Controversies in endobronchial ultrasound.

Endobronchial ultrasound (EBUS) is a minimally invasive highly accurate and safe endoscopic technique for the evaluation of mediastinal lymphadenopathy and mediastinal masses including centrally located lung tumors. The combination of transbronchial and transoesophageal tissue sampling has improved lung cancer staging, reducing the need for more invasive and surgical diagnostic procedures. Despite the high level of evidence regarding EBUS use in the aforementioned situations, there are still challenges and controversial issues such as follows: Should informed consent for EBUS and flexible bronchoscopy be different? Is EBUS able to replace standard bronchoscopy in patients with suspected lung cancer? Which is the best position, screen orientation, route of intubation, and sedation/anesthesia to perform EBUS? Is it advisable to use a balloon in all procedures? How should the operator acquire skills and how should competence be ensured? This Pro-Con article aims to address these open questions.

Diagnostic and therapeutic delays in lung cancer during the COVID-19 pandemic: a single center experience at a German Cancer center.

BACKGROUND: The COVID-19 pandemic has had negative drawbacks on the healthcare system worldwide and on individuals other than those directly affected by the virus. Delays in cancer therapy and diagnosis have been reported in the literature. We hypothesized similar effects on patients with lung cancer at our center. METHODS: We retrospectively analyzed data of patients referred to our center with newly diagnosed lung cancer from 2018 to 2022. We considered distribution of UICC Stages and time from case presentation in our multidisciplinary tumor board or from therapeutic indication from treating physician to therapy initiation (surgery, systemic therapies and radiation) to define delays in diagnosis and treatment. RESULTS: 1020 patients with newly diagnosed lung cancer were referred to our center from 2018 to 2022, with a median of 206 cases yearly (range: 200-208). Cases with Stage IV in 2020-2022 were significantly higher than in 2018-2019 (57% vs. 46%, p = 0,001). 228 operative resections took place between 2018 and 2022, 100 from January 2018 to February 2020 and 128 from March 2020 to December 2022. Median time from presentation in our tumor board to resection was also significantly longer after the beginning of the pandemic than before (22 days vs. 15,5 days, p = 0,013). No significant delays were observed for administration of systemic treatment and initiation of radiation. CONCLUSIONS: During the pandemic higher disease stages were reported for patients with lung cancer, yet there were no clinically relevant delays in treatment. In the context of the post-covid era new diagnostic strategies are necessary to facilitate early diagnosis of lung cancer. Despite the pandemic, for patients with suspicious symptoms prompt access to healthcare facilities is essential for early diagnosis.

Effects of Medicaid managed care on early detection of cancer: Evidence from mandatory Medicaid managed care program in Pennsylvania.

OBJECTIVE: To examine changes in late- versus early-stage diagnosis of cancer associated with the introduction of mandatory Medicaid managed care (MMC) in Pennsylvania. DATA SOURCES AND STUDY SETTING: We analyzed data from the Pennsylvania cancer registry (2010-2018) for adult Medicaid beneficiaries aged 21-64 newly diagnosed with a solid tumor. To ascertain Medicaid and managed care status around diagnosis, we linked the cancer registry to statewide hospital-based facility records collected by an independent state agency (Pennsylvania Health Care Cost Containment Council). STUDY DESIGN: We leveraged a natural experiment arising from county-level variation in mandatory MMC in Pennsylvania. Using a stacked difference-in-differences design, we compared changes in the probability of late-stage cancer diagnosis among those residing in counties that newly transitioned to mandatory managed care to contemporaneous changes among those in counties with mature MMC programs. DATA COLLECTION/EXTRACTION METHODS: N/A. PRINCIPAL FINDINGS: Mandatory MMC was associated with a reduced probability of late-stage cancer diagnosis (-3.9 percentage points; 95% CI: -7.2, -0.5; p = 0.02), particularly for screening-amenable cancers (-5.5 percentage points; 95% CI: -10.4, -0.6; p = 0.03). We found no significant changes in late-stage diagnosis among non-screening amenable cancers. CONCLUSIONS: In Pennsylvania, the implementation of mandatory MMC for adult Medicaid beneficiaries was associated with earlier stage of diagnosis among newly diagnosed cancer patients with Medicaid, especially those diagnosed with screening-amenable cancers. Considering that over half of the sample was diagnosed with late-stage cancer even after the transition to mandatory MMC, Medicaid programs and managed care organizations should continue to carefully monitor receipt of cancer screening and design strategies to reduce barriers to guideline-concordant screening or diagnostic procedures.

Application value of artificial intelligence algorithm-based magnetic resonance multi-sequence imaging in staging diagnosis of cervical cancer.

The aim of this research is to explore the application value of Deep residual network model (DRN) for deep learning-based multi-sequence magnetic resonance imaging (MRI) in the staging diagnosis of cervical cancer (CC). This research included 90 patients diagnosed with CC between August 2019 and May 2021 at the hospital. After undergoing MRI examination, the clinical staging and surgical pathological staging of patients were conducted. The research then evaluated the results of clinical staging and MRI staging to assess their diagnostic accuracy and correlation. In the staging diagnosis of CC, the feature enhancement layer was added to the DRN model, and the MRI imaging features of CC were used to enhance the image information. The precision, specificity, and sensitivity of the constructed model were analyzed, and then the accuracy of clinical diagnosis staging and MRI staging were compared. As the model constructed DRN in this research was compared with convolutional neural network (CNN) and the classic deep neural network visual geometry group (VGG), the precision was 67.7, 84.9, and 93.6%, respectively. The sensitivity was 70.4, 82.5, and 91.2%, while the specificity was 68.5, 83.8, and 92.2%, respectively. The precision, sensitivity, and specificity of the model were remarkably higher than those of CNN and VGG models (P < 0.05). As the clinical staging and MRI staging of CC were compared, the diagnostic accuracy of MRI was 100%, while that of clinical diagnosis was 83.7%, showing a significant difference between them (P < 0.05). Multi-sequence MRI under intelligent algorithm had a high diagnostic rate for CC staging, deserving a good clinical application value.

Establishing and Externally Validating a Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score-Based Nomogram for Predicting Early Recurrence in BCLC Stage 0/A Hepatocellular Carcinoma Patients After Radical Liver Resection: A Multi-Center Study.

Purpose: Early recurrence (ER) is associated with poor prognosis in hepatocellular carcinoma (HCC). In this study, we developed and externally validated a nomogram based on the hemoglobin, albumin, lymphocytes, and platelets (HALP) score to predict ER for patients with BCLC stage 0/A HCC who underwent radical liver resection. Patients and Methods: A total of 808 BCLC stage 0/A HCC patients from six hospitals were included in this study, and they were assigned to a training cohort (n = 500) and an external validation cohort (n = 308). We used univariate and multivariate Cox regression analysis to identify the independent risk factors for disease-free survival (DFS). We also established and externally validated a nomogram based on these risk predictors. The nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), the concordance index (C-index), the calibration curve, decision curve analysis (DCA), and Kaplan‒Meier analysis. Results: Multivariate COX regression showed that HBV DNA ≥10,000 IU/mL (P < 0.001), HALP score ≤38.20 (P < 0.001), tumor size (P = 0.003), clinically significant portal hypertension (P = 0.001), Edmondson-Steiner grade (III-IV) (P = 0.007), satellite nodules (P < 0.001), and MVI (P = 0.001) were independent risk factors for post-operative tumor recurrence. The AUC of our nomogram for predicting the 2-year and 5-year DFS was 0.756 and 0.750, respectively, in the training cohort and 0.764 and 0.705, respectively, in the external validation cohort. We divided the patients into low-, intermediate- and high-risk groups according to the risk score calculated by the nomogram. There were statistically significant differences in the DFS and overall survival (OS) among the three groups of patients (P < 0.001). Conclusion: We developed and externally validated a new nomogram, which is accurate and can predict ER in BCLC stage 0/A HCC patients after curative liver resection.

Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience.

BACKGROUND: Current processes collecting cancer stage data in population-based cancer registries (PBCRs) lack standardisation, resulting in difficulty utilising diverse data sources and incomplete, low-quality data. Implementing a cancer staging tiered framework aims to improve stage collection and facilitate inter-PBCR benchmarking. OBJECTIVE: Demonstrate the application of a cancer staging tiered framework in the Western Australian Cancer Staging Project to establish a standardised method for collecting cancer stage at diagnosis data in PBCRs. METHODS: The tiered framework, developed in collaboration with a Project Advisory Group and applied to breast, colorectal, and melanoma cancers, provides business rules - procedures for stage collection. Tier 1 represents the highest staging level, involving complete American Joint Committee on Cancer (AJCC) tumour-node-metastasis (TNM) data collection and other critical staging information. Tier 2 (registry-derived stage) relies on supplementary data, including hospital admission data, to make assumptions based on data availability. Tier 3 (pathology stage) solely uses pathology reports. FINDINGS: The tiered framework promotes flexible utilisation of staging data, recognising various levels of data completeness. Tier 1 is suitable for all purposes, including clinical and epidemiological applications. Tiers 2 and 3 are recommended for epidemiological analysis alone. Lower tiers provide valuable insights into disease patterns, risk factors, and overall disease burden for public health planning and policy decisions. Capture of staging at each tier depends on data availability, with potential shifts to higher tiers as new data sources are acquired. CONCLUSIONS: The tiered framework offers a dynamic approach for PBCRs to record stage at diagnosis, promoting consistency in population-level staging data and enabling practical use for benchmarking across jurisdictions, public health planning, policy development, epidemiological analyses, and assessing cancer outcomes. Evolution with staging classifications and data variable changes will futureproof the tiered framework. Its adaptability fosters continuous refinement of data collection processes and encourages improvements in data quality.

Changes in New Patient Consultations During the COVID-19 Pandemic at a Canadian Comprehensive Cancer Center.

BACKGROUND: The impact of COVID-19 pandemic-related disruptions on cancer services is emerging. We evaluated the impact of the first 2 years of the pandemic on new patient consultations for all cancers at a comprehensive cancer center within a publicly funded health care system and assessed whether there was evidence of stage shift. METHODS: We performed a retrospective study using the Princess Margaret Cancer Registry. New consultations with medical, radiation, or surgical oncology were categorized by year and quarter. Logistic regression was used to assess the effect of period before and during the COVID-19 pandemic on cancer stage at consultation, adjusting for age, sex, and diagnosis location (our hospital network vs elsewhere). RESULTS: In all, 53,759 new patient consultations occurred from January 1, 2018, to June 30, 2022. After the pandemic was declared, there was a decrease in all types of consultations by 43.3% in the second quarter of 2020, and referral volumes did not recover during the first year. There was no evidence of stage shift for all cancer types during the later quarters of the pandemic for the overall population. CONCLUSIONS: New patient consultations decreased across cancer stages, referral type, and most disease sites at our tertiary cancer center. We did not observe evidence of stage shift in this population. Further research is needed to determine whether this reflects the resilience of our health care system in maintaining cancer services or a delay in the presentation of advanced cancer cases. These data are important for shaping future cancer care delivery and recovery strategies.

The association between referral by specialists in oral diagnosis on survival rates of patients with oral cancer: A retrospective cohort study.

BACKGROUND: To assess the influence of diagnosis and referral provided by specialists in oral diagnosis on disease-free survival and overall survival of patients with oral cancer. METHODS: A cohort of 282 patients with oral cancer treated at a regional cancer hospital from 1998 to 2016 was analyzed retrospectively. The referral register of the patients was analyzed and assigned to two groups: (1) those referred by oral diagnosis specialists (n = 129), or (2) those referred by nonspecialized professionals (n = 153). The cancer treatment evolution was assessed from the patients' records, and the outcome was registered concerning cancer recurrence and death. Sociodemographic and clinicopathological variables were explored as predictors of disease-free survival and overall survival. RESULTS: Group 1 exhibited lower T stages and a reduced incidence of regional and distant metastases. Surgery was performed in 75.2% of cases in Group 1, while in Group 2, the rate was 60.8%. Advanced T stages and regional metastases reduced the feasibility of surgery. Higher TNM stages and tumor recurrence were associated with decreased disease-free survival, while surgical intervention was a protective factor. Higher TNM stage had a negative impact on the overall survival. CONCLUSION: Specialized oral diagnosis did not directly impact disease-free survival and overall survival and did not influence the indication of surgery in oral cancer; however, it was associated with the diagnosis of early tumors and better prognosis.

Decision-level data fusion based on laser-induced breakdown and Raman spectroscopy: A study of bimodal spectroscopy for diagnosis of lung cancer at different stages.

Lung cancer staging is crucial for personalized treatment and improved prognosis. We propose a novel bimodal diagnostic approach that integrates LIBS and Raman technologies into a single platform, enabling comprehensive tissue elemental and molecular analysis. This strategy identifies critical staging elements and molecular marker signatures of lung tumors. LIBS detects concentration patterns of elemental lines including Mg (I), Mg (II), Ca (I), Ca (II), Fe (I), and Cu (II). Concurrently, Raman spectroscopy identifies changes in molecular content, such as phenylalanine (1033 cm-1), tyrosine (1174 cm-1), tryptophan (1207 cm-1), amide III (1267 cm-1), and proteins (1126 cm-1 and 1447 cm-1), among others. The bimodal information is fused using a decision-level Bayesian fusion model, significantly enhancing the performance of the convolutional neural network architecture in classification algorithms, with an accuracy of 99.17 %, sensitivity of 99.17 %, and specificity of 99.88 %. This study provides a powerful new tool for the accurate staging and diagnosis of lung tumors.

Assessing the use of the triglyceride-glycemic index (TyG), neutrophil-lymphocyte Ratio (NLR), and platelet-lymphocyte Ratio (PLR) in distinguishing benign and malignant tumors among patients with complaints of breast mass.

Introduction: Breast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses. Methods: A prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov-Smirnov tests, Mann-Whitney U tests, receiver's operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants. Results: This study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction. Conclusions: TyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.

Minimally Invasive with Maximal Yield: A Narrative Review of Current Practices in Mediastinal Lymph Node Staging in Non-Small Cell Lung Cancer.

Background: Lung cancer remains the leading cause of cancer deaths in the United States despite declining incidence and improved outcomes because of advancements in early detection and development of novel therapies. Accurate mediastinal lymph node staging is crucial for determining prognosis and guiding treatment decisions, particularly for non-small cell lung cancer (NSCLC). Materials and Methods: A systematic search of PubMed was conducted to identify English language articles published between January 2010 and January 2024 focusing on preoperative lymph node staging in adults with NSCLC. Case series, observational studies, randomized trials, guidelines, narrative reviews, systematic reviews, and meta-analyses were included. Results: Various imaging modalities, surgical and nonsurgical procedures for mediastinal lymph node staging were reviewed, including positron emission tomography with computed tomography, cervical mediastinoscopy, video-assisted cervical mediastinoscopy, anterior mediastinotomy, video-assisted thoracoscopy, endobronchial ultrasound-guided fine needle aspiration (EBUS-FNA), transesophageal endoscopic ultrasound-guided fine needle aspiration (EUS-FNA), and computed tomography-guided percutaneous lymph node biopsy. EBUS-FNA emerged as the preferred initial staging procedure because of its high sensitivity and low complication rate. Combining it with other procedures or confirmatory testing may be helpful in determining appropriate treatment. Conclusions: Although cervical mediastinoscopy remains a valuable confirmatory procedure in select cases, its role as a first-line staging modality is diminishing with the widespread adoption of EBUS-FNA and EUS-FNA. The combination of EBUS-FNA and EUS-FNA allows access to nearly all mediastinal lymph node stations with high diagnostic accuracy. Future research may further refine the selection criteria for invasive mediastinal staging procedures, ultimately optimizing patient outcomes in the management of NSCLC.

Contemporary Diagnosis of Very Low-risk Prostate Cancer in a Multihospital Health Care System.

The National Comprehensive Cancer Network (NCCN) very low risk (VLR) category for prostate cancer (PCa) represents clinically insignificant disease, and detection of VLR PCa contributes to overdiagnosis. Greater use of magnetic resonance imaging (MRI) and biomarkers before patient selection for prostate biopsy (PBx) reduces unnecessary biopsies and may reduce the diagnosis of clinically insignificant PCa. We tested a hypothesis that the proportion of VLR diagnoses has decreased with greater use of MRI-informed PBx using data from our 11-hospital system. From 2018 to 2023, 351/3197 (11%) men diagnosed with PCa met the NCCN VLR criteria. The proportion of VLR diagnoses did not change from 2018 to 2023 (p = 0.8) despite an increase in the use of MRI-informed PBx (from 49% to 82%; p < 0.001). Of patients who underwent combined systematic and targeted PBx and were diagnosed with VLR disease, cancer was found in systematic PBx regions in 79% of cases and in targeted PBx regions in 31% of cases. When performing both systematic and targeted PBx, prebiopsy MRI-based risk calculators could limit VLR diagnosis by 41% using a risk threshold of >5% for Gleason grade group ≥3 PCa to recommend biopsy; the reduction would be 77% if performing targeted PBx only. These findings suggest that VLR disease continues to account for a significant minority of PCa diagnoses and could be limited by targeted PBx and risk stratification calculators. PATIENT SUMMARY: We looked at recent trends for the diagnosis of very low-risk (VLR) prostate cancer. We found that VLR cancer still seems to be frequently diagnosed despite the use of MRI (magnetic resonance imaging) scans before biopsy. The use of risk calculators to identify men who could avoid biopsy and/or biopsy only for lesions that are visible on MRI could reduce the overdiagnosis of VLR prostate cancer.

DMGM: deformable-mechanism based cervical cancer staging via MRI multi-sequence.

Objective.This study aims to leverage a deep learning approach, specifically a deformable convolutional layer, for staging cervical cancer using multi-sequence MRI images. This is in response to the challenges doctors face in simultaneously identifying multiple sequences, a task that computer-aided diagnosis systems can potentially improve due to their vast information storage capabilities.Approach.To address the challenge of limited sample sizes, we introduce a sequence enhancement strategy to diversify samples and mitigate overfitting. We propose a novel deformable ConvLSTM module that integrates a deformable mechanism with ConvLSTM, enabling the model to adapt to data with varying structures. Furthermore, we introduce the deformable multi-sequence guidance model (DMGM) as an auxiliary diagnostic tool for cervical cancer staging.Main results.Through extensive testing, including comparative and ablation studies, we validate the effectiveness of the deformable ConvLSTM module and the DMGM. Our findings highlight the model's ability to adapt to the deformation mechanism and address the challenges in cervical cancer tumor staging, thereby overcoming the overfitting issue and ensuring the synchronization of asynchronous scan sequences. The research also utilized the multi-modal data from BraTS 2019 as an external test dataset to validate the effectiveness of the proposed methodology presented in this study.Significance.The DMGM represents the first deep learning model to analyze multiple MRI sequences for cervical cancer, demonstrating strong generalization capabilities and effective staging in small dataset scenarios. This has significant implications for both deep learning applications and medical diagnostics. The source code will be made available subsequently.

Critical updates in neuroendocrine tumors: Version 9 American Joint Committee on Cancer staging system for gastroenteropancreatic neuroendocrine tumors.

The American Joint Committee on Cancer (AJCC) staging system for all cancer sites, including gastroenteropancreatic neuroendocrine tumors (GEP-NETs), is meant to be dynamic, requiring periodic updates to optimize AJCC staging definitions. This entails the collaboration of experts charged with evaluating new evidence that supports changes to each staging system. GEP-NETs are the second most prevalent neoplasm of gastrointestinal origin after colorectal cancer. Since publication of the AJCC eighth edition, the World Health Organization has updated the classification and separates grade 3 GEP-NETs from poorly differentiated neuroendocrine carcinoma. In addition, because of major advancements in diagnostic and therapeutic technologies for GEP-NETs, AJCC version 9 advocates against the use of serum chromogranin A for the diagnosis and monitoring of GEP-NETs. Furthermore, AJCC version 9 recognizes the increasing role of endoscopy and endoscopic resection in the diagnosis and management of NETs, particularly in the stomach, duodenum, and colorectum. Finally, T1NXM0 has been added to stage I in these disease sites as well as in the appendix.

Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma.

BACKGROUND: One of the primary reasons for the dismal survival rates in pancreatic ductal adenocarcinoma (PDAC) is that most patients are usually diagnosed at late stages. There is an urgent unmet clinical need to identify and develop diagnostic methods that could precisely detect PDAC at its earliest stages. AIM: To evaluate the potential value of radiomics analysis in the differentiation of early-stage PDAC from late-stage PDAC. METHODS: A total of 71 patients with pathologically proved PDAC based on surgical resection who underwent contrast-enhanced computed tomography (CT) within 30 d prior to surgery were included in the study. Tumor staging was performed in accordance with the 8th edition of the American Joint Committee on Cancer staging system. Radiomics features were extracted from the region of interest (ROI) for each patient using Analysis Kit software. The most important and predictive radiomics features were selected using Mann-Whitney U test, univariate logistic regression analysis, and minimum redundancy maximum relevance (MRMR) method. Random forest (RF) method was used to construct the radiomics model, and 10-times leave group out cross-validation (LGOCV) method was used to validate the robustness and reproducibility of the model. RESULTS: A total of 792 radiomics features (396 from late arterial phase and 396 from portal venous phase) were extracted from the ROI for each patient using Analysis Kit software. Nine most important and predictive features were selected using Mann-Whitney U test, univariate logistic regression analysis, and MRMR method. RF method was used to construct the radiomics model with the nine most predictive radiomics features, which showed a high discriminative ability with 97.7% accuracy, 97.6% sensitivity, 97.8% specificity, 98.4% positive predictive value, and 96.8% negative predictive value. The radiomics model was proved to be robust and reproducible using 10-times LGOCV method with an average area under the curve of 0.75 by the average performance of the 10 newly built models. CONCLUSION: The radiomics model based on CT could serve as a promising non-invasive method in differential diagnosis between early and late stage PDAC.

The characteristics and prognosis of different disease patterns of multiple primary lung cancers categorized according to the 8th edition lung cancer staging system.

INTRODUCTION: The 8th edition lung cancer staging system was the first to describe the detailed diagnosis and staging of multiple primary lung cancers (MPLC). However, the characteristics and prognosis of MPLC categorized according to the new system have not been evaluated. METHOD: We retrospectively analyzed data from surgically treated MPLC patients in a single center from 2011 to 2013 and explored the characteristics and outcomes of different MPLC disease patterns. RESULTS: In total, 202 surgically treated MPLC patients were identified and classified into different groups according to disease categories and diagnostic time (multifocal ground glass/lepidic (GG/L) nodules: n = 139, second primary lung cancer (SPLC): n = 63, simultaneous MPLC (sMPLC): n = 171, and metachronous MPLC (mMPLC): n = 31). There were significant differences in clinical characteristics between SPLC and GG/L nodule patients and simultaneous and metachronous MPLC patients. The overall 1-, 3-, and 5-year lung cancer-specific survival rates of MPLC were 97.98%, 90.18%, and 82.81%, respectively. Five-year survival was better in patients with multiple GG/L nodules than in those with SPLC (87.94% vs. 71.29%, P < 0.05). Sex was an independent prognostic factor for sMPLC (5-year survival, female vs. male, 88.0% vs. 69.5%, P < 0.05), and in multiple tumors, the highest tumor stage was an independent prognostic factor for all categories of MPLC. CONCLUSIONS: The different disease patterns of MPLC have significantly different characteristics and prognoses. Clinicians should place treatment emphasis on the tumor with the highest stage as it is the main contributor to the prognosis of all categories of MPLC patients.

Clinical utility of [68Ga]Ga-PSMA-11 PET/CT in initial staging of patients with prostate cancer and importance of intraprostatic SUVmax values.

BACKGROUND: As in disease recurrence, providing clinicians with the exact extent of the disease at the time of initial diagnosis is key in the management and individual treatment of prostate cancer (PC) patients. Intending to examine the usefulness of gallium- 68 PSMA-11 positron emission tomography/computed tomography ([68Ga]Ga-PSMA-11 PET/CT) and to determine if there is a correlation between prostate-specific antigen (PSA) serum values, WHO/ISUP (World Health Organization/International Society of Urological Pathology's) grade group of the tumor and SUVmax (maximized standardized uptake value) values we retrospectively analyzed PET/CT studies performed for initial staging of the disease. PATIENTS AND METHODS: We retrospectively evaluated 34 studies of patients who underwent [68Ga]Ga-PSMA-11 PET/CT as part of the initial staging of prostate cancer. All patients had prostate cancer confirmed by histological assessment after biopsy and had Gleason score and PSA serum values obtained. The mean PSA value was 33.8 ± 40.9 nmol/L (range 2.2-232). RESULTS: Nineteen patients had extended disease (55.9%). The mean SUVmax in prostate lesions was 19.5 ± 12.6. The mean value of SUVmax of PET studies in the high-risk group was significantly higher than those of low risk (23.5 ± 13.2 and 10.6 ± 5.4, p < 0.05). A positive correlation was observed between the ISUP group and SUVmax value of prostate lesions (Pearson's r = 0.557, p < 0.01). A positive correlation was also found in the comparison between PSA values and SUVmax (Pearson's r = 0.34, p < 0.05). CONCLUSIONS: In our study, [68Ga]Ga-PSMA-11 PET/CT scans detected the extended disease in more than half of the patients. Locating disease beyond the prostate gland allowed better informed clinical decisions and modified treatment. A positive correlation was found between intraprostatic SUVmax values and the ISUP group of prostate cancer. High-risk patients had SUVmax values that were significantly higher than those of low-risk patients. The correlation between the Gleason score and SUVmax value can be explained by the increased intensity of PSMA expression as the tumor grade increases.

Application of Natural Language Processing in Electronic Health Record Data Extraction for Navigating Prostate Cancer Care: A Narrative Review.

Introduction: Natural language processing (NLP)-based data extraction from electronic health records (EHRs) holds significant potential to simplify clinical management and aid research. This review aims to evaluate the current landscape of NLP-based data extraction in prostate cancer (PCa) management. Materials and Methods: We conducted a literature search of PubMed and Google Scholar databases using the keywords: "Natural Language Processing," "Prostate Cancer," "data extraction," and "EHR" with variations of each. No language or time limits were imposed. All results were collected in a standardized manner, including country of origin, sample size, algorithm, objective of outcome, and model performance. The precision, recall, and the F1 score of studies were collected as a metric of model performance. Results: Of the 14 studies included in the review, 2 articles focused on documenting digital rectal examinations, 1 on identifying and quantifying pain secondary to PCa, 8 on extracting staging/grading information from clinical reports, with an emphasis on TNM-classification, risk stratification, and identifying metastasis, 2 articles focused on patient-centered post-treatment outcomes such as incontinence, erectile, and bowel dysfunction, and 1 on loneliness/social isolation following PCa diagnosis. All models showed moderate to high data annotation/extraction accuracy compared with the gold standard method of manual data extraction by chart review. Despite their potential, NLPs face challenges in handling ambiguous, institution-specific language and context nuances, leading to occasional inaccuracies in clinical data interpretation. Conclusion: NLP-based data extraction has effectively extracted various outcomes from PCa patients' EHRs. It holds the potential for automating outcome monitoring and data collection, resulting in time and labor savings.

Outcome of transarterial radioembolization in patients with hepatocellular carcinoma as a first-line interventional therapy and after a previous transarterial chemoembolization.

Purpose: Due to a lack of data, there is an ongoing debate regarding the optimal frontline interventional therapy for unresectable hepatocellular carcinoma (HCC). The aim of the study is to compare the results of transarterial radioembolization (TARE) as the first-line therapy and as a subsequent therapy following prior transarterial chemoembolization (TACE) in these patients. Methods: A total of 83 patients were evaluated, with 38 patients having undergone at least one TACE session prior to TARE [27 male; mean age 67.2 years; 68.4% stage Barcelona clinic liver cancer (BCLC) B, 31.6% BCLC C]; 45 patients underwent primary TARE (33 male; mean age 69.9 years; 40% BCLC B, 58% BCLC C). Clinical [age, gender, BCLC stage, activity in gigabecquerel (GBq), Child-Pugh status, portal vein thrombosis, tumor volume] and procedural [overall survival (OS), local tumor control (LTC), and progression-free survival (PFS)] data were compared. A regression analysis was performed to evaluate OS, LTC, and PFS. Results: No differences were found in OS (95% CI: 1.12, P = 0.289), LTC (95% CI: 0.003, P = 0.95), and PFS (95% CI: 0.4, P = 0.525). The regression analysis revealed a relationship between Child-Pugh score (P = 0.005), size of HCC lesions (>10 cm) (P = 0.022), and OS; neither prior TACE (Child-Pugh B patients; 95% CI: 0.120, P = 0.729) nor number of lesions (>10; 95% CI: 2.930, P = 0.087) correlated with OS. Conclusion: Prior TACE does not affect the outcome of TARE in unresectable HCC.

Comparison between pelvic MRI, CT, and PET/CT in baseline staging and radiation planning of anal squamous cell carcinoma.

PURPOSE: To investigate the differences in baseline staging of anal squamous cell carcinoma based on CT, MRI, and PET/CT, and the resultant impact on the radiation plan. METHODS: This retrospective study included consecutive patients with anal squamous cell carcinoma who underwent baseline pelvic MRI, CT, and PET/CT (all examinations within 3 weeks of each other) from January 2010 to April 2020. CTs, MRIs, and PET/CTs were re-interpreted by three separate radiologists. Several imaging features were assessed; tumor stage was determined based on the eight edition of the American Joint Committee on Cancer (AJCC) staging manual; and T (tumor), N (node), and M (metastasis) categories were determined based on National Comprehensive Cancer Network (NCCN) guidelines. Radiologist assessments were then randomly presented to a radiation oncologist who formulated the radiation plan in a blinded fashion. RESULTS: Across 28 patients (median age, 62 years [range, 31-78], T-category classification was significantly different on PET/CT compared to MRI and CT (p = 0.037 and 0.031, respectively). PET/CT staged a higher proportion of patients with T1/T2 disease (16/28, 57%) compared to MRI (11/28, 39%) and CT (10/28, 36%). MRI staged a higher proportion of patients with T3/T4 disease (14/28, 50%) compared to CT (12/28, 43%) and PET/CT (11/28, 39%). However, there was no significant difference between the three imaging modalities in terms of either N-category, AJCC staging, or NCCN TNM group classification, or in treatment planning. CONCLUSION: Our exploratory study showed that MRI demonstrated a higher proportion of T3/T4 tumors, while PET/CT demonstrated more T1/T2 tumors; however, MRI, CT, and PET/CT did not show any significant differences in AJCC and TNM group categories, nor was there any significant difference in treatment doses between them when assessed independently by an experienced radiation oncologist.