. The role the tumour microenvironment (TME) plays in determining tumour progression is complex and not fully understood. To improve our understanding, it is critical that the TME is studied systematically within clinically annotated patient cohorts with long-term follow-up. Here we studied the TME in three clinical cohorts of metastatic CRC with diverse molecular subtype and treatment history. The MISSONI cohort included cases with microsatellite instability that received immunotherapy (n = 59, 24 months median follow-up). The BRAF cohort included BRAF V600E mutant microsatellite stable (MSS) cancers (n = 141, 24 months median follow-up). The VALENTINO cohort included RAS/RAF WT MSS cases who received chemotherapy and anti-EGFR therapy (n = 175, 32 months median follow-up). Using a Deep learning cell classifier, trained upon >38,000 pathologist annotations, to detect eight cell types within H&E-stained sections of CRC, we quantified the spatial tissue organisation and colocalisation of cell types across these cohorts. We found that the ratio of infiltrating endothelial cells to cancer cells, a possible marker of vascular invasion, was an independent predictor of progression-free survival (PFS) in the BRAF+MISSONI cohort (p = 0.033, HR = 1.44, CI = 1.029-2.01). In the VALENTINO cohort, this pattern was also an independent PFS predictor in TP53 mutant patients (p = 0.009, HR = 0.59, CI = 0.40-0.88). Tumour-infiltrating lymphocytes were an independent predictor of PFS in BRAF+MISSONI (p = 0.016, HR = 0.36, CI = 0.153-0.83). Elevated tumour-infiltrating macrophages were predictive of improved PFS in the MISSONI cohort (p = 0.031). We validated our cell classification using highly multiplexed immunofluorescence for 17 markers applied to the same sections that were analysed by the classifier (n = 26 cases). These findings uncovered important microenvironmental factors that underpin treatment response across and within CRC molecular subtypes, while providing an atlas of the distribution of 180 million cells in 375 clinically annotated CRC patients. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Lachnoclostridium intestinal flora is associated with immunotherapy efficacy in nasopharyngeal carcinoma.

Effective biomarkers for assessing anti-PD-1/PD-L1 therapy efficacy in patients with nasopharyngeal carcinoma (NPC) are still lacking. The human gut microbiota has been shown to influence clinical response to anti-PD-1/PD-L1 therapy in many cancers. However, the relationship between the gut microbiota and the efficacy of immunotherapy in patients with nasopharyngeal carcinoma has not been determined. We conducted a prospective study in which fecal and blood samples from patients with NPC were subjected to 16S rDNA sequencing and survival analysis. To investigate potential differences in the gut microbiome between these groups and to identify potential biomarkers indicative of immunotherapy efficacy, patients were categorized into two groups according to their clinical response to immunotherapy, the responder group (R group) and the non-responder group (NR group). Progression-free survival (PFS) between these subgroups was analyzed using Kaplan-Meier survival analysis with the log-rank test. Additionally, we performed univariate and multivariate analyses to evaluate prognostic factors. Finally, we carried out non-targeted metabolomics to examine the metabolic effects associated with the identified microbiome. Our 16S rDNA sequencing results showed that the abundance of Lachnoclostridium was higher in the NR group than in the R group (p = 0.003), and alpha diversity analysis showed that the abundance of microbiota in the NR group was higher than that in the R group (p = 0.050). Patients with a lower abundance of Lachnoclostridium had better PFS (p = 0.048). Univariate (p = 0.017) and multivariate analysis (p = 0.040) showed that Lachnoclostridium was a predictor of PFS. Non-targeted metabolomics analysis revealed that Lachnoclostridium affects the efficacy of immunotherapy through the usnic acid. High abundance of Lachnoclostridium predicts poor prognosis in patients with NPC receiving immunotherapy.

Enhanced Survival Outcomes with FOLFIRINOX in Initial Metastatic Pancreatic Cancer: Single-Center Study

Introduction. The aim of this article is to evaluate the efficacy and outcomes of FOLFIRINOX as a first-line treatment for initial metastatic pancreatic cancer patients at the Clinical Center University of Sarajevo. Methods. The research presents a retrospective analysis was conducted on 33 patients treated with FOLFIRINOX, between January 2021 and January 2023. Baseline characteristics, tumor markers (CEA, CA 19-9, CA 125), neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelet ratio (NPR) and initial metastatic site were evaluated using Cox regression analysis in order to identify predictive and prognostic factors for progression-free survival (PFS) and overall survival (OS). Results. The median age of patients was 64 (range 38-76). There were 18 males and 15 females. The median OS was 21.7 months (95% CI, 10.5-32.9) and the median PFS was 10.0 months (95% CI, 8.2-11.8). A statistically significant negative correlation was found between NLR and OS (r=-0.464, p=0.045). Patients with initial liver metastasis had a numerically worse median OS (16.3 months, 95% CI, 5.1-27.5), compared to those with non-liver metastasis (OS not reached, p=0.058). Tumor markers, NLR, NPR, and initial metastatic site were not independent predictors of PFS and OS. Conclusion. FOLFIRINOX demonstrates significant efficacy in treating metastatic pancreatic cancer in a real-world setting. Personalized approaches, including genetic profiling and microbiome analysis, along with AI integration, offer promising avenues to enhance treatment outcomes and quality of life for patients. Keywords: metastatic pancreatic cancer, enhanced outcomes, overall survival, progression-free survival.