Quality assurance for cancer patient safety: Clinical assessment for precise angles in linac during radiation therapy.

PURPOSE: Quality assurance for stereotactic body radiation treatment requires that isocentric verification be ensured during gantry rotation at various angles. This study examined statistical parameters on Winston-Lutz tests to distinguish the deviation of angles from isocenter during gantry rotation using machine learning. METHOD: The Varian TrueBeam linac was aligned with the marked lines on the Ruby phantom. Eight images were captured while the gantry was rotating at a 45° shift. The statistical features were derived from IsoCheck EPID software. The decision tree model was applied to these Winston-Lutz tests to cluster data into two groups: precise and error angles. RESULTS: At 90° and 270° angles, the gantry exhibits isocentric stability compared to other angles. In these angles, the most statistical features were inside the range. Most variations were observed at 0° and 180° angles. In most tests, the angles 45°, 135°, 225°, and 315° showed reasonable performance and with less variation. CONCLUSION: The comprehensive statistical analyses for gantry rotation of angles assists expert radiotherapists in determining the contribution of each feature that highly affects gantry movement at specific angles. Misalignment between radiation isocenter and imaging isocenter, tuning of the beam at each angle, or a slight change in the position of the Ruby phantom can further improve the inaccuracy that causes the most variations. Better precision can effectively increase patient safety and quality during cancer treatment.

Nuclear medicine imaging modalities to detect incidentalomas and their impact on patient management: a systematic review.

PURPOSE: This systematic review aims to investigate the role of nuclear imaging techniques in detecting incidentalomas and their impact on patient management. METHODS: Following PRISMA guidelines, a comprehensive literature search was conducted from February to May 2022. Studies in English involving patients undergoing nuclear medicine studies with incidental tumor findings were included. Data on imaging modalities, incidentaloma characteristics, management changes, and follow-up were extracted and analyzed. RESULTS: Ninety-two studies involving 64.884 patients were included. Incidentalomas were detected in 611 cases (0.9%), with thyroid being the most common site. PET/CT with FDG and choline tracers showed the highest incidentaloma detection rates. Detection of incidentalomas led to a change in therapeutic strategy in 59% of cases. Various radiotracers demonstrated high sensitivity for incidentaloma detection, particularly in neuroendocrine tumors and prostate cancer. CONCLUSION: Nuclear imaging techniques play a crucial role in detecting incidentalomas, leading to significant changes in patient management. The high sensitivity of these modalities highlights their potential in routine oncology follow-up protocols. Future directions may include enhancing spatial resolution and promoting theranostic approaches for improved patient care.

Clinical impact of drug-drug interactions on abemaciclib in the real-world experience of AB-ITALY study.

Abemaciclib demonstrated clinical benefit in women affected by HR+/HER2- advanced breast cancer (aBC). Drug-drug interactions (DDIs) can lead to reduced treatment efficacy or increased toxicity. This retro-prospective study aimed to evaluate outcomes, DDIs' impact, and toxicities of abemaciclib combined with endocrine therapy in a real-world setting. Patients from 12 referral Italian hospitals with HR+/HER2- aBC who received abemaciclib were included. Clinical data about comorbidities, concurrent medications, outcomes, and adverse events (AE) were collected. Drug-PIN® (Personalized Interactions Network) is a tool recognizing the role of multiple interactions between active and/or pro-drug forms combined with biochemical and demographic patient data. The software was used to define the Drug-PIN score and Drug-PIN tier (green, yellow, dark yellow, and red) for each patient. Univariate and multivariate analyses were performed to identify predictors of patients' PFS or toxicity. One hundred seventy-three patients were included. 13% of patients had >75years. The overall response rate (ORR) was 63%. The general population's median PFS (mPFS) was 22 months (mo), while mOS were not reached. Patients treated with abemaciclib in combination with AI and fulvestrant had a mPFS of 36 and 19 mo, respectively. The most common toxicities were diarrhea, asthenia, and neutropenia detected in 63%,49%, and 49% of patients. The number of concomitant medications and comorbidities were not associated with survival outcomes (22 vs 17 mo, p = 0.068, p = 0.99). Drug-PIN tier from dark yellow to red and Drug-PIN score >12 were associated with shorter PFS compared to no/low-risk DDIs and score <12 (15 vs 23, p = 0.005, p = 0.0017). Drug interaction was confirmed as an independent biomarker in a multivariate model (p = 0.02). No difference in any grade AE, severe toxicities, and diarrhea were detected among different age subgroups. No association was found between Drug-PIN score or Drug-PIN tier and overall toxicity (p = 0.44), severe AEs (p = 0.11), or drug reduction (p = 0.27). The efficacy and safety of abemaciclib plus ET were confirmed in a real-world setting, even in the elderly population and patients with comorbidities. Evaluation of DDIs with Drug-PIN appears to be an independent predictor of PFS.

Personalizing Prophylactic Radiotherapy for Hip Heterotopic Ossification: An AMSTAR-2 Compliant Review of Meta-analyses.

BACKGROUND/AIM: Heterotopic ossification (HO) is a common complication following total hip arthroplasty. Various prophylactic treatments have been proposed, including radiotherapy (RT). This review summarizes the evidence from meta-analyses on the efficacy of RT in preventing hip HO. MATERIALS AND METHODS: A literature search was conducted on PubMed. The quality of the meta-analyses was assessed using the AMSTAR-2 tool. RESULTS: Seven meta-analyses were included. One meta-analysis reported a significant reduction in HO occurrence after RT compared to the control group. Comparing RT and non-steroidal anti-inflammatory drugs, one and two meta-analyses showed significantly greater efficacy of RT in preventing severe HO and better outcomes in patients receiving drugs, respectively. Regarding RT settings, the postoperative and preoperative RT were each supported by one meta-analysis. Furthermore, two meta-analyses showed an advantage of multi-fractionated RT over single fraction RT. The overall confidence rate of the meta-analyses was moderate, low, and critically low in one, three, and three meta-analyses, respectively. CONCLUSION: RT is a confirmed prophylactic intervention for HO. However, the precise optimization of timing, dosage, and fractionation requires elucidation. Future research should focus on the development of predictive models through large-scale data collection and advanced analytics to refine individualized treatment strategies and assess RT comparative effectiveness with drugs.

Assessing the impact of mixed reality-assisted informed consent: A study protocol.

Background: Informed consent is a crucial aspect of modern medicine, but it can be challenging due to the complexity of the information involved. Mixed reality (MR) has emerged as a promising technology to improve communication. However, there is a lack of comprehensive research on the impact of MR on medical informed consent. The proposed research protocol provides a solid foundation for conducting future investigations and developing MR-based protocols that can enhance patients' understanding and engagement in the decision-making process. Methods: This study will employ a randomized controlled trial design. Two arms will be defined: MR-assisted informed consent (MRaIC) as the experimental arm and conventional informed consent (CIC) as the control arm consent, with 52 patients in each group. The protocol includes the use of questionnaires to analyze the anxiety levels and the awareness of the procedure that the patient is going to perform to study the impact of MRaIC versus CIC before medical procedures. Results: The study will evaluate the impact of MR on patients' information comprehension, engagement during the process of obtaining informed consent, emotional reactions, and consent decisions. Ethical concerns will be addressed. Conclusion: This study protocol provides a comprehensive approach to investigate the impact of MR on medical informed consent. The findings may contribute to a better understanding of the effects of MR on information comprehension, engagement during the process of obtaining informed consent, psychological experience, consent decisions, and ethical considerations. The integration of MR technology has the potential to enhance surgical communication practices and improve the informed consent process.

CD137+ and regulatory T cells as independent prognostic factors of survival in advanced non-oncogene addicted NSCLC patients treated with immunotherapy as first-line.

BACKGROUND: Immune checkpoint inhibitors (ICIs), administered alone or combined with chemotherapy, are the standard of care in advanced non-oncogene addicted Non-Small Cell Lung Cancer (NSCLC). Despite these treatments' success, most long-term survival benefit is restricted to approximately 20% of patients, highlighting the need to identify novel biomarkers to optimize treatment strategies. In several solid tumors, immune soluble factors, the activatory CD137+ Tcells, and the immunosuppressive cell subsets Tregs and MDSCs (PMN(Lox1+)-MDSC and M-MDSCs) correlated with responses to ICIs and clinical outcomes thus becoming appealing predictive and prognostic factors. This study investigated the role of distinct CD137+ Tcell subsets, Tregs, MDSCs, and immune-soluble factors in NSCLC patients as possible biomarkers. METHODS: The levels of T cells, MDSCs and soluble factors were evaluated in 89 metastatic NSCLC patients who underwent ICIs as first- or second-line treatment. T cell analysis was performed by cytoflurimetry evaluating Tregs and different CD137+ Tcell subsets also combined with CD3+, CD8+, PD1+, and Ki67+ markers. Circulating cytokines and immune checkpoints were also evaluated by Luminex analysis. All these parameters were correlated with several clinical factors (age, sex, smoking status, PS and TPS), response to therapy, PFS , and OS . The analyses were conducted in the overall population and in patients treated with ICIs as first-line (naïve patients). RESULTS: In both groups of patients, high levels of circulating CD137+ and CD137+PD1+ T cells (total, CD4 and CD8) and the soluble factor LAG3 positively correlated with response to therapy. In naïve patients, PMN(Lox1+)-MDSCs negatively correlated with clinical response, and a high percentage of Tregs was associated with favorable survival. Moreover, the balance between Treg/CD137+ Tcells or PMN(Lox1+)-MDSC/CD137+ Tcells was higher in non-responding patients and was associated with poor survival. CD137+ Tcells and Tregs resulted as two positive independent prognostic factors. CONCLUSION: High levels of CD137+, CD137+PD1+ Tcells and sLAG3 could predict the response to ICIs in NSCLC patients independently by previous therapy. Combining the evaluation of CD137+ Tcells and Tregs also as Treg/CD137+ T cells ratio it is possible to identify naive patients with longer survival.

Augmented reality in brachytherapy: A narrative review.

Brachytherapy (BRT) plays a pivotal role in the treatment of tumors, offering precise radiation therapy directly to the affected area. However, this technique demands extensive training and skills development, posing challenges for widespread adoption and ensuring patient safety. This narrative review explored the utilization of augmented reality (AR) in BRT, seeking to summarize existing evidence, discuss key findings, limitations, and quality of research as well as outline future research directions. The review revealed promising findings regarding the integration of AR in BRT. Studies have suggested the feasibility and potential benefits of AR in education, training, intra-operative guidance, and treatment planning. However, the evidence remains limited and heterogeneous, with most studies in preliminary phases. Standardization, prospective clinical trials, patient-centered outcomes assessment, and cost-effectiveness analysis emerge as critical areas for future research. Augmented reality holds transformative potential for BRT by enhancing precision, safety, and training efficiency. To fully implement these benefits, the field requires standardized protocols, rigorous clinical trials, and in-depth patient-centered investigations. Policy-makers and healthcare providers should closely monitor developments in AR and consider its implementation in clinical practice, contingent and robust evidence, and cost-effectiveness analysis. The pro-active pursuit of evidence-based practices will contribute to optimizing patient care in BRT.

Machine Learning Applied to Pre-Operative Computed-Tomography-Based Radiomic Features Can Accurately Differentiate Uterine Leiomyoma from Leiomyosarcoma: A Pilot Study.

BACKGROUND: The accurate discrimination of uterine leiomyosarcomas and leiomyomas in a pre-operative setting remains a current challenge. To date, the diagnosis is made by a pathologist on the excised tumor. The aim of this study was to develop a machine learning algorithm using radiomic data extracted from contrast-enhanced computed tomography (CECT) images that could accurately distinguish leiomyosarcomas from leiomyomas. METHODS: Pre-operative CECT images from patients submitted to surgery with a histological diagnosis of leiomyoma or leiomyosarcoma were used for the region of interest identification and radiomic feature extraction. Feature extraction was conducted using the PyRadiomics library, and three feature selection methods combined with the general linear model (GLM), random forest (RF), and support vector machine (SVM) classifiers were built, trained, and tested for the binary classification task (malignant vs. benign). In parallel, radiologists assessed the diagnosis with or without clinical data. RESULTS: A total of 30 patients with leiomyosarcoma (mean age 59 years) and 35 patients with leiomyoma (mean age 48 years) were included in the study, comprising 30 and 51 lesions, respectively. Out of nine machine learning models, the three feature selection methods combined with the GLM and RF classifiers showed good performances, with predicted area under the curve (AUC), sensitivity, and specificity ranging from 0.78 to 0.97, from 0.78 to 1.00, and from 0.67 to 0.93, respectively, when compared to the results obtained from experienced radiologists when blinded to the clinical profile (AUC = 0.73 95%CI = 0.62-0.84), as well as when the clinical data were consulted (AUC = 0.75 95%CI = 0.65-0.85). CONCLUSIONS: CECT images integrated with radiomics have great potential in differentiating uterine leiomyomas from leiomyosarcomas. Such a tool can be used to mitigate the risks of eventual surgical spread in the case of leiomyosarcoma and allow for safer fertility-sparing treatment in patients with benign uterine lesions.

Sarcopenic Obesity in Cervical Carcinoma: A Strong and Independent Prognostic Factor beyond the Conventional Predictors (ESTHER Study-AFRAID Project).

Locally advanced cervical cancer represents a significant treatment challenge. Body composition parameters such as body mass index, sarcopenia, and sarcopenic obesity, defined by sarcopenia and BMI ≥ 30 kg/m2, have been identified as potential prognostic factors, yet their overall impact remains underexplored. This study assessed the relationship between these anthropometric parameters alongside clinical prognostic factors on the prognosis of 173 cervical cancer patients. Survival outcomes in terms of local control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) were analyzed using Kaplan regression methods-Meier and Cox. Older age, lower hemoglobin levels, higher FIGO (International Federation of Gynecology and Obstetrics) stages, and lower total radiation doses were significantly associated with worse outcomes. Univariate analysis showed a significant correlation between BMI and the outcomes examined, revealing that normal-weight patients show higher survival rates, which was not confirmed by the multivariate analysis. Sarcopenia was not correlated with any of the outcomes considered, while sarcopenic obesity was identified as an independent negative predictor of DFS (HR: 5.289, 95% CI: 1.298-21.546, p = 0.020) and OS (HR: 2.645, 95% CI: 1.275-5.488, p = 0.009). This study highlights the potential of sarcopenic obesity as an independent predictor of clinical outcomes. These results support their inclusion in prognostic assessments and treatment planning for patients with advanced cervical cancer.

A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms.

PURPOSE: To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose4 iterative reconstruction for brain computed tomography (CT) phantom images. METHODS: Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDIvol): 7/14/29/49/56/67 mGy). Images were reconstructed using FBP, levels 2/5 of iDose4, and PI algorithm (Sharper/Sharp/Standard/Smooth/Smoother). Image quality was assessed by evaluating CT numbers, image histograms, noise, image non-uniformity (NU), noise power spectrum, target transfer function, and detectability index. RESULTS: The five PI levels did not significantly affect the mean CT number. For a given CTDIvol using Sharper-to-Smoother levels, the spatial resolution for all the investigated materials and the detectability index increased while the noise magnitude decreased, slightly affecting noise texture. For a fixed PI level increasing the CTDIvol the detectability index increased, the noise magnitude decreased. From 29 mGy, NU values converged within 1 Hounsfield Unit from each other without a substantial improvement at higher CTDIvol values. CONCLUSIONS: The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDIvol.

Targeting of H19/cell adhesion molecules circuitry by GSK-J4 epidrug inhibits metastatic progression in prostate cancer.

BACKGROUND: About 30% of Prostate cancer (PCa) patients progress to metastatic PCa that remains largely incurable. This evidence underlines the need for the development of innovative therapies. In this direction, the potential research focus might be on long non-coding RNAs (lncRNAs) like H19, which serve critical biological functions and show significant dysregulation in cancer. Previously, we showed a transcriptional down-regulation of H19 under combined pro-tumoral estrogen and hypoxia treatment in PCa cells that, in turn, induced both E-cadherin and ß4 integrin expression. H19, indeed, acts as transcriptional repressor of cell adhesion molecules affecting the PCa metastatic properties. Here, we investigated the role of H19/cell adhesion molecules circuitry on in vivo PCa experimental tumor growth and metastatic dissemination models. METHODS: H19 was silenced in luciferase-positive PC-3 and 22Rv1 cells and in vitro effect was evaluated by gene expression, proliferation and invasion assays before and after treatment with the histone lysine demethylase inhibitor, GSK-J4. In vivo tumor growth and metastasis dissemination, in the presence or absence of GSK-J4, were analyzed in two models of human tumor in immunodeficient mice by in vivo bioluminescent imaging and immunohistochemistry (IHC) on explanted tissues. Organotypic Slice Cultures (OSCs) from fresh PCa-explant were used as ex vivo model to test GSK-J4 effects. RESULTS: H19 silencing in both PC-3 and 22Rv1 cells increased: i) E-cadherin and ß4 integrin expression as well as proliferation and invasion, ii) in vivo tumor growth, and iii) metastasis formation at bone, lung, and liver. Of note, treatment with GSK-J4 reduced lesions. In parallel, GSK-J4 efficiently induced cell death in PCa-derived OSCs. CONCLUSIONS: Our findings underscore the potential of the H19/cell adhesion molecules circuitry as a targeted approach in PCa treatment. Modulating this interaction has proven effective in inhibiting tumor growth and metastasis, presenting a logical foundation for targeted therapy.

Improving mixed-reality neuronavigation with blue-green light: a comparative multimodal laboratory study.

OBJECTIVE: This study aimed to rigorously assess the accuracy of mixed-reality neuronavigation (MRN) in comparison with magnetic neuronavigation (MN) through a comprehensive phantom-based experiment. It introduces a novel dimension by examining the influence of blue-green light (BGL) on MRN accuracy, a previously unexplored avenue in this domain. METHODS: Twenty-nine phantoms, each meticulously marked with 5-6 fiducials, underwent CT scans as part of the navigation protocol. A 3D model was then superimposed onto a 3D-printed plaster skull using a semiautomatic registration process. The study meticulously evaluated the accuracy of both navigation techniques by pinpointing specific markers on the plaster surface. Precise measurements were then taken using digital calipers, with navigation conducted under three distinct lighting conditions: indirect white light (referred to as no light [NL]), direct white light (WL), and BGL. The research enlisted two operators with distinct levels of experience, one senior and one junior, to ensure a comprehensive analysis. The study was structured into two distinct experiments (experiment 1 [MN] and experiment 2 [MRN]) conducted by the two operators. Data analysis focused on calculating average and median values within subgroups, considering variables such as the type of lighting, precision, and recording time. RESULTS: In experiment 1, no statistically significant differences emerged between the two operators. However, in experiment 2, notable disparities became apparent, with the senior operator recording longer times but achieving higher precision. Most significantly, BGL consistently demonstrated a capacity to enhance accuracy in MRN across both experiments. CONCLUSIONS: This study demonstrated the substantial positive influence of BGL on MRN accuracy, providing profound implications for the design and implementation of mixed-reality systems. It also emphasized that integrating BGL into mixed-reality environments could profoundly improve user experience and performance. Further research is essential to validate these findings in real-world settings and explore the broader potential of BGL in a variety of mixed-reality applications.

Real-World Outcomes of Trastuzumab Deruxtecan in Patients With HER2+ Metastatic Breast Cancer: The DE-REAL Study.

BACKGROUND: Trastuzumab deruxtecan (T-DXd) demonstrated unprecedented efficacy in patients with pretreated HER2+ metastatic breast cancer (mBC). However, few data are available about its efficacy in routine clinical practice. In this multicenter retrospective study, we examined effectiveness and safety of T-DXd in a real-world population. METHODS: Clinico-pathological information about patients with HER2+ mBC who received T-DXd were collected from 12 Italian hospitals. HER2 status was determined locally. Patients who received at least one administration of T-DXd, as any therapy line for advanced disease were included in the analysis. The primary endpoint was real-word PFS (rwPFS). RESULTS: One hundred and forty-three patients were included. Median age was 66 (range: 37-90), and 4 men were included. Hormone receptor (HR) status was positive in 108 (75%) patients and negative in 35(25%). T-DXd was administered as first, second, third, or subsequent lines in 4 (3%), 16 (11%), 42 (29%), and 81 (57%) patients, respectively. Among 123 patients with measurable disease, the ORR was 68%, and the DCR was 93% (9 CRs, 74 PRs, and 30 SD). Nine (7%) patients had a primary resistance to T-DXd. With a median follow-up of 12 months, the median rwPFS was 16 months. RwPFS was 84%, 59%, and 39% at 6, 12, and 18 months, respectively. A favorable trend in rwPFS was reported in patients receiving T-DXd as I/II line versus further lines (17 vs. 15 months; P = .098). Any-grade toxicity was registered in 84 patients (59%). Most common adverse events (AEs) reported were nausea (33%), neutropenia (21%), and asthenia (21%). Liver toxicity and diarrhea were uncommon (5% and 1%). Severe toxicities was registered in 18% of patients, and the most frequent were neutropenia, nausea/vomiting, and ILD observed in 15, 2, and 3 patients. AEs led to dose reduction in 37 patients (26%). Dose reduction and AEs do not affect patients' response and survival outcomes. CONCLUSIONS: Efficacy and safety of T-DXd were confirmed in an unselected real-world population of HER2+ mBC. These results are consistent with the results of known findings, and no new safety concerns were reported.

Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning.

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

A single centre intercomparison between commercial treatment planning systems for 90Y radioembolization using virtual and experimental phantoms.

INTRODUCTION: Dedicated Treatment Planning Systems (TPSs) were developed to personalize 90Y-transarterial radioembolization. This study evaluated the agreement among four commercial TPSs assessing volumes of interest (VOIs) volumes and dose metrics. METHODS: A homogeneous (EH) and an anthropomorphic phantom with hot and cold inserts (EA) filled with 99mTc-pertechnetate were acquired with a SPECT/CT scanner. Their virtual versions (VH and VA, respectively) and a phantom with activity inside a single voxel (VK) were generated by an in-house MATLAB script. Images and delineated VOIs were imported into the TPSs to compute voxel-based absorbed dose distributions with various dose deposition approaches: local deposition method (LDM) and dose kernel convolution (DKC) with/without local density correction (LDC). VOI volumes and mean absorbed doses were assessed against their median value across TPSs. Dose-volume histograms (DVHs) and VK-derived dose profiles were evaluated. RESULTS: Small (<2.1 %) and large (up to 42.4 %) relative volume differences were observed on large (>500 ml) and small VOIs, respectively. Mean absorbed doses relative differences were < 3 % except for small VOIs with steep dose gradients (up to 89.1 % in the VA Cold Sphere VOI). Within the same TPS, LDC negligibly affected the mean absorbed dose, while DKC and LDM showed differences up to 63 %. DHVs were mostly overlapped in experimental phantoms, with some differences in the virtual versions. Dose profiles agreed within 1 %. CONCLUSION: TPSs showed an overall good agreement except for small VOI volumes and mean absorbed doses of VOIs with steep dose gradients. These discrepancies should be considered in the dosimetry uncertainty assessment, thus requiring an appropriate harmonization.

Decoding the Complexity of Systemic Inflammation Predictors in Locally Advanced Cervical Cancer, with Hemoglobin as the Hidden Key (the ESTHER Study).

Locally advanced cervical cancer (LACC) is treated with concurrent chemoradiation (CRT). Predictive models could improve the outcome through treatment personalization. Several factors influence prognosis in LACC, but the role of systemic inflammation indices (IIs) is unclear. This study aims to assess the correlation between IIs and prognosis in a large patient cohort considering several clinical data. We retrospectively analyzed pretreatment IIs (NLR, PLR, MLR, SII, LLR, COP-NLR, APRI, ALRI, SIRI, and ANRI) in 173 LACC patients. Patient, tumor, and treatment characteristics were also considered. Univariate and multivariate Cox's regressions were conducted to assess associations between IIs and clinical factors with local control (LC), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS). Univariate analysis showed significant correlations between age, HB levels, tumor stage, FIGO stage, and CRT dose with survival outcomes. Specific pretreatment IIs (NLR, PLR, APRI, ANRI, and COP-NLR) demonstrated associations only with LC. The multivariate analysis confirmed Hb levels, CRT dose, and age as significant predictors of OS, while no II was correlated with any clinical outcome. The study findings contradict some prior research on IIs in LACC, emphasizing the need for comprehensive assessments of potential confounding variables.

A Radiomic-Based Machine Learning Model Predicts Endometrial Cancer Recurrence Using Preoperative CT Radiomic Features: A Pilot Study.

BACKGROUND: Current prognostic models lack the use of pre-operative CT images to predict recurrence in endometrial cancer (EC) patients. Our study aimed to investigate the potential of radiomic features extracted from pre-surgical CT scans to accurately predict disease-free survival (DFS) among EC patients. METHODS: Contrast-Enhanced CT (CE-CT) scans from 81 EC cases were used to extract the radiomic features from semi-automatically contoured volumes of interest. We employed a 10-fold cross-validation approach with a 6:4 training to test set and utilized data augmentation and balancing techniques. Univariate analysis was applied for feature reduction leading to the development of three distinct machine learning (ML) models for the prediction of DFS: LASSO-Cox, CoxBoost and Random Forest (RFsrc). RESULTS: In the training set, the ML models demonstrated AUCs ranging from 0.92 to 0.93, sensitivities from 0.96 to 1.00 and specificities from 0.77 to 0.89. In the test set, AUCs ranged from 0.86 to 0.90, sensitivities from 0.89 to 1.00 and specificities from 0.73 to 0.90. Patients classified as having a high recurrence risk prediction by ML models exhibited significantly worse DSF (p-value < 0.001) across all models. CONCLUSIONS: Our findings demonstrate the potential of radiomics in predicting EC recurrence. While further validation studies are needed, our results underscore the promising role of radiomics in forecasting EC outcomes.

Classical Prognostic Factors Predict Prognosis Better than Inflammatory Indices in Locally Advanced Cervical Cancer: Results of a Comprehensive Observational Study including Tumor-, Patient-, and Treatment-Related Data (ESTHER Study).

Systemic inflammation indices were found to be correlated with therapeutic outcome in several cancers. This study retrospectively analyzes the predictive role of a broad range of systemic inflammatory markers in patients with locally advanced cervical cancer (LACC) including patient-, tumor-, and treatment-related potential prognostic factors. All patients underwent definitive chemoradiation and pretreatment values of several inflammatory indices (neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio, monocyte/lymphocyte ratio, systemic immune inflammation index (SII), leukocyte/lymphocyte ratio, combination of platelet count and NLR, aspartate aminotransferase/platelet ratio index, aspartate aminotransferase/lymphocyte ratio index, systemic inflammatory response index, and aspartate transaminase/neutrophil ratio index) were calculated. Their correlation with local control (LC), distant metastasis-free (DMFS), disease-free (DFS), and overall survival (OS) was analyzed. One hundred and seventy-three patients were included. At multivariable analysis significant correlations were recorded among clinical outcomes and older age, advanced FIGO stage, lower hemoglobin levels, larger tumor size, and higher body mass index values. The multivariate analysis showed only the significant correlation between higher SII values and lower DMFS rates (p < 0.01). Our analysis showed no significant correlation between indices and DSF or OS. Further studies are needed to clarify the role of inflammation indices as candidates for inclusion in predictive models in this clinical setting.