bims-necame Biomed News
on Metabolism in small cell neuroendocrine cancers
Issue of 2025–10–12
thirteen papers selected by
Grigor Varuzhanyan, UCLA
  1. Metabolic and imaging phenotypes associated with RB1 and TP53 loss in prostate cancer.
  2. Molecular Subtypes of Neuroendocrine Carcinoma: From Chaos to Consensus.
  3. Prognostic significance of liver-to-muscle FDG uptake ratio and ınflammatory biomarkers in small cell lung cancer.
  4. Antibody Drugs Conjugates in Small-Cell Lung Cancer: Present-Day Status and Promises.
  5. Basal cells drive small cell lung cancer plasticity.
  6. ARID1A Governs Genomic Stability and Proliferation in SCLC via c-MYC/PARP1 Suppression Driving Vulnerability to BET Inhibitors.
  7. Models of High-Grade Serous Ovarian Carcinoma.
  8. Negative associations between macronutrient quality index and lung cancer incidence and mortality: results from the prostate, lung, colorectal, and ovarian cancer screening trial.
  9. Spliceosome inhibition induces Z-RNA and ZBP1-driven cell death in small cell lung cancer.
  10. C11orf53 Promotes the Progression of Small Cell Lung Cancer by Regulating Glucose Metabolism.
  11. A computer-aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [18F]- PET/CT images.
  12. IDeate-Lung02: a Phase 3 study of second-line ifinatamab deruxtecan in patients with relapsed small cell lung cancer.
  13. Predicting response and survival to first-line treatment with baseline [18F]FDG-PET-CT in patients with small-cell lung cancer: an integrated diagnostic approach.
  1. Neoplasia. 2025 Oct 07. pii: S1476-5586(25)00115-0. [Epub ahead of print]70 101235
    Metabolic and imaging phenotypes associated with RB1 and TP53 loss in prostate cancer.
    Fahim Ahmad, Margaret White, Kazutoshi Yamamoto, Daniel R Crooks, Supreet Agarwal, Ye Yang, Brian Capaldo, Sonam Raj, Aian Neil Alilin, Anita Ton, Stephen Adler, Jurgen Seidel, Colleen Olkowski, Murali Krishna Cherukuri, Peter L Choyke, Kathleen Kelly, Jeffrey R Brender.
      Advanced prostate cancer is treated with androgen receptor (AR) signaling inhibitors, which are initially effective, but most patients eventually develop resistance and progress to castrate-resistant prostate cancer (CRPC). Loss of RB1 in CRPC tumors is correlated with rapid progression and poor patient survival and, in combination with TP53 loss, predisposes patients to the development of transitional neuroendocrine prostate cancer (NEPC). Although progressive CRPC is clinically associated with higher 18FDG-PET SUVmax values, it is unknown whether inactivation of RB1 and/or TP53 is a driver of increased glucose import. Using a cohort of patient-derived xenograft (PDX)-derived CRPC organoids, we found that NEPC could not be conclusively distinguished from adenocarcinoma by 18FDG uptake alone, and PSMA protein levels did not correlate with cancer phenotype or 18FDG uptake. Castration-resistant models showed higher 18FDG uptake, but lower pyruvate-to-lactate conversion compared to their castration-sensitive counterparts. In parallel studies using castration-sensitive prostate cancer models, RB1/TP53 knockdown did not affect 18FDG uptake, but increased basal respiration and glycolytic activity, with combined depletion leading to glucose diversion into glycogenesis. These metabolic changes were reflected in increased lactate dehydrogenase flux detected by 13C-hyperpolarized magnetic resonance spectroscopy upon RB1 loss, but not in 18FDG uptake. The metabolic heterogeneity revealed here suggests that a multimodal molecular imaging approach can improve tumor characterization, potentially leading to a better prognosis in cancer treatment.
    Keywords:  FDG-PET; Metabolic reprograming; Metabolism; Prostate cancer; RB1; TP53
    DOI:  https://doi.org/10.1016/j.neo.2025.101235
  2. Annu Rev Pathol. 2025 Oct 08.
    Molecular Subtypes of Neuroendocrine Carcinoma: From Chaos to Consensus.
    Zhanyu Wang, Nan Sun, Jie He, Esther Redin, Charles M Rudin.
      Neuroendocrine carcinomas (NECs) represent a notoriously aggressive family of lethal malignancies arising across diverse anatomical sites. Molecular subtyping based on key transcription factors ASCL1, NEUROD1, POU2F3, and YAP1 has significantly advanced understanding of tumor heterogeneity in small cell lung cancer (SCLC). Beyond SCLC, extrapulmonary NECs demonstrate analogous heterogeneity, similarly governed by these transcriptional determinants. Recent studies have further identified a fifth subtype driven by the lineage-specifying factor HNF4A. This review aims to propose a unified pan-NEC classification framework for consistent molecular subtyping across pulmonary, gastro-entero-pancreatic (GEP), and genitourinary systems. We delineate the distinct lineage hallmarks of the ANHPY subtypes (neuroendocrine, neuronal, GEP-like, tuft-like, and epithelial-mesenchymal transition phenotypes) and explore their connections to defining mechanisms, genetic alterations, clinicopathological features, and therapeutic vulnerabilities. This unified framework serves as a molecular roadmap for precise NEC research and management.
    DOI:  https://doi.org/10.1146/annurev-pathmechdis-042524-023153
  3. Clin Transl Oncol. 2025 Oct 06.
    Prognostic significance of liver-to-muscle FDG uptake ratio and ınflammatory biomarkers in small cell lung cancer.
    Kadriye Başkurt, Semra Demirtaş Şenlik, Galip Can Uyar, Enes Yeşilbaş, Ömür Berna Çakmak Öksüzoğlu, Osman Sütcüoğlu.
       BACKGROUND: Small cell lung cancer (SCLC) is an aggressive malignancy frequently complicated by systemic inflammation, cachexia, and metabolic dysfunction. While 18F-FDG PET/CT is routinely used for disease staging, its potential to reflect host metabolic status through tissue-specific uptake metrics remains underexplored. We investigated the prognostic significance of the liver-to-rectus femoris mean standardized uptake value ratio (LRF) alongside systemic inflammatory markers in patients with SCLC.
    METHODS: This retrospective study included 155 newly diagnosed SCLC patients who underwent baseline 18F-FDG PET/CT prior to systemic therapy. Quantitative PET/CT metrics-particularly LRF SUVmean-were analyzed in relation to clinical characteristics, inflammatory indices (CRP-to-albumin ratio [CAR], neutrophil-to-lymphocyte ratio [NLR]), and survival outcomes. Kaplan-Meier and multivariate Cox regression analyses were used to assess progression-free survival (PFS) and overall survival (OS).
    RESULTS: An elevated LRF ratio (≥ 3.18) was independently associated with shorter PFS (7.52 vs. 10.22 months; p = 0.047) and OS (7.85 vs. 9.40 months; p = 0.021) in extensive-stage SCLC. Similarly, patients with CAR ≥ 0.29 had significantly worse progression-free survival (7.10 vs. 11.50 months; p = 0.001) and overall survival (7.55 vs. 13.74 months; p = 0.008) compared to those with CAR < 0.29. LRF SUVmean positively correlated with CAR and negatively with serum albumin. In contrast, NLR was not significantly associated with survival outcomes.
    CONCLUSION: The LRF SUVmean ratio represents a novel, noninvasive PET/CT-derived biomarker that reflects host metabolic frailty and correlates with systemic inflammation. Integration of metabolic imaging parameters such as LRF with established laboratory markers may improve prognostic stratification in SCLC and guide supportive care strategies.
    Keywords:  CRP-to-albumin ratio; Liver-to-rectus femoris SUVmean; Metabolic imaging; PET/CT; Prognosis; Small cell lung cancer; Systemic inflammation
    DOI:  https://doi.org/10.1007/s12094-025-04070-1
  4. Oncologist. 2025 Oct 06. pii: oyaf332. [Epub ahead of print]
    Antibody Drugs Conjugates in Small-Cell Lung Cancer: Present-Day Status and Promises.
    Arjun Syal, May-Lucie Meyer, Kenneth Angelino, Noah Osei, Jorge E Gomez, Fred R Hirsch, Triparna Sen.
       INTRODUCTION: Small cell lung cancer (SCLC) accounts for 15% of lung cancers and is characterized by an aggressive disease course and historically poor prognosis. Although tumors in most patients respond to initial chemotherapy, relapse is nearly universal and treatment options remain limited. Antibody-drug conjugates (ADCs) have emerged as a novel therapeutic class with potential to address this unmet need.
    METHODS: ClinicalTrials.gov, PubMed, and their associated references and press releases were queried for the search terms "Antibody-drug conjugates" and "SCLC." Only English-language sources were included.
    RESULTS: Multiple ADCs targeting diverse antigens have been evaluated in relapsed or refractory SCLC. Topoisomerase I inhibitor payloads have generated the most consistent activity across DLL3, TROP2, B7-H3, and SEZ6 targets, while microtubule and pyrrolobenzodiazepine (PBD)- based constructs have not demonstrated durable benefit. Despite encouraging response rates, progression-free survival has remained short, reflecting intrinsic resistance, antigen heterogeneity, and toxicity-related dose limitations.
    CONCLUSION: While ADCs have generated encouraging response rates in SCLC, durability has remained limited. Future development will require optimization of payloads, linkers, and trial design to determine whether ADCs can achieve a sustained role in this disease.
    Keywords:  antibody-drug conjugates; biomarkers; small cell lung cancer; targeted therapy; thoracic oncology
    DOI:  https://doi.org/10.1093/oncolo/oyaf332
  5. Nat Genet. 2025 Oct;57(10): 2349
    Basal cells drive small cell lung cancer plasticity.
    Safia Danovi.
      
    DOI:  https://doi.org/10.1038/s41588-025-02387-9
  6. Research (Wash D C). 2025 ;8 0908
    ARID1A Governs Genomic Stability and Proliferation in SCLC via c-MYC/PARP1 Suppression Driving Vulnerability to BET Inhibitors.
    Guozhen Cao, Liying Ma, Xueqin Dai, Peng Hou, Xinhuang Yao, Gongfeng Li, Jiahui Zhang, Ceshi Chen, Wenchu Lin.
      Small cell lung cancer (SCLC) is a highly aggressive neuroendocrine tumor among the most lethal cancers. ARID1A has a dual role in oncogenic and tumor-suppressive functions, depending on the type of cancer. However, its role in SCLC remains unclear. Herein, we showed that ARID1A was highly expressed and correlated with prognosis in SCLC. In vitro and in vivo investigations manifested that ARID1A inhibited cell survival, proliferation, and tumor growth, functioning as a gatekeeper for cell proliferation and a caretaker of genome stability in SCLC cells. Mechanistically, ARID1A transcriptionally represses c-MYC and PARP1 expression. ARID1A depletion triggered replication stress response (RSR), DNA double-strand breaks (DSBs), and PI3K/AKT pathway activation, which could be counteracted by c-MYC or PARP1 silencing. These findings establish ARID1A as a critical antagonist of c-MYC and PARP1 signaling, coordinating proliferation control and genomic integrity maintenance. Furthermore, we revealed that ARID1A loss confers therapeutic vulnerability to the BET inhibitor (JQ1). The ARID1A-targeting compound BRD-K98645985 exhibited potent single-agent antitumor activity and synergized with JQ1 to suppress SCLC progression, highlighting a novel combinatorial therapeutic strategy. Collectively, our findings elucidate ARID1A as a critical regulator of SCLC pathogenesis through its dual control of proliferation and genomic stability while revealing novel therapeutic vulnerabilities that can be exploited through ARID1A-targeting strategies and BET inhibitor combinations.
    DOI:  https://doi.org/10.34133/research.0908
  7. Cold Spring Harb Perspect Med. 2025 Oct 06. pii: a041949. [Epub ahead of print]
    Models of High-Grade Serous Ovarian Carcinoma.
    Oscar J Pundel, Benjamin G Neel.
      High-grade serous ovarian carcinoma (HGSC) remains an incompletely understood, highly lethal disease. Historically, a lack of fidelitous in vitro and in vivo models representing HGSC biology and therapy response has been a major barrier to progress. As we discuss below, multiple (if not most) early studies used-and some investigators continue to use-human "ovarian cancer cell lines" that lack key genomic/genetic features of HGSC, rendering their conclusions questionable. The frequently deployed ID8 syngeneic mouse model is similarly suspect, as it derives from ovarian surface epithelium (OSE) and is Trp53 wild-type. In contrast, most, if not all, HGSC arises in fallopian tube epithelium (FTE), and bona fide HGSC is universally TP53 mutant or silenced. Over the past 10 years, attempts have been made to rectify these historical deficiencies, including careful assessment of the genetic composition of standard ovarian cancer cell lines and the development of mouse and human organoids, genetically engineered mouse models (GEMMs), and patient-derived xenografts (PDXs). In this review, we discuss these advances, exploring their differences, strengths, and weaknesses. We also describe "next-generation" approaches to more faithfully model HGSC cells in the context of a more realistic tumor microenvironment.
    DOI:  https://doi.org/10.1101/cshperspect.a041949
  8. Front Nutr. 2025 ;12 1647438
    Negative associations between macronutrient quality index and lung cancer incidence and mortality: results from the prostate, lung, colorectal, and ovarian cancer screening trial.
    Dazhan Feng, Heng Su, Qi Wei, Yi Xiao, Haitao Gu, Linglong Peng, Yuxiang Luo, Ling Xiang, Junxia Xue, Yunhao Tang, Citong Zhang, Dengliang Liu.
       Background: Prior research has not examined the connection between the quality of macronutrients and the occurrence as well as fatality rates of lung cancer (LC). Consequently, to delve deeper into the correlations between macronutrient quality and the likelihood of developing LC, we carried out an extensive, long-term prospective cohort study of 101,755 American adults from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.
    Methods: Our research cohort comprised 154,887 adults, aged between 55 and 74, who were enrolled from 10 screening facilities across the United States. The macronutrient quality index (MQI) was derived from participants' responses to a dietary history questionnaire (DHQ). To quantify the strength and precision of the relationships between MQI and the incidence as well as mortality of LC, we employed Cox proportional hazards regression modeling to estimate hazard ratios (HRs) alongside their corresponding 95% confidence intervals (CIs). Additionally, we conducted subgroup analyses to scrutinize whether the observed link between MQI and LC risk was subject to modification by potential confounding variables. To reinforce the reliability of our results, sensitivity analyses were also carried out.
    Results: Over an average follow-up period spanning 8.82 ± 1.95 years (accumulating to 897,809 person-years of observation), we recorded 1,706 LC diagnoses, encompassing 1,464 cases of non-small cell lung cancer (NSCLC) and 242 cases of small cell lung cancer (SCLC). Additionally, there were 1,217 deaths attributed to LC, with 1,005 NSCLC-related and 212 SCLC-related fatalities. Our results demonstrate a distinct, statistically significant inverse association between a higher MQI and both a reduced incidence (HR Q4 vs. Q1: 0.65; 95% CI: 0.56-0.76; p < 0.001 for trend) and decreased mortality (HR Q4 vs. Q1: 0.71; 95% CI: 0.60-0.84; p < 0.001 for trend) of LC. This inverse relationship held true for both NSCLC and SCLC subtypes. The robustness of the associations between MQI and the incidence as well as mortality of LC was solidly affirmed through sensitivity analyses.
    Conclusion: Our research outcomes imply that prioritizing the intake of higher-quality macronutrients could serve as a viable strategy to mitigate LC risk within the American population.
    Keywords:  cancer prevention; cohort study; epidemiology; lung cancer; macronutrient quality index
    DOI:  https://doi.org/10.3389/fnut.2025.1647438
  9. Cell Rep. 2025 Oct 04. pii: S2211-1247(25)01155-6. [Epub ahead of print]44(10): 116384
    Spliceosome inhibition induces Z-RNA and ZBP1-driven cell death in small cell lung cancer.
    Xinpei Jiang, Xueying Ma, Yunyun Zhou, Xiaodan Liu, Ting Zhang, William Kim, Siddharth Balachandran, Israel Cañadas.
      Spliceosome inhibitors emerged as promising anticancer agents. Recent studies have demonstrated that spliceosome-targeted therapies (STTs) trigger antitumor immune responses by inducing the accumulation of right-handed double-stranded (ds)RNA (A-RNA), resulting in the activation of RIG-I-like receptors (RLRs) and type I interferon-driven antiviral responses. Here, we show that spliceosome inhibition by pharmacological or genetic neutralization of SF3B1 activity induces the accumulation of endogenous left-handed dsRNAs (Z-RNAs) derived from intron-retained RNAs. These Z-RNAs activate the Z-form nucleic acid-sensor ZBP1, which triggers cell death in mouse embryonic fibroblasts and small cell lung cancer (SCLC) cells. Spliceosome inhibition induced potent ZBP1-dependent cell death in cancer-associated fibroblasts, which was essential for enhancing immunotherapy response in mouse models of SCLC. Collectively, these results demonstrate that spliceosome inhibitors can be used to generate Z-RNA and trigger on-demand ZBP1-dependent cell death in cells of the tumor microenvironment (TME) as a therapeutic strategy to enhance immunotherapy responses in resistant cancers.
    Keywords:  CP: Cancer; CP: Molecular biology; SCLC; SF3B1; STTs; Z-RNA; ZBP1; immunotherapy; necroptosis; small cell lung cancer; spliceosome; spliceosome-targeted therapy
    DOI:  https://doi.org/10.1016/j.celrep.2025.116384
  10. Chem Biodivers. 2025 Oct 10. e01157
    C11orf53 Promotes the Progression of Small Cell Lung Cancer by Regulating Glucose Metabolism.
    Shuxian Liao, Mengxia Zhang, Shengfen Li, Yuan Gao, Ruilei Huang, Xuhong Zhao, Qian Ning, Shengsong Tang.
      Small cell lung cancer (SCLC), the most malignant subtype of lung cancer, is a major cause of death among lung cancer patients. Drug resistance, high recurrence, and limitations of surgery are all obstacles to SCLC treatment. Consequently, clarifying the underlying mechanism of SCLC progression and identifying potential targets for therapeutic intervention are of paramount significance for improving the clinical outcomes of SCLC patients. C11orf53 has been demonstrated to play a crucial role in the NCI-H526 cell activity. However, few studies have focused on how C11orf53 affects the activity of NCI-H526 cells and the corresponding regulatory pathways. Herein, our study shows that C11orf53 affects the viability and proliferation of SCLC NCI-H526 cells by influencing the glycolytic pathway. We established the C11orf53 overexpression and knockdown systems in the NCI-H526 cell line with C11orf53-specific small-interfering RNA and lentivirus to assess the effects of C11orf53 on the activity and proliferation of NCI-H526 cells. Furthermore, the NCI-H526 cells with C11orf53 knockdown and overexpression were utilized to elucidate the molecular mechanism of C11orf53. Our study shows that C11orf53 knockdown significantly reduced the viability and proliferation of NCI-H526 cells. Additionally, adenosine triphosphate levels, glucose consumption, lactate secretion, and the expression of key enzymes involved in the glycolytic pathway were markedly decreased in NCI-H526 cells. These findings confirmed that the effect of C11orf53 on the activity and proliferation of NCI-H526 cells is mediated by its role in regulating cellular glycolysis.
    Keywords:  C11orf53; NCI‐H526; glycolysis; metabolism; small cell lung cancer
    DOI:  https://doi.org/10.1002/cbdv.202501157
  11. J Appl Clin Med Phys. 2025 Oct;26(10): e70285
    A computer-aided diagnosis (CAD) system based on convolutional neural networks for lung cancer diagnosis from 2D [18F]- PET/CT images.
    Mohammad Karimpour, Neda Taghinezhad, Alireza Mehdizadeh, Mehrosadat Alavi, Tahereh Mahmoudi.
       OBJECTIVE: This study aims to automatically classify lung conditions into normal, non-small cell lung cancer (NSCLC), and small cell lung cancer (SCLC) using [18F] FDG PET/CT images and deep learning.
    METHODS: PET/CT scans from 146 patients (1974 scans) were retrospectively analyzed using two strategies: (1) transfer learning with pre-trained CNNs, and (2) a custom CNN (Res-SE Net) incorporating residual and squeeze-and-excitation (SE) modules. A patient-based data splitting approach was used to avoid data leakage. Models were trained and validated at the scan level and evaluated at the patient level using majority voting. Grad-CAM was employed to generate lesion-localization heatmaps.
    RESULTS: Among the seven evaluated CNN models, the proposed Res-SE Net demonstrated superior performance, achieving an accuracy of 91.67% and a sensitivity of 92.00% in detecting NSCLC, and an accuracy of 90.14% with a sensitivity of 90.00% for distinguishing SCLC cases. When tested on an external dataset, the model attained an accuracy of 98.00% in binary classification (Normal vs. Cancer). In the three-class classification task, the model achieved an accuracy of 73.02% for NSCLC and 66.26% for SCLC.
    CONCLUSION: These findings demonstrate the potential of Res-SE Net architecture for accurate multi-class lung cancer classification using [18F] FDG PET/CT images.
    Keywords:  (18F) fluorodeoxyglucose; PET/CT; computer aided diagnosis (CAD); convolutional neural network (CNN); lung cancer
    DOI:  https://doi.org/10.1002/acm2.70285
  12. Future Oncol. 2025 Oct 07. 1-8
    IDeate-Lung02: a Phase 3 study of second-line ifinatamab deruxtecan in patients with relapsed small cell lung cancer.
    Taofeek K Owonikoko, Lauren Byers, Ying Cheng, Hidetoshi Hayashi, Luis Paz-Ares, Maurice Pérol, Haichuan Hu, Meng Qian, Cecilio Roi Garcia, Juliette Godard, Mei Tang, Charles M Rudin.
      Patients with small cell lung cancer (SCLC) have poor prognosis and limited treatment options beyond first-line therapy. B7 homolog 3 (B7-H3) is minimally expressed in normal tissues but highly expressed in SCLC. Ifinatamab deruxtecan (I-DXd), a B7-H3-directed antibody-drug conjugate, has demonstrated promising efficacy and a manageable safety profile in various tumours, including SCLC. IDeate-Lung02 is a global, randomized, open-label Phase 3 study of ~540 patients with relapsed SCLC. Adults with one prior line of platinum-based systemic therapy, ECOG performance status 0-1, and ≥1 measurable lesion (per RECIST 1.1) are eligible for study participation. Patients with asymptomatic untreated or previously treated brain metastases may participate. Patients are randomized 1:1 to receive I-DXd 12 mg/kg intravenously every 3 weeks or treatment of physician's choice (topotecan, amrubicin, or lurbinectedin). Dual primary endpoints are objective response rate (ORR) by blinded independent central review (BICR) and overall survival. Secondary endpoints include ORR by investigator; progression-free survival, duration of response, disease control rate, and time to response, all by BICR and investigator; patient-reported outcomes; and safety. IDeate-Lung02 will ascertain whether I-DXd treatment after only one prior line of systemic treatment improves outcomes for patients with relapsed SCLC compared with topotecan, amrubicin, or lurbinectedin.Clinical Trial Registration: NCT06203210.
    Keywords:  B7 homolog 3 (B7-H3); DS-7300; MK-2400; Small cell lung cancer (SCLC); antibody–drug conjugate (ADC); ifinatamab deruxtecan (I-DXd)
    DOI:  https://doi.org/10.1080/14796694.2025.2565995
  13. Ther Adv Med Oncol. 2025 ;17 17588359251379665
    Predicting response and survival to first-line treatment with baseline [18F]FDG-PET-CT in patients with small-cell lung cancer: an integrated diagnostic approach.
    David Ventura, Philipp Schindler, Peter Kies, Annalen Bleckmann, Michael Mohr, Georg Lenz, Michael Schäfers, Wolfgang Roll, Georg Evers.
       Background: Small-cell lung cancer (SCLC) is a highly malignant disease with a propensity for early progression and high mortality. The prognostic value of treatment response and survival has been verified for solely established imaging, clinical, and biochemical markers. There is a lack of evidence for the combination of those parameters with machine learning and integrated models, particularly in the context of molecular imaging.
    Objectives: The aim of this study was to predict early disease progression and survival using CT-based radiomic features (RF), integrating [18F]FDG-PET-CT and clinical parameters.
    Design: This retrospective pilot study included 62 patients with non-metastatic and metastatic SCLC who underwent stage-based primary treatment following baseline [18F]FDG-PET-CT. The development of a machine learning approach, incorporating clinical and molecular imaging parameters, enables the creation of a model capable of predicting treatment response and survival.
    Methods: A radiomics signature was generated based on the first-line treatment response by RECIST 1.1 criteria. The RF was integrated using binary logistic regression analysis with the PET parameter metabolic tumor volume (MTV) of the primary tumor and initial disease stage. The integrated model with the highest AUC for predicting early disease progression was evaluated for predicting progression-free survival (PFS) and overall survival (OS) in both non-metastatic and metastatic patients.
    Results: A single CT-based RF demonstrated predictive capacity (AUC = 0.81). Integration of the MTV and disease stage enhanced the predictive capacity (AUC = 0.9). A Youden index-based threshold of <0.62 was identified as a significant predictor of prolonged PFS: non-metastatic disease with a median PFS of 25 versus 4 months (HR = 0.072; p = 0.002); metastatic disease with a median PFS of 9 versus 5 months (HR 0.219; p = 0.004). The integrated model also predicted OS in metastatic disease with a median OS of 15 versus 8 months (HR 0.381; p = 0.013).
    Conclusion: A multiparametric approach based on a Radiomics model may potentially be capable of identifying patients at risk for early disease progression, PFS, and OS in non-metastatic and metastatic SCLC.
    Keywords:  FDG-PET; PFS/OS prediction; SCLC; integrated model; radiomics
    DOI:  https://doi.org/10.1177/17588359251379665
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