AB-IPI: A Clinicopathological Prognostic Score Integrating Host Fitness and Tumor Biology in Diffuse Large B-Cell Lymphoma

AB-IPI: A Clinicopathological Prognostic Score Integrating Host Fitness and Tumor Biology in Diffuse Large B-Cell Lymphoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article AB-IPI: A Clinicopathological Prognostic Score Integrating Host Fitness and Tumor Biology in Diffuse Large B-Cell Lymphoma Noriyuki Sakata, Yuka Tanaka, Ken Naganuma, Yasuyuki Takahashi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8451544/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The introduction of rituximab has eroded the discriminatory power of the International Prognostic Index (IPI) in diffuse large B-cell lymphoma (DLBCL), necessitating refined risk stratification. To address this, we developed the AB-IPI, a prognostic tool integrating tumor burden with host inflammation and biological resistance. In a retrospective analysis of 289 patients treated with R-CHOP-like immunochemotherapy, multivariate Cox regression identified IPI score > 2, serum albumin 50% as independent prognostic factors for overall survival. The AB-IPI stratified patients into four risk groups with 5-year survival rates ranging from 88.0% (Low) to 29.0% (High) (P < 0.0001). Crucially, the AB-IPI demonstrated superior discrimination (C-index: 0.725 vs. 0.702) and calibration compared to the standard IPI, with Decision Curve Analysis confirming greater net benefit. By capturing the triad of tumor burden, host fitness, and tumor biology using universally accessible biomarkers, the AB-IPI offers a robust, practical alternative for identifying high-risk patients in the rituximab era. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Oncology Diffuse Large B-cell Lymphoma Prognosis R-CHOP International Prognostic Index Serum Albumin BCL2 Figures Figure 1 Figure 2 Figure 3 Introduction Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent subtype of non-Hodgkin lymphoma (NHL) among adults, accounting for approximately 30–40% of all lymphoid malignancies worldwide 1 . It is a biologically and clinically heterogeneous disease entity, characterized by aggressive growth and remarkable diversity in genetic drivers, clinical presentation, and therapeutic responsiveness. The management of DLBCL has undergone a paradigm shift over the past two decades. The advent of R-CHOP therapy—a combination of the anti-CD20 monoclonal antibody rituximab with cyclophosphamide, doxorubicin, vincristine, and prednisolone—established itself as the global standard of care in the early 2000s following pivotal randomized trials 1 , 2 . This immunochemotherapy regimen dramatically improved outcomes, shifting the cure rate from approximately 40% in the CHOP era to approximately 60–70% in the rituximab era. However, the success of R-CHOP also illuminates a critical residual challenge: the management of the 30–40% of patients who experience primary refractory disease or relapse after initial response. For this subset of patients, the prognosis remains dismal. The SCHOLAR-1 study highlighted that patients with refractory DLBCL have a median overall survival of only 6.3 months, with very few achieving long-term remission with conventional salvage therapies 3 . While novel therapies such as chimeric antigen receptor T-cell (CAR-T) therapy, bispecific antibodies, and antibody-drug conjugates have provided new hope, their optimal deployment relies heavily on the ability to accurately identify high-risk patients at the time of initial diagnosis. For nearly thirty years, the International Prognostic Index (IPI) has served as the gold standard for risk stratification in aggressive lymphoma 4 . While the IPI remains a foundational tool for assessing macroscopic tumor burden, its ability to discriminate between risk groups has significantly diminished in the rituximab era. The universal improvement in survival outcomes with R-CHOP has led to a phenomenon widely recognized as the "erosion" of the IPI's predictive power 5 , 6 . Specifically, the distinction between the "high-intermediate" and "high-risk" groups has blurred, with observed survival rates in modern cohorts often exceeding original IPI predictions. Consequently, patients originally classified as high-risk by the IPI now frequently achieve 5-year survival rates of 50% or higher, making it difficult to justify experimental intensified frontline therapies based on IPI score alone. In response to these limitations, alternative prognostic models have been proposed 6 . The Revised IPI (R-IPI) and NCCN-IPI aimed to enhance discrimination 7 , 8 . However, the NCCN-IPI introduces significant complexity to the clinical workflow, requiring normalized LDH ratios and precise radiological adjudication of extranodal sites, rendering it cumbersome for routine practice 5 , 8 , 9 . Furthermore, these models remain strictly clinical; they measure tumor burden and patient frailty but fail to capture the underlying biological heterogeneity driving therapeutic resistance 10 . Simultaneously, molecular risk stratification via Cell-of-Origin (COO) subtypes or "Double Hit" status has become standard 11 – 13 , yet these technologies are often costly and unavailable in resource-limited settings. There is a pressing need for a prognostic tool that bridges the gap between the clinical utility of the IPI and the biological precision of molecular profiling using universally accessible biomarkers 14 . Accurate prognostication requires a holistic approach integrating three distinct pillars: "tumor burden," "host fitness," and "tumor biology." To achieve this integration without relying on inaccessible genomic testing, this study focused on two readily available biomarkers: serum albumin and BCL2 protein expression. Serum albumin functions as a potent surrogate for systemic inflammation and host resilience 15 – 25 , while BCL2 protein expression captures biological resistance to apoptosis 13 . In this study, we aimed to develop the AB-IPI (Albumin-BCL2 Refined Prognostic Index), a novel scoring system integrating the standard IPI with serum albumin and BCL2 protein expression. We hypothesized that this simple 3-factor model would provide superior risk stratification and clinical utility compared to the standard IPI by effectively capturing the triad of tumor burden, host inflammation, and biological resistance. Methods Study Design and Patients This study was a retrospective observational analysis of 289 consecutive patients with newly diagnosed de novo Diffuse Large B-cell Lymphoma (DLBCL), Not Otherwise Specified (NOS), treated at two tertiary care institutions: Saitama Medical University Saitama Medical Center and Otemae Hospital. The study period spanned from January 2008 to December 2020. Inclusion criteria were: (1) histologically confirmed DLBCL, NOS according to the WHO classification 26 (2) age > 18 years; and (3) treatment with R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, Prednisolone) or R-CHOP-like immunochemotherapy with curative intent as first-line therapy. Exclusion criteria included: (1) primary Central Nervous System (CNS) lymphoma; (2) transformed lymphoma (e.g., from follicular lymphoma); (3) HIV-associated lymphoma; and (4) incomplete clinical data preventing the calculation of IPI or missing BCL2/Albumin data. The final study cohort consisted of 289 out of 316 cases. The study protocol was approved by the Ethics Committees of Saitama Medical University Saitama Medical Center and Otemae Hospital (No. 2025-114) and adhered to the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. All methods were performed in accordance with the relevant guidelines and regulations. Due to the retrospective nature of the study, the requirement for informed consent was waived, and an opt-out method was employed. Data Collection and Definitions Clinical data were extracted from electronic medical records. Baseline characteristics included age, sex, ECOG performance status (PS), serum lactate dehydrogenase (LDH) levels, Ann Arbor stage, and extranodal involvement. Biomarker Assessment: Serum albumin was measured at diagnosis; a cutoff of <3.6 g/dL was determined based on receiver operating characteristic (ROC) curve analysis (Supplementary Fig. S2). BCL2 protein expression was evaluated on formalin-fixed paraffin-embedded (FFPE) tissue using immunohistochemistry (IHC). Positivity was defined as >50% of tumor cells exhibiting cytoplasmic staining 21,24 . Statistical Analysis The primary endpoint was Overall Survival (OS), calculated from the date of diagnosis to the date of death from any cause or the last follow-up. The prognostic impact of variables was assessed using the Kaplan-Meier method and log-rank test. A multivariate Cox proportional hazards regression model was constructed to identify independent prognostic factors. The AB-IPI score was derived from significant variables identified in the multivariate analysis. Model performance was evaluated using Harrell’s C-index (Internal validation was performed using 1,000 bootstrap resamples to calculate the optimism-corrected C-index, adhering to the TRIPOD guidelines for type 2b validation) to assess discrimination, and Time-dependent AUC 27 . Calibration was assessed by plotting predicted vs. observed survival probabilities at 5 years. Clinical utility was evaluated using Decision Curve Analysis (DCA) 28 . All statistical analyses were performed using R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Statistical analyses were performed using the rms, survival, and riskRegression packages in R. A P-value < 0.05 was considered statistically significant. Results Patient Characteristics A total of 289 patients with newly diagnosed de novo DLBCL, treated with R-CHOP or R-CHOP-like immunochemotherapy, were identified. We present the baseline survival outcomes (PFS and OS) in Supplementary Fig. S1 . The baseline characteristics of the total cohort (n = 289) are summarized in Table 1. The median age was 69 years (range: 19–97), representative of the general DLBCL population. The cohort had a slight male predominance (52.6%). Regarding standard risk stratification, the patients were distributed across the IPI risk groups as follows: Low risk (0–1 factors) 39.8%, Low-Intermediate (2 factors) 16.6%, High-Intermediate (3 factors) 18.7%, and High risk (4–5 factors) 24.9%. This distribution confirms a typical risk profile for a tertiary care center cohort. High BCL2 expression (> 50% of tumor cells) was found in 52.9% (n = 153) of patients, while hypoalbuminemia ( 2 was present in 43.6% (n = 126) of the cohort. Characteristic Category Number of Patients (%) Age (years) Median (Range) 69 (19–97) Sex Male 152 (52.6%) Female 137 (47.4%) IPI Risk Group Low (0–1) 115 (39.8%) Low-Intermediate (2) 48 (16.6%) High-Intermediate (3) 54 (18.7%) High (4–5) 72 (24.9%) AB-IPI Factors IPI Score > 2 Present 126 (43.6%) Albumin 50% Present 153 (52.9%) Table 1. Baseline characteristics of the study cohort (n = 289). Identification of Independent Prognostic Factors To validate the variables for the AB-IPI model, a multivariate Cox proportional hazards regression analysis for Overall Survival (OS) was performed in the analyzable cohort (n = 289). The analysis included the dichotomized IPI score (0–2 vs. 3–5), serum albumin (3.6 g/dL > vs. ≤ 3.6 g/dL), and BCL2 expression (50% ≥ vs. > 50%) as covariates. As shown in Table 2, all three factors emerged as statistically significant, independent predictors of poor survival. Notably, hypoalbuminemia (< 3.6 g/dL) was identified as the strongest single predictor in the model, with a Hazard Ratio (HR) of 2.62 (95% CI: 1.69–4.06; P 2 retained significant prognostic value (HR 2.13; 95% CI: 1.37–3.30; P = 0.0008), confirming that tumor burden remains a fundamental driver of mortality. BCL2 overexpression was also an independent adverse factor (HR 1.72; 95% CI: 1.18–2.52; P = 0.0061). Variable Hazard Ratio (HR) 95% Confidence Interval P-value Albumin < 3.6 g/dL 2.62 1.69–4.06 2 2.13 1.37–3.30 0.0008 BCL2 Expression > 50% 1.72 1.18–2.52 0.0061 Table 2. Multivariate Cox Proportional Hazards Analysis for Overall Survival. Risk Stratification and Survival Analysis Based on the multivariate analysis, the AB-IPI score was constructed by assigning 1 point for the presence of each adverse factor: IPI score > 2 Serum Albumin 50% The cohort was stratified into four risk groups based on the total score (0 to 3 points). Kaplan-Meier survival analysis demonstrated a robust and statistically significant separation between the four risk groups (log-rank P < 0.0001). The 5-year OS rates were: Low Risk (Score 0) : 88.0% Intermediate-1 Risk (Score 1) : 76.1% Intermediate-2 Risk (Score 2) : 45.0% High Risk (Score 3) : 29.0% The separation between the Intermediate-1 and Intermediate-2 groups was particularly pronounced (76.1% vs. 45.0%), indicating that the accumulation of a second risk factor marks a biological inflection point where standard R-CHOP efficacy drops precipitously. Performance Comparison: AB-IPI vs. Standard IPI A comprehensive evaluation of the AB-IPI against the standard IPI was performed using discrimination and calibration metrics (Table 3). Discrimination : The AB-IPI achieved an optimism-corrected Harrell’s C-index of 0.725, superior to the standard IPI's C-index of 0.702. Furthermore, the 5-year Time-dependent Area Under the Curve (AUC) was higher for the AB-IPI (0.742) compared to the IPI (0.719). Calibration : The calibration plot for 5-year OS showed excellent agreement between predicted and observed survival, with a calibration slope of 0.98 (where 1.0 represents perfect calibration). Clinical Utility : Decision Curve Analysis (DCA) demonstrated that the AB-IPI provided a higher Net Benefit than the standard IPI across a wide range of clinically relevant threshold probabilities (0.1 to 0.5), implying superior utility for guiding treatment decisions. Metric AB-IPI Standard IPI Harrell’s C-index (Optimism-corrected) 0.725 0.702 5-Year Time-dependent AUC 0.742 0.719 Calibration Slope (1.0 = ideal) 0.98 - Table 3. Performance Comparison of AB-IPI versus Standard IPI. Discussion In this study, we developed and validated the AB-IPI, a simple yet biologically grounded prognostic model for DLBCL. By integrating the macroscopic tumor burden assessment of the standard IPI with biomarkers of host systemic inflammation (serum albumin) and intrinsic tumor resistance (BCL2 protein expression), the AB-IPI provides more granular and powerful risk stratification than the standard IPI. The findings identify a distinct high-risk subgroup (Score 3) with a 5-year overall survival of less than 30%, highlighting a critical unmet need in the rituximab era. A key finding was that hypoalbuminemia (<3.6 g/dL) was the strongest single predictor of overall survival. This underscores the critical importance of the "host factor 15 . In aggressive lymphoma, hypoalbuminemia is rarely a simple reflection of nutritional intake; rather, it is a hallmark of the systemic inflammatory response 17 . The tumor microenvironment in DLBCL is rich in inflammatory cytokines, particularly IL-6 and TNF-α, which suppress hepatic albumin synthesis 16,21 . Thus, low serum albumin serves as an integrated readout of the cumulative cytokine burden and host cachexia, which are associated with immune exhaustion and altered pharmacokinetics of therapeutic antibodies like rituximab 18. The inclusion of BCL2 protein expression addresses the "tumor biology" dimension. BCL2 overexpression raises the threshold for apoptosis induced by cytotoxic chemotherapy 13,23 . While the prognostic value of BCL2 IHC has been debated in the molecular era, our real-world data confirms that BCL2 expression >50% remains an independent adverse factor. Unlike rare *MYC/BCL2* rearrangements, BCL2 protein overexpression is prevalent (52.9% in this cohort), making it a broadly applicable target for risk stratification 24 . The most immediate clinical application of the AB-IPI is the identification of the High-Risk (Score 3) group. These patients are clearly underserved by standard R-CHOP and may be prime candidates for intensified approaches, such as those incorporating polatuzumab vedotin or bispecific antibodies 25 . This study has limitations inherent to its retrospective design 10 . Although the cohort was consecutively accrued, selection bias cannot be entirely excluded. Additionally, while BCL2 expression was assessed using a standard 50% cutoff, variability in staining intensity is a known challenge, though this cutoff is widely utilized in clinical trials. Finally, external validation in independent cohorts is necessary to confirm the model's generalizability. In conclusion, the AB-IPI is a practical, biologically grounded tool that outperforms the standard IPI in the rituximab era. By capturing the triad of tumor burden, host inflammation, and biological resistance, it offers a democratized approach to precision medicine. Declarations Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions protecting patient privacy and the absence of specific patient consent for public data sharing. However, de-identified data are available from the corresponding author (Y.T.) on reasonable request, subject to approval by the Institutional Review Board . Acknowledgements We thank the clinical staff of Saitama Medical University and Otemae Hospital for their dedication to patient care. Author Contributions N.S. and Y.Tanaka conceived the study, analyzed the data, and wrote the manuscript. K.N. and Y.Takahashi collected clinical data and performed statistical validation. S.M. and M.H. performed the pathological review and BCL2 immunohistochemical scoring. T.T. supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript. Competing Interests The authors declare no competing interests. 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07:45:54","extension":"xml","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":72220,"visible":true,"origin":"","legend":"","description":"","filename":"22e0833ef29b4ea296c0ec2dc59fee001structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/c4b73957a26be0505fd0d499.xml"},{"id":100017636,"identity":"8b425b37-025d-49aa-93cb-d9a10444df51","added_by":"auto","created_at":"2026-01-12 07:10:53","extension":"html","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":84581,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/bdda8aaa4def6aa4e0b161c5.html"},{"id":100361346,"identity":"c3015042-3da0-4f7f-8ac5-f8bd55d011f3","added_by":"auto","created_at":"2026-01-16 07:45:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128043,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier estimates of overall survival stratified by the AB-IPI score (\u003cem\u003en\u003c/em\u003e=289). The AB-IPI significantly stratified patients into four distinct risk groups (Score 0, 1, 2, and 3; log-rank \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/5cc23b52a0de399ec1d398d8.png"},{"id":100017632,"identity":"f7bbceca-c901-4632-aa70-6224e96f3dcb","added_by":"auto","created_at":"2026-01-12 07:10:53","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154290,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plot for the AB-IPI model at 5 years. The solid line represents the AB-IPI performance, which closely aligns with the diagonal dotted line (perfect calibration), indicating accurate risk prediction (Slope = 0.98).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/0629b3a0122de204323e6e32.jpeg"},{"id":100017629,"identity":"d1c887ce-025b-4c6e-93a1-622908b0d396","added_by":"auto","created_at":"2026-01-12 07:10:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":110900,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Curve Analysis (DCA) for the AB-IPI and standard IPI. The AB-IPI (red line) provides a higher Net Benefit than the standard IPI (blue line) across clinically relevant threshold probabilities, demonstrating superior clinical utility.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/d63d0ae095c678f5e7fb543c.png"},{"id":101732586,"identity":"24cade28-a799-459b-af5e-5de23c635859","added_by":"auto","created_at":"2026-02-03 06:26:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1327899,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/dbbc349d-e271-467a-acb8-e15c2481a76f.pdf"},{"id":100362453,"identity":"268b46bb-547f-4cf8-998a-5b33bce190cb","added_by":"auto","created_at":"2026-01-16 07:46:45","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":206057,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-8451544/v1/e0a50a5b6388e0253f5b1145.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"AB-IPI: A Clinicopathological Prognostic Score Integrating Host Fitness and Tumor Biology in Diffuse Large B-Cell Lymphoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDiffuse large B-cell lymphoma (DLBCL) represents the most prevalent subtype of non-Hodgkin lymphoma (NHL) among adults, accounting for approximately 30\u0026ndash;40% of all lymphoid malignancies worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is a biologically and clinically heterogeneous disease entity, characterized by aggressive growth and remarkable diversity in genetic drivers, clinical presentation, and therapeutic responsiveness.\u003c/p\u003e \u003cp\u003eThe management of DLBCL has undergone a paradigm shift over the past two decades. The advent of R-CHOP therapy\u0026mdash;a combination of the anti-CD20 monoclonal antibody rituximab with cyclophosphamide, doxorubicin, vincristine, and prednisolone\u0026mdash;established itself as the global standard of care in the early 2000s following pivotal randomized trials\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. This immunochemotherapy regimen dramatically improved outcomes, shifting the cure rate from approximately 40% in the CHOP era to approximately 60\u0026ndash;70% in the rituximab era. However, the success of R-CHOP also illuminates a critical residual challenge: the management of the 30\u0026ndash;40% of patients who experience primary refractory disease or relapse after initial response. For this subset of patients, the prognosis remains dismal. The SCHOLAR-1 study highlighted that patients with refractory DLBCL have a median overall survival of only 6.3 months, with very few achieving long-term remission with conventional salvage therapies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile novel therapies such as chimeric antigen receptor T-cell (CAR-T) therapy, bispecific antibodies, and antibody-drug conjugates have provided new hope, their optimal deployment relies heavily on the ability to accurately identify high-risk patients at the time of initial diagnosis. For nearly thirty years, the International Prognostic Index (IPI) has served as the gold standard for risk stratification in aggressive lymphoma\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. While the IPI remains a foundational tool for assessing macroscopic tumor burden, its ability to discriminate between risk groups has significantly diminished in the rituximab era. The universal improvement in survival outcomes with R-CHOP has led to a phenomenon widely recognized as the \"erosion\" of the IPI's predictive power\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Specifically, the distinction between the \"high-intermediate\" and \"high-risk\" groups has blurred, with observed survival rates in modern cohorts often exceeding original IPI predictions. Consequently, patients originally classified as high-risk by the IPI now frequently achieve 5-year survival rates of 50% or higher, making it difficult to justify experimental intensified frontline therapies based on IPI score alone.\u003c/p\u003e \u003cp\u003eIn response to these limitations, alternative prognostic models have been proposed\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The Revised IPI (R-IPI) and NCCN-IPI aimed to enhance discrimination\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e. However, the NCCN-IPI introduces significant complexity to the clinical workflow, requiring normalized LDH ratios and precise radiological adjudication of extranodal sites, rendering it cumbersome for routine practice\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, these models remain strictly clinical; they measure tumor burden and patient frailty but fail to capture the underlying biological heterogeneity driving therapeutic resistance\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Simultaneously, molecular risk stratification via Cell-of-Origin (COO) subtypes or \"Double Hit\" status has become standard\u003csup\u003e\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, yet these technologies are often costly and unavailable in resource-limited settings.\u003c/p\u003e \u003cp\u003eThere is a pressing need for a prognostic tool that bridges the gap between the clinical utility of the IPI and the biological precision of molecular profiling using universally accessible biomarkers\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Accurate prognostication requires a holistic approach integrating three distinct pillars: \"tumor burden,\" \"host fitness,\" and \"tumor biology.\" To achieve this integration without relying on inaccessible genomic testing, this study focused on two readily available biomarkers: serum albumin and BCL2 protein expression. Serum albumin functions as a potent surrogate for systemic inflammation and host resilience\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23 CR24\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, while BCL2 protein expression captures biological resistance to apoptosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to develop the AB-IPI (Albumin-BCL2 Refined Prognostic Index), a novel scoring system integrating the standard IPI with serum albumin and BCL2 protein expression. We hypothesized that this simple 3-factor model would provide superior risk stratification and clinical utility compared to the standard IPI by effectively capturing the triad of tumor burden, host inflammation, and biological resistance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a retrospective observational analysis of 289 consecutive patients with newly diagnosed \u003cem\u003ede novo\u003c/em\u003e Diffuse Large B-cell Lymphoma (DLBCL), Not Otherwise Specified (NOS), treated at two tertiary care institutions: Saitama Medical University Saitama Medical Center and Otemae Hospital. The study period spanned from January 2008 to December 2020.\u003c/p\u003e\n\u003cp\u003eInclusion criteria were: (1) histologically confirmed DLBCL, NOS according to the WHO classification\u003csup\u003e26\u003c/sup\u003e (2) age \u0026gt; 18 years; and (3) treatment with R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, Prednisolone) or R-CHOP-like immunochemotherapy with curative intent as first-line therapy.\u003c/p\u003e\n\u003cp\u003eExclusion criteria included: (1) primary Central Nervous System (CNS) lymphoma; (2) transformed lymphoma (e.g., from follicular lymphoma); (3) HIV-associated lymphoma; and (4) incomplete clinical data preventing the calculation of IPI or missing BCL2/Albumin data. The final study cohort consisted of 289 out of 316 cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe study protocol was approved by the Ethics Committees of Saitama Medical University Saitama Medical Center and Otemae Hospital (No. 2025-114) and adhered to the Ethical Guidelines for Medical and Health Research Involving Human Subjects in Japan. All methods were performed in accordance with the relevant guidelines and regulations. Due to the retrospective nature of the study, the requirement for informed consent was waived, and an opt-out method was employed.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection and Definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data were extracted from electronic medical records. Baseline characteristics included age, sex, ECOG performance status (PS), serum lactate dehydrogenase (LDH) levels, Ann Arbor stage, and extranodal involvement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiomarker Assessment:\u003c/strong\u003e Serum albumin was measured at diagnosis; a cutoff of \u0026lt;3.6 g/dL was determined based on receiver operating characteristic (ROC) curve analysis (Supplementary Fig. S2). BCL2 protein expression was evaluated on formalin-fixed paraffin-embedded (FFPE) tissue using immunohistochemistry (IHC). Positivity was defined as \u0026gt;50% of tumor cells exhibiting cytoplasmic staining\u003csup\u003e21,24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary endpoint was Overall Survival (OS), calculated from the date of diagnosis to the date of death from any cause or the last follow-up. The prognostic impact of variables was assessed using the Kaplan-Meier method and log-rank test. A multivariate Cox proportional hazards regression model was constructed to identify independent prognostic factors.\u003c/p\u003e\n\u003cp\u003eThe AB-IPI score was derived from significant variables identified in the multivariate analysis. Model performance was evaluated using Harrell’s C-index (Internal validation was performed using 1,000 bootstrap resamples to calculate the optimism-corrected C-index, adhering to the TRIPOD guidelines for type 2b validation) to assess discrimination, and Time-dependent AUC \u003csup\u003e27\u003c/sup\u003e. Calibration was assessed by plotting predicted vs. observed survival probabilities at 5 years. Clinical utility was evaluated using Decision Curve Analysis (DCA)\u003csup\u003e28\u003c/sup\u003e. All statistical analyses were performed using R software (version 4.2.1; R Foundation for Statistical Computing, Vienna, Austria). Statistical analyses were performed using the rms, survival, and riskRegression packages in R. A P-value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePatient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 289 patients with newly diagnosed \u003cem\u003ede novo\u003c/em\u003e DLBCL, treated with R-CHOP or R-CHOP-like immunochemotherapy, were identified. We present the baseline survival outcomes (PFS and OS) in Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe baseline characteristics of the total cohort (n\u0026thinsp;=\u0026thinsp;289) are summarized in Table\u0026nbsp;1. The median age was 69 years (range: 19\u0026ndash;97), representative of the general DLBCL population. The cohort had a slight male predominance (52.6%). Regarding standard risk stratification, the patients were distributed across the IPI risk groups as follows: Low risk (0\u0026ndash;1 factors) 39.8%, Low-Intermediate (2 factors) 16.6%, High-Intermediate (3 factors) 18.7%, and High risk (4\u0026ndash;5 factors) 24.9%. This distribution confirms a typical risk profile for a tertiary care center cohort. High BCL2 expression (\u0026gt;\u0026thinsp;50% of tumor cells) was found in 52.9% (n\u0026thinsp;=\u0026thinsp;153) of patients, while hypoalbuminemia (\u0026lt;\u0026thinsp;3.6 g/dL) was observed in 40.8% (n\u0026thinsp;=\u0026thinsp;118). An IPI score\u0026thinsp;\u0026gt;\u0026thinsp;2 was present in 43.6% (n\u0026thinsp;=\u0026thinsp;126) of the cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of Patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (Range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (19\u0026ndash;97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (52.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137 (47.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIPI Risk Group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow (0\u0026ndash;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow-Intermediate (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48 (16.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh-Intermediate (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (18.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh (4\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72 (24.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAB-IPI Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPI Score\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126 (43.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;3.6 g/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCL2 Expression\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTable\u0026nbsp;1. Baseline characteristics of the study cohort (n\u0026thinsp;=\u0026thinsp;289).\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Independent Prognostic Factors\u003c/h2\u003e \u003cp\u003eTo validate the variables for the AB-IPI model, a multivariate Cox proportional hazards regression analysis for Overall Survival (OS) was performed in the analyzable cohort (n\u0026thinsp;=\u0026thinsp;289). The analysis included the dichotomized IPI score (0\u0026ndash;2 vs. 3\u0026ndash;5), serum albumin (3.6 g/dL\u0026thinsp;\u0026gt;\u0026thinsp;vs. \u0026le; 3.6 g/dL), and BCL2 expression (50% \u0026ge; vs. \u0026gt; 50%) as covariates.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;2, all three factors emerged as statistically significant, independent predictors of poor survival. Notably, hypoalbuminemia (\u0026lt;\u0026thinsp;3.6 g/dL) was identified as the strongest single predictor in the model, with a Hazard Ratio (HR) of 2.62 (95% CI: 1.69\u0026ndash;4.06; P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This reinforces the hypothesis that host inflammation and nutritional fitness are critical determinants of outcomes in the rituximab era. IPI score\u0026thinsp;\u0026gt;\u0026thinsp;2 retained significant prognostic value (HR 2.13; 95% CI: 1.37\u0026ndash;3.30; P\u0026thinsp;=\u0026thinsp;0.0008), confirming that tumor burden remains a fundamental driver of mortality. BCL2 overexpression was also an independent adverse factor (HR 1.72; 95% CI: 1.18\u0026ndash;2.52; P\u0026thinsp;=\u0026thinsp;0.0061).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard Ratio (HR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlbumin\u0026thinsp;\u0026lt;\u0026thinsp;3.6 g/dL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026ndash;4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIPI Score\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.37\u0026ndash;3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBCL2 Expression\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.18\u0026ndash;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTable\u0026nbsp;2. Multivariate Cox Proportional Hazards Analysis for Overall Survival.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRisk Stratification and Survival Analysis\u003c/h3\u003e\n\u003cp\u003eBased on the multivariate analysis, the AB-IPI score was constructed by assigning 1 point for the presence of each adverse factor:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIPI score\u0026thinsp;\u0026gt;\u0026thinsp;2\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSerum Albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.6 g/dL\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBCL2 Expression\u0026thinsp;\u0026gt;\u0026thinsp;50%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe cohort was stratified into four risk groups based on the total score (0 to 3 points). Kaplan-Meier survival analysis demonstrated a robust and statistically significant separation between the four risk groups (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The 5-year OS rates were:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLow Risk (Score 0)\u003c/b\u003e: 88.0%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntermediate-1 Risk (Score 1)\u003c/b\u003e: 76.1%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIntermediate-2 Risk (Score 2)\u003c/b\u003e: 45.0%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh Risk (Score 3)\u003c/b\u003e: 29.0%\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe separation between the Intermediate-1 and Intermediate-2 groups was particularly pronounced (76.1% vs. 45.0%), indicating that the accumulation of a second risk factor marks a biological inflection point where standard R-CHOP efficacy drops precipitously.\u003c/p\u003e\n\u003ch3\u003ePerformance Comparison: AB-IPI vs. Standard IPI\u003c/h3\u003e\n\u003cp\u003eA comprehensive evaluation of the AB-IPI against the standard IPI was performed using discrimination and calibration metrics (Table\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDiscrimination\u003c/b\u003e: The AB-IPI achieved an optimism-corrected Harrell\u0026rsquo;s C-index of 0.725, superior to the standard IPI's C-index of 0.702. Furthermore, the 5-year Time-dependent Area Under the Curve (AUC) was higher for the AB-IPI (0.742) compared to the IPI (0.719).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCalibration\u003c/b\u003e: The calibration plot for 5-year OS showed excellent agreement between predicted and observed survival, with a calibration slope of 0.98 (where 1.0 represents perfect calibration).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eClinical Utility\u003c/b\u003e: Decision Curve Analysis (DCA) demonstrated that the AB-IPI provided a higher Net Benefit than the standard IPI across a wide range of clinically relevant threshold probabilities (0.1 to 0.5), implying superior utility for guiding treatment decisions.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAB-IPI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard IPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHarrell\u0026rsquo;s C-index (Optimism-corrected)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5-Year Time-dependent AUC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCalibration Slope (1.0\u0026thinsp;=\u0026thinsp;ideal)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTable\u0026nbsp;3. Performance Comparison of AB-IPI versus Standard IPI.\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated the AB-IPI, a simple yet biologically grounded prognostic model for DLBCL. By integrating the macroscopic tumor burden assessment of the standard IPI with biomarkers of host systemic inflammation (serum albumin) and intrinsic tumor resistance (BCL2 protein expression), the AB-IPI provides more granular and powerful risk stratification than the standard IPI. The findings identify a distinct high-risk subgroup (Score 3) with a 5-year overall survival of less than 30%, highlighting a critical unmet need in the rituximab era.\u003c/p\u003e\n\u003cp\u003eA key finding was that hypoalbuminemia (\u0026lt;3.6 g/dL) was the strongest single predictor of overall survival. This underscores the critical importance of the \"host factor\u003csup\u003e15\u003c/sup\u003e. In aggressive lymphoma, hypoalbuminemia is rarely a simple reflection of nutritional intake; rather, it is a hallmark of the systemic inflammatory response\u003csup\u003e17\u003c/sup\u003e. The tumor microenvironment in DLBCL is rich in inflammatory cytokines, particularly IL-6 and TNF-α, which suppress hepatic albumin synthesis\u003csup\u003e16,21\u003c/sup\u003e. Thus, low serum albumin serves as an integrated readout of the cumulative cytokine burden and host cachexia, which are associated with immune exhaustion and altered pharmacokinetics of therapeutic antibodies like rituximab\u003csup\u003e18.\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion of BCL2 protein expression addresses the \"tumor biology\" dimension. BCL2 overexpression raises the threshold for apoptosis induced by cytotoxic chemotherapy\u003csup\u003e13,23\u003c/sup\u003e. While the prognostic value of BCL2 IHC has been debated in the molecular era, our real-world data confirms that BCL2 expression \u0026gt;50% remains an independent adverse factor. Unlike rare *MYC/BCL2* rearrangements, BCL2 protein overexpression is prevalent (52.9% in this cohort), making it a broadly applicable target for risk stratification\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe most immediate clinical application of the AB-IPI is the identification of the High-Risk (Score 3) group. These patients are clearly underserved by standard R-CHOP and may be prime candidates for intensified approaches, such as those incorporating polatuzumab vedotin or bispecific antibodies\u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis study has limitations inherent to its retrospective design\u003csup\u003e10\u003c/sup\u003e. Although the cohort was consecutively accrued, selection bias cannot be entirely excluded. Additionally, while BCL2 expression was assessed using a standard 50% cutoff, variability in staining intensity is a known challenge, though this cutoff is widely utilized in clinical trials. Finally, external validation in independent cohorts is necessary to confirm the model's generalizability.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the AB-IPI is a practical, biologically grounded tool that outperforms the standard IPI in the rituximab era. By capturing the triad of tumor burden, host inflammation, and biological resistance, it offers a democratized approach to precision medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions protecting patient privacy and the absence of specific patient consent for public data sharing. However, de-identified data are available from the corresponding author (Y.T.) on reasonable request, subject to approval by the Institutional Review Board\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the clinical staff of Saitama Medical University and Otemae Hospital for their dedication to patient care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.S. and Y.Tanaka conceived the study, analyzed the data, and wrote the manuscript. K.N. and Y.Takahashi collected clinical data and performed statistical validation. S.M. and M.H. performed the pathological review and BCL2 immunohistochemical scoring. T.T. supervised the study and critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;No Funding\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSehn, L. H. \u0026amp; Salles, G. Diffuse Large B-Cell Lymphoma. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e\u003cstrong\u003e384\u003c/strong\u003e, 842\u0026ndash;858 (2021).\u003c/li\u003e\n \u003cli\u003eCoiffier, B. \u003cem\u003eet al.\u003c/em\u003e CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. \u003cem\u003eN. Engl. J. 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Making\u003c/em\u003e\u003cstrong\u003e26\u003c/strong\u003e, 565\u0026ndash;574 (2006).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Diffuse Large B-cell Lymphoma, Prognosis, R-CHOP, International Prognostic Index, Serum Albumin, BCL2","lastPublishedDoi":"10.21203/rs.3.rs-8451544/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8451544/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe introduction of rituximab has eroded the discriminatory power of the International Prognostic Index (IPI) in diffuse large B-cell lymphoma (DLBCL), necessitating refined risk stratification. To address this, we developed the AB-IPI, a prognostic tool integrating tumor burden with host inflammation and biological resistance. In a retrospective analysis of 289 patients treated with R-CHOP-like immunochemotherapy, multivariate Cox regression identified IPI score\u0026thinsp;\u0026gt;\u0026thinsp;2, serum albumin\u0026thinsp;\u0026lt;\u0026thinsp;3.6 g/dL, and BCL2 protein expression\u0026thinsp;\u0026gt;\u0026thinsp;50% as independent prognostic factors for overall survival. The AB-IPI stratified patients into four risk groups with 5-year survival rates ranging from 88.0% (Low) to 29.0% (High) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Crucially, the AB-IPI demonstrated superior discrimination (C-index: 0.725 vs. 0.702) and calibration compared to the standard IPI, with Decision Curve Analysis confirming greater net benefit. By capturing the triad of tumor burden, host fitness, and tumor biology using universally accessible biomarkers, the AB-IPI offers a robust, practical alternative for identifying high-risk patients in the rituximab era.\u003c/p\u003e","manuscriptTitle":"AB-IPI: A Clinicopathological Prognostic Score Integrating Host Fitness and Tumor Biology in Diffuse Large B-Cell Lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 07:10:48","doi":"10.21203/rs.3.rs-8451544/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d024eb2f-cc27-45a9-a69b-e0cb7c7a64a3","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60826199,"name":"Health sciences/Biomarkers"},{"id":60826200,"name":"Biological sciences/Cancer"},{"id":60826201,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-02-03T06:26:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 07:10:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8451544","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8451544","identity":"rs-8451544","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}