Clinical Pathological Data Research Articles

P-2267. Autopsies: Revisiting the Gold Standard

Abstract Background Infections in solid organ transplant (SOT) recipients continue to be a clinical challenge despite modern diagnostics. Autopsies have the potential to identify missed infectious diagnoses thereby informing the practice of transplant infectious diseases. We sought to evaluate the clinical value of autopsies by analyzing pre-and post-mortem infectious diagnoses of deceased SOT recipients. Methods We retrospectively reviewed deceased adult SOT recipients who underwent autopsies at our institution from January 2017 to December 2022. Electronic medical chart review of these recipients was conducted to determine the clinical infectious diagnoses during the hospitalization in which death occurred. Autopsy reports were reviewed for post-mortem pathological findings. Patients who expired near admission without clinical information or did not expire from infection were excluded. Two physicians independently reviewed clinical information and pathological data and applied the Modified Goldman Criteria to categorize clinical and autopsy infectious discrepancies. Results During the study period, 21 autopsies were performed (Table 1). Pre-mortem infectious diagnoses included bloodstream infections (n = 11), pneumonia alone including 1 case of Rhizopus and 1 case of Pneumocystis (n = 7), CMV viremia (n = 2), cholangitis (n = 1), and sepsis of undetermined etiology (n = 2). In no instances did an autopsy exam reveal an infection undiagnosed pre-mortem, and thus there were no diagnostic errors that met Modified Goldman Criteria. No specific histopathological staining was performed in the cases with CMV viremia or suspected Pneumocystis pneumonia. In the case of Rhizopus, no comment regarding evidence of angioinvasion or extent of infection was made in the autopsy report. Conclusion We did not identify any missed infectious diagnoses on post-mortem evaluation that would have impacted management or outcome. These findings emphasize the importance of collaboration between infectious disease specialists and anatomic pathologists to direct histopathologic and microbiologic testing on tissues of clinical interest. Accurate autopsy diagnoses for SOT recipients, who are at risk for opportunistic and emerging infections, can critically advance transplant medicine. Disclosures All Authors: No reported disclosures

Meningeal Metastasis from Naso-Ethmoidal Malignancies: Pathogenesis, Risk Factors, and Prognostic Impact

Introduction: Meningeal metastasis (MM) from naso-ethmoidal malignancies (NEMs) is rare, its metastatic route is still debated, and its prognostic impact remains unclear. Our aim is to analyze a retrospective series of NEMs with non-contiguous MM to study the possible route of spread and the prognostic value of MM. Materials and methods: The institutional database of SNC treated at the University of Brescia between 1995 and 2021 was reviewed. Clinical–pathological data were collected, and survivals were estimated with Kaplan–Meier. Univariate and multivariate logistic regression analysis were run to identify predictors of MM. Results: Among 296 patients, 17 experienced non-contiguous MM, all located along the dura. Intestinal-type adenocarcinoma (10/17) and olfactory neuroblastoma (3/17) were the most frequent histologies. At univariate analysis, brain edema (p < 0.0001), resection (p = 0.026) or invasion (p = 0.006) of brain parenchyma, and local (p = 0.0004) and nodal (p = 0.021) recurrence were predictors of MM. At multivariate analysis, local recurrence was confirmed as an independent factor (odds ratio: 11.88, p = 0.0005). Dural surgical resection was not a risk factor. The five-year disease-specific survival was longer in patients with exclusive MM compared with patients with distant metastasis at other sites (64.3% vs. 30.1% p = 0.046). Conclusions: Dural venous shunt is the most likely pathway of spread of MM. Local recurrence is the only independent risk factor. Exclusive MM has a better prognosis than extrameningeal metastasis.

Evaluation and treatment of lead toxicosis in rehabilitated avian species: 95 cases (2014-2023).

To evaluate avian patients presented to a wildlife rehabilitation facility with confirmed lead toxicity for clinical signs associated with blood lead level groups, efficacy of subcutaneous chelation therapy with calcium disodium EDTA, and case outcome based on presenting blood lead levels. A retrospective record review of 95 avian cases representing 19 species treated for lead toxicosis from a wildlife rehabilitation center in Wisconsin between 2014 and 2023 was conducted. Data were evaluated for presenting complaints, clinical signs, radiographic findings, chelation therapy protocol, clinical pathology data, and case outcome. A low lead level of < 20 µg/dL was the most commonly diagnosed level in avian patients. The presence of neurologic disease was found most commonly in birds with higher blood lead levels, whereas poor body condition, trauma, and anemia were most common in birds with low lead levels. Bald eagles (Haliaeetus leucocephalus) and tundra swans (Cygnus columbianus) were the most prevalent species presented. All methods of chelation therapy evaluated resulted in a reduction of blood lead levels. Patients with intake blood lead levels > 60 µg/dL were more likely to die or be euthanized while in care. A bird with a blood lead level > 60 µg/dL is more likely to present with neurologic disease and have a poor case outcome. Diluted calcium disodium EDTA was clinically effective when administered SC. Initial blood lead level, in conjunction with species, clinical signs, and radiographic changes, can help guide clinical decision-making for avian patients with lead toxicosis.

Integrating deep learning in public health: a novel approach to PICC-RVT risk assessment.

Machine learning is pivotal for predicting Peripherally Inserted Central Catheter-related venous thrombosis (PICC-RVT) risk, facilitating early diagnosis and proactive treatment. Existing models often assess PICC-RVT risk as static and discrete outcomes, which may limit their practical application. This study aims to evaluate the effectiveness of seven diverse machine learning algorithms, including three deep learning and four traditional machine learning models, that incorporate time-series data to assess PICC-RVT risk. It also seeks to identify key predictive factors for PICC-RVT using these models. We conducted a retrospective multi-center cohort study involving 5,272 patients who underwent PICC placement. After preprocessing patient data, the models were trained. Demographic, clinical pathology, and treatment data were analyzed to identify predictive factors. A variable analysis was then conducted to determine the most significant predictors of PICC-RVT. Model performance was evaluated using the Concordance Index (c-index) and the composite Brier score, and the Intraclass Correlation Coefficient (ICC) from cross-validation folds assessed model stability. Deep learning models generally outperformed traditional machine learning models in terms of predictive accuracy (mean c-index: 0.949 vs. 0.732; mean integrated Brier score: 0.046 vs. 0.093). Specifically, the DeepSurv model demonstrated exceptional precision in risk assessment (c-index: 0.95). Stability varied with the number of predictive factors, with Cox-Time showing the highest ICC (0.974) with 16 predictive factors, and DeepSurv the most stable with 26 predictive factors (ICC: 0.983). Key predictors across models included albumin levels, prefill sealant type, and activated partial thromboplastin time. Machine learning models that incorporate time-to-event data can effectively predict PICC-RVT risk. The DeepSurv model, in particular, shows excellent discriminative and calibration capabilities. Albumin levels, type of prefill sealant, and activated partial thromboplastin time are critical indicators for identifying and managing high-risk PICC-RVT patients.

Conidiobolomicose com comprometimento neurológico em ovino no Pará, Brasil

ABSTRACT: The study was conducted on a 3-year-old female Santa Inês sheep from a property located in the municipality of Santo Antônio de Tauá, west of Pará. The affected animal had a history of exophthalmos in the left eye, nasal discharge, and breathing difficulty for more than three months. On physical examination, the animal had a low body score, apathy, unilateral facial asymmetry, moderate unilateral left exophthalmos, mixed dyspnea and unilateral profuse serosanguinous nasal discharge. At necropsy, a large nasal mass was observed, predominantly yellowish-white with blackish-red areas, with an irregular, finely granular, moist, and friable surface. The mass invaded from the mucocutaneous junction of the left nostril to the choanae, infiltrated the frontal sinuses, the cribiform plate, and reached the meninges and the frontal portion of the brain. Microscopy of the surface of the nasal epithelium showed severe destruction of the turbinates and epithelia, evidenced by extensive areas of necrosis with a large amount of cellular debris, ulceration, and secondary bacterial infection. In the central nervous system there was pyogranulomatous meningitis with marked vessel congestion, as well as foci of necrosis and granulomatous inflammation. Immunohistochemistry showed intense staining of numerous intralesional hyphae for antibodies against Conidiobolus lamprauges. The diagnosis of granulomatous rhinitis of mycotic origin associated with the Conidiobolus lamprauges was based on epidemiological, clinical-pathological, and immunohistochemical data.

Myotonia Congenita in Australian Merino Sheep with a Missense Variant in CLCN1.

Myotonia congenita is a hereditary, non-dystrophic skeletal muscle disorder associated with muscle stiffness due to delayed muscle relaxation after contraction. We review myotonia congenita in domesticated animals and humans and investigated suspected myotonia congenita in a flock of Merino sheep in Australia. In 2020, a property in New South Wales reported a four-year history of lambs that would fall on disturbance before rapidly recovering, with 13 affected sheep identified in 2020. Episodes were associated with a short period of tetanic spasms and a stiff gait upon rising. Lambs were otherwise normal between episodes, although over time, lost body condition and occasionally died from misadventure. An inherited condition was considered from limited pedigree information and a preliminary diagnosis of myotonia congenita was made based on clinical presentation. Biochemistry from four sheep found variable, but typically mild increases in creatine kinase (CK) and aspartate aminotransferase (AST). Modified electromyography on six affected sheep found irregular electrical activity within the muscle. For four sheep, there were no consistent significant abnormalities on post mortem examination and histopathology-typical for this condition. A review of the Online Mendelian Inheritance in Man (OMIM) and Online Mendelian Inheritance in Animals (OMIA) databases was conducted to summarise information about myotonia congenita in humans and eight non-human species of animals. Comparing the characteristic clinical presentation, pathology and electromyography data of affected Merino sheep to similar conditions in other species assisted the identification of likely candidate genes. Whole genome sequencing of two affected lambs detected a missense variant in CLCN1 (NC_056057.1:g.107930611C>T; XM_004008136.5:c.844C>T; XP_004008185.4:p.(P282S)), with a predicted deleterious effect on protein function. An SNP genotyping assay was developed, and the variant segregated with the disease in 12 affected sheep and obligate carrier rams under an assumed recessive mode of inheritance. Identifying a likely causal variant and developing a diagnostic test allows screening of suspected affected or carrier Merino sheep for early intervention to reduce propagation of the variant within flocks.

Expression of mTOR, CD163, α-SMA, FOXp3 as survival predictors and its significance in patients with oral squamous cell carcinoma

BackgroundOral cancer; categorised under head and neck cancers (HNC) predominantly originates from squamous cells and is referred as oral squamous cell carcinoma (OSCC). Various factors (internal and external) causes OSCC. PI3K/AKT/mTOR pathways are known to be primarily mutated in HNC. mTOR remains as a key regulator for various physiological and developmental processes in normal and cancer cells. Cancer cells are surrounded by tumor microenvironment, majorly composed of immune cells. Cancer associated fibroblasts, macrophages and regulatory T cells controlled by mTOR, plays an important role in the progression of cancer.MethodsTwo hundred sixty retrospective patient samples were collected along with their demographical and clinic-pathological data. Here, we have analysed expression of mTOR, α-SMA, CD163 and FOXp3 using immunohistochemistry and their survival outcomes were calculated using Kaplan-Meier statistical method.ResultsOverexpression of CD163 and α-SMA was detected in samples of patients compared to mTOR and FOXp3. Their expression was compared with clinico-oncological parameters. We also observed two and three combinations of markers and its association with the prognosis of the cancer. The results suggest, higher the expression of all the four markers in combination correlated to poor prognosis of patients and vice-versa.ConclusionThe study reveals that over expression of CD163 and α-SMA is strongly associated with disease outcome. The combinations of all the four marker expression profile will be emerging strategy towards prognosis and also to determine survival outcomes in patients. This is a pioneering observation of these combinations of markers in OSCC despite certain limitations.

Actionable non-small cell lung cancer mutation identification by comprehensive genomic profiling for clinical trial enrollment: the European Program for the ROutine testing of Patients with Advanced lung cancer (EPROPA).

To reduce the gap about the relevant heterogeneity of molecular testing and cancer care across Europe, Women Against Lung Cancer in Europe (WALCE) promoted the European Program for ROutine testing of Patients with Advanced lung cancer (EPROPA) and provided a free-of-charge molecular profiling platform for non-small cell lung cancer sample characterization with the aim of increasing the detection of targetable drivers and improving patients' access to clinical trials. From January 2021 to December 2023, 20 centres located at 5 different European countries (Greece, Slovenia, Romania, Albania and Italy) joined EPROPA, with 555 advanced NSCLC patients registered into the program. Anonymized patients' clinical-pathological data were shared through the EPROPA web platform and tissue samples were collected to the Molecular Pathology Unit of the Reference Center (University of Turin) for molecular analyses. A comprehensive genomic profiling by targeted next-generation sequencing approach has been performed and molecular reports have been discussed within the molecular tumour board (MTB) in order to assess patients' eligibility for clinical trials. The average turnaround time was 8 days, with only 30 out of 555 (6%) tissue samples not suitable for molecular analysis. Among the 525 analyzed samples, a total of 570 molecular alterations have been identified, including 264 pathogenic targetable oncogenic alterations and 113 cases with co-occurring mutations. A total of 18 molecular alterations with potential germline and hereditary cancer syndrome implications have been reported. The identification of a clinical trial was considered for 205 patients. After MTB discussion, 30 patients were enrolled and treated in clinical studies available in Europe. Survival outcome were significantly improved in patients with targetable molecular alterations receiving a matched targeted therapy. This data confirmed the feasibility and usefulness of the program in the real-world practice scenario, supporting the implementation of NGS-based molecular characterization of NSCLC samples, in order to reduce the unequal access to tests, drugs and clinical trials in Europe.

Clinical value of epidermal growth factor receptor mutation testing in peripheral blood of patients with non-small cell lung cancer.

To investigate the clinical value of EGFR mutation testing in peripheral blood of patients with non-small cell lung cancer. This was a retrospective study. A total of 89 patients with non-small cell lung cancer (NSCLC) admitted to Affiliated Hospital of Hebei University between June 2019 and May 2023 were selected, and divided into the wild-type group and the mutant group according to the results of EGFR mutation testing in peripheral blood. Clinic pathological data of patients were collected and compared between the two groups, the correlation between EGFR mutation and the prognosis was analyzed. No statistically significant difference in the mutation rate of EGFR gene was observed between peripheral blood ctDNA and tumor tissue (p=0.879). EGFR mutation in peripheral blood ctDNA was not significantly correlated with sex, age, ethnicity and ECOG score (p>0.05). The EGFR mutation rate was increased in patients with adenocarcinoma compared with that in patient with squamous carcinoma, and in non-smoking patients compared with that in smoking patients, and the differences were statistically significant (p<0.05). The 1-, 2-, and 3-year disease-free survival (DFS) rates were significantly increased in the mutant group compared with those in the wild-type group, respectively (p<0.05). EGFR mutation in peripheral blood and tumor tissues is highly consistent in NSCLC patients. The EGFR mutation rate is higher in adenocarcinoma and non-smoking patients, and the prognosis is better in patients with EGFR mutations.

Virtual Control Groups in Non-clinical Toxicity Studies: Impacts on Toxicologic Clinical Pathology Data Interpretation.

One of the emerging concepts on the reduction of animal use in non-clinical studies is the use of virtual control group (VCG) to replace concurrent control group (CCG). The VCG involves the generation of a control data from historical control data to match a specific study design. This review focuses on two recently published proof-of-concept (POC) studies conducted in rats. One major issue that was consistently seen across these POC studies was the non-reproducibility of some quantitative endpoints between the CCG and the VCG, with clinical pathology parameters being the most affected. The inconsistencies observed with the clinical pathology parameters when using VCGs may lead to: (1) misconception about the accuracy and sensitivity of traditional clinical pathology biomarkers and its implications on safety monitoring in the clinic; (2) inability to correctly identify and characterize organ dysfunctions; (3) interference with the weight-of-evidence approach used in identifying hazards in toxicologic clinical pathology and toxicology studies at large; and (4) wrong interpretations and data reproducibility issues. Other alternatives to reduce animal use in toxicology studies are also discussed including blood microsampling for toxicokinetics, scientifically justified use of recovery animals, and appropriate use and continuous investments in new alternative methods.

FAP+ gastric cancer mesenchymal stromal cells via paracrining INHBA and remodeling ECM promote tumor progression

Gastric cancer (GC) mesenchymal stromal cells (GCMSCs) are the predominant components of the tumor microenvironment (TME) and play a role in the occurrence, development, and metastasis of tumors. However, GCMSCs exhibit phenotypic and functional heterogeneity. The key population of GCMSCs which are vital to tumor progression remains elusive. The expression of fibroblast activation protein (FAP) in gastric cancer was analyzed and verified using clinical pathology data and single-cell RNA sequencing database of gastric cancer patients. FAP positive GCMSCs (FAP+ GCMSCs) were isolated via flow cytometry and characterized through transcriptomic sequencing. The impact of conditioned medium from FAP+ GCMSCs on gastric cancer cell lines was assessed using Enzyme-linked immunosorbent assay (ELISA) and Western blot analyses. Additionally, immunohistochemistry (IHC) and Masson’s trichrome staining were employed to explore the association between FAP+ GCMSCs and extracellular matrix (ECM) deposition in gastric cancer tissues. Our study demonstrates that FAP is predominantly expressed in the mesenchymal stromal cells within the gastric cancer milieu. FAP+ GCMSCs exhibited enhanced proliferation, migration, contraction, and tumor-promoting capabilities compared to their FAP− counterparts. These cells significantly increased proliferation and migration of gastric cancer cells through the paracrine secretion of Inhibin Subunit Beta A (INHBA) and activation of the SMAD2/3 signaling pathway. Moreover, FAP+ GCMSCs also induced collagen deposition in ECM and then up-regulated invasion and stemness of GC cells. Mechanistically, this process was mediated by the interaction of collagen with Integrin Subunit Beta 1 (ITGB1), triggering the phosphorylation of Focal Adhesion Kinase (FAK) and Yes Associated Transcriptional Regulator (YAP). Our findings reveal that FAP+ GCSMCs enhanced the GC progression via releasing cytokine INHBA and remodeling ECM providing a theoretical basis for further exploration of tumor stromal-targeting therapy of gastric cancer.

Influence of Immunoexpression of Mismatch Repair Complex Proteins on Disease-Free Survival in Non-Surgically Treated Oropharyngeal Squamous Cell Carcinomas.

To evaluate the influence of MMR complex protein immunoexpression on disease-free survival in oropharyngeal SCC treated non-surgically. 85 cases of oropharyngeal SCC diagnosed and treated at the Ceará Cancer Institute were surveyed, from which clinical-pathological data and paraffin blocks of incisional biopsies were retrieved for immunohistochemical reaction for MSH2, MSH6, PMS2, MLH1 and p16. Disease-free survival was calculated and Kruskal-Wallis and Friedman/Dunn tests, chi-square and Fisher's exact, Log-Rank Mantel Cox and Cox regression were performed. In p16- tumors, loss of MSH2 expression was associated with shorter disease-free survival (p = 0.035) and mean MSH6 expression was significantly higher than MSH2 (p = 0.001). Loss of MSH2 expression in p16 + tumors was associated with longer disease-free survival compared to p16- tumors. Imbalance in the MSH6/MSH2 ratio in p16 + tumors was associated with longer survival compared to p16- tumors. MLH1/PMS2 imbalance was significantly higher in p16 + with recurrence (p = 0.003). Low MSH2 immunoexpression increased the risk of relapse by 9.10 times (CI95% 1.99 to 83.06). Microsatellite instability in oropharyngeal SCC is demonstrated by the association between loss of protein expression and its heterodimer imbalance with disease-free survival. It was demonstrated that the imbalance of the MMR complex can consequently lead to resistance to treatment and a decrease in disease-free survival in p16 + oropharyngeal SCC tumors.

Development of the interpretable typing prediction model for osteosarcoma and chondrosarcoma based on machine learning and radiomics: a multicenter retrospective study.

Osteosarcoma and chondrosarcoma are common malignant bone tumors, and accurate differentiation between these two tumors is crucial for treatment strategies and prognosis assessment. However, traditional radiological methods face diagnostic challenges due to the similarity in imaging between the two. Clinical CT images and pathological data of 76 patients confirmed by pathology from January 2018 to January 2024 were retrospectively collected from Guizhou Medical University Affiliated Hospital and Guizhou Medical University Second Affiliated Hospital. A total of 788 radiomic features, including shape, texture, and first-order statistics, were extracted in this study. Six machine learning models, including Random Forest (RF), Extra Trees (ET), AdaBoost, Gradient Boosting Tree (GB), Linear Discriminant Analysis (LDA), and XGBoost (XGB), were trained and validated. Additionally, the importance of features and the interpretability of the models were evaluated through SHAP value analysis. The RF model performed best in distinguishing between these two tumor types, with an AUC value close to perfect at 1.00. The ET and AdaBoost models also demonstrated high performance, with AUC values of 0.98 and 0.93, respectively. SHAP value analysis revealed significant influences of wavelet-transformed GLCM and First Order features on model predictions, further enhancing diagnostic interpretability. This study confirms the effectiveness of combining machine learning with radiomic features in improving the accuracy and interpretability of osteosarcoma and chondrosarcoma diagnosis. The excellent performance of the RF model is particularly suitable for complex imaging data processing, providing valuable insights for the future.

The nomogram model and its value study of Gd-EOB-DTPA enhanced MRI for preoperative diagnosis of proliferative hepatocellular carcinoma

Objective: To develop a nomogram model for preoperative diagnosis of proliferative hepatocellular carcinoma(HCC) based on gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI), and to explore its clinical value. Methods: MRI and clinical pathological data of patients confirmed by pathology as proliferative HCC (178 cases) and non-proliferative HCC (378 cases) between September 2017 and November 2022 who underwent preoperative Gd-EOB-DTPA enhanced MRI scans were retrospectively collected. The MRI features and clinical pathological characteristics of proliferative and non-proliferative HCC were evaluated. Multivariable logistic regression analysis was utilized to identify independent predictive factors for proliferative HCC, the R software was used to construct the nomogram prediction model, and its diagnostic performance was evaluated through receiver operating characteristic (ROC) curve. The calibration curve and decision curve analysis (DCA) were drawn to evaluate the calibration performance and clinical application value of the nomogram model. The optimal cut-off value was selected by calculating the Youden index to distinguish high risk and low risk. Kaplan-Meier survival curve was used to analyze the survival prognosis of proliferative and non-proliferative HCC, and log-rank test was used for comparison. Results: There were significant differences in AFP level(χ2=17.244, P<0.001), morphology of tumor(χ2=13.669, P<0.001), intertumoral fat(χ2=10.495, P=0.001), arterial phase peritumoral enhancement(χ2=37.662, P<0.001), tumor capsule(χ2=23.961, P<0.001), substantial intratumoral necrosis(χ2=77.184, P<0.001), intratumoral hemorrhage(χ2=4.892, P=0.027), peritumoral hypointense in hepatobiliary phase(χ2=47.675, P<0.001), rim arterial phase hyperenhancement(χ2=115.976, P<0.001), intratumoral artery(χ2=15.528, P<0.001) and venous tumor thrombus(χ2=10.532, P=0.001) between proliferative and non-proliferative HCC groups. Multivariate Logistic regression analysis showed that AFP>200 ng/ml(OR=0.640, P=0.044), no intertumoral fat(OR=1.947, P=0.033), substantial intratumoral necrosis(OR=0.480, P=0.003), peritumoral hypointense in hepatobiliary phase(OR=0.432, P=0.001), and rim arterial phase hyperenhancement(OR=0.180, P<0.001) were independent predictors of preoperative diagnosis of proliferative HCC. Based on the independent predictors, a nomogram model for preoperative prediction of proliferative HCC was established. The area under the ROC curve of the model for predicting proliferative HCC was 0.772 (95%CI: 0.735～0.807), the sensitivity was 69.1%, and the specificity was 75.4%. The calibration curve and DCA curve showed that the calibration performance and clinical applicability of the nomogram model were good. Kaplan-Meier curve showed that the survival rate of patients with proliferative HCC after hepatectomy was significantly lower than that of non-proliferative HCC (P<0.001), and the high-risk group was significantly lower than the low-risk group (P<0.001). Conclusions: The nomogram prediction model based on Gd-EOB-DTPA enhanced MRI imaging features combined with AFP >200 ng/ml can accurately diagnose proliferative HCC before operation and predict prognosis.

Analysis of therapeutic effects and influencing factors of ICIs in lung-cancer patients.

The aim of this retrospective study was to analyze the efficacy and risk factors of immune checkpoint inhibitors (ICIs) in lung cancer patients. One hundred lung cancer patients who were treated in our hospital from May 2021 to May 2023 were selected as the study subjects and divided into chemotherapy group (n = 50) and ICIs group (n = 50), in which the chemotherapy group was given the combined treatment of vincristine and cisplatin (NP), while the ICIs group was given ICIs for treatment. The therapeutic effect and adverse reactions (hypertriglyceridemia, anemia, hypertension and hypoproteinemia) of the two groups were compared, and fasting venous blood was collected. The levels of carcinoembryonic antigen (CEA) and cancer antigen 199 (CA199) were compared between the two groups before and after treatment. According to the therapeutic effect, 100 patients with lung cancer were divided into complete remission (CR) + partial remission (PR) group (n = 52) and stable (SD) + progressive (PD) group (n = 48). The clinical data and pathologic data of the two groups were compared. The rates of objective effective rate (ORR) in chemotherapy group and ICIs group were 36.00% and 68.00% respectively, and the level of ORR in ICIs group was significantly higher than that in chemotherapy group, with statistical significance (P < 0.05). There was no significant difference in serum CEA and CA199 levels between the two groups before operation (P > 0.05). Three months after operation, the serum CEA and CA199 levels in ICIs group were significantly lower than those in chemotherapy group, and the difference was statistically significant (P < 0.05). The adverse reactions of hypertriglyceridemia, anemia, hypertension and hypoproteinemia in chemotherapy group and ICIs group during treatment were all grade 1-2, and the incidence of adverse reactions was similar between the two groups (P > 0.05). There was no significant difference in sex, age, anatomic position, pathologic type, smoking history and differentiation between the two groups (P > 0.05). In SD + PD group, the preoperative maximum tumor diameter > 4cm, tumor node metastasis (TNM) stage IV, lactate dehydrogenase (LDH) ≥ 183 U/L, and tumor volume ≥ 120m3 were significantly higher than those in CR + PR group, and the prognostic nutritional index (PNI) ≥ 41.8 and the proportion of ICIs were significantly lower than those in CR + PR group, with statistical significance (P < 0.05). Multifactorial logistic regression analysis showed that preoperative maximum tumor diameter > 4cm and LDH ≥ 183U/L were risk factors for poor lung cancer outcome, and PNI ≥ 41.8 and ICIs treatment were protective factors for poor lung cancer outcome (P < 0.05). ICIs is effective in the treatment of lung cancer, which can obviously reduce the tumor load and has high safety. In addition, the maximum tumor diameter and LDH are risk factors that affect the poor curative effect of lung cancer. High PNI level and ICIs treatment are helpful to improve the effect of lung cancer, and early monitoring is helpful to guide the treatment plan and evaluate the treatment effect.

Characteristics of gastric cancers developed more than 10 years after eradication of Helicobacter pylori.

Helicobacter pylori (H pylori) eradication is expected to effectively prevent gastric cancer (GC). However, GC cases may occur even longer than 10 years after H pylori eradication (L10AE). Moreover, the associated factors and characteristics are unknown. In this retrospective, single-center study conducted between 2017 and 2022, patients with GC diagnosed after H pylori eradication were enrolled and categorized into groups according to whether they were shorter than 10 years after H pylori eradication (S10AE) or L10AE. Patients were also categorized according to the depth of cancer invasion. Clinical data, pathological data, and risk factors were analyzed using logistic regression. Clinicopathological characteristics of GC diagnosed at L10AE and those invading the submucosal tissue or deeper (SMD) were investigated. In total, 202 cases of GC occurring after H pylori eradication were identified. Comparison of 158 S10AE and 44 L10AE GC cases revealed a significantly longer surveillance interval (SI) in L10AE cases (median: 2.0 vs 1.0 years, P = .01). Comparison of 150 intramucosal and 52 SMD GC cases revealed that L10AE GC cases were significantly more frequent amongst the SMD cases (18.0% vs 32.7%, P = .03). Pathologically, undifferentiated and mixed types were significantly more frequent in GC cases with SMD invasion (P < .001). Multivariate analysis revealed that L10AE was significantly related to GC cases with SMD invasion (odds ratio, 2.45; 95% confidence interval, 1.15-5.11; P = .019). SI was significantly longer in GC that developed at L10AE than at S10AE. In addition, GC with SMD invasion was more frequently detected in L10AE than in S10AE. Our data indicated that SI should not be groundlessly extended in patients at L10AE.

Oncointerpreter.ai enables interactive, personalized summarization of cancer diagnostics data.

Cancer diagnosis comes as a shock to many patients, and many of them feel unprepared to handle the complexity of the life-changing event, understand technicalities of the diagnostic reports, and fully engage with the clinical team regarding the personalized clinical decision-making. We develop Oncointerpreter.ai an interactive resource to offer personalized summarization of clinical cancer genomic and pathological data, and frame questions or address queries about therapeutic opportunities in near-real time via a graphical interface. It is built on the Mistral-7B and Llama-2 7B large language models trained on a local database trained using a large, curated corpus. We showcase its utility with case studies, where Oncointerpreter.ai extracted key clinical and molecular attributes from deidentified pathology and clinical genomics reports, summarized their contextual significance and answered queries on pertinent treatment options. Oncointerpreter also provided personalized summary of currently active clinical trials that match the patients' disease status, their selection criteria, and geographic locations. Benchmarking and comparative assessment indicated that the model responses were generally consistent, and hallucination, ie, factually incorrect or nonsensical response was rare; treatment- and outcome related queries led to context-aware responses, and response time correlated with verbosity. The choice of model and domain-specific training also affected the response quality. Oncointerpreter.ai can aid the existing clinical care with interactive, individualized summarization of diagnostics data to promote informed dialogs with the patients with new cancer diagnoses. https://github.com/Siris2314/Oncointerpreter.

Construction and interpretation of machine learning-based prognostic models for survival prediction among intestinal-type and diffuse-type gastric cancer patients

BackgroundGastric cancer is one of the most common malignant tumors worldwide, with high incidence and mortality rates, and it has a complex etiology and complex pathological features. Depending on the tumor type, gastric cancer can be classified as intestinal-type and diffuse-type gastric cancer, each with distinct pathogenic mechanisms and clinical presentations. In recent years, machine learning techniques have been widely applied in the medical field, offering new perspectives for the diagnosis, treatment, and prognosis of gastric cancer patients.MethodsThis study recruited 2158 gastric cancer patients and constructed prognostic prediction models for both intestinal-type and diffuse-type gastric cancer. Clinical pathological data were collected from patients, and machine learning algorithms were used for feature selection and model construction. The performance of the models was validated with training and testing datasets. The Shapley additive explanations (SHAP) values were used to interpret the model predictions and identify the main factors that influence patient survival.ResultsIn the prognostic model for intestinal-type gastric cancer, the gradient boosting decision tree (GBDT) model demonstrated the best performance, with key features including pTNM, CA125, tumor size, CA199, and PALB. Similarly, in the prognostic model for diffuse-type gastric cancer, the GBDT model was utilized, with key features comprising pTNM, Borrmann type IV disease, lymphocyte (LYM), lactate dehydrogenase (LDH), potassium (K), perineural invasion (PNI), tumor size, and whole stomach location. Risk stratification analysis revealed that the prognosis of high-risk patients was significantly worse than that of low-risk patients.ConclusionMachine learning shows great potential in predicting survival outcomes of gastric cancer patients, providing strong support for the development of personalized treatment plans.

MicroRNA-582-5p inhibits the progression of gastric cancer cells and their resistance to oxaliplatin by suppressing ATG7 expression.

Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract worldwide. Both environmental and genetic factors contribute to the occurrence and development of GC. Surgery and chemotherapy are the main treatment modalities for gastric cancer; however, some patients show insensitivity to chemotherapeutic agents. Chemotherapy resistance is one of the primary reasons for poor treatment outcomes and the high likelihood of recurrence and metastasis in gastric cancer patients. Numerous studies have confirmed a correlation between the dysregulation of microRNA expression and the development of various malignant tumors, as well as their resistance to chemotherapeutic agents. However, the role of microRNA-582-3p in gastric cancer cells and its mechanism in the resistance of gastric cancer cells to oxaliplatin have not been studied. We first used q-PCR, CCK8, transwell, and scratch assays to validate the expression of microRNA-582-3p in gastric cancer tissues and cells, while also analyzing the relationship between its expression levels and the clinical pathological data of patients. Additionally, we further confirmed the impact of microRNA-582-3p on gastric cancer cell progression and oxaliplatin resistance through knockdown and overexpression experiments. Subsequently, to explore the specific mechanisms of microRNA-582-3p in gastric cancer, we verified the downstream target of microRNA-582-3p, ATG7, using dual-luciferase reporter assays and examined the effect of ATG7 on gastric cancer cell functions. Moreover, we conducted rescue experiments to further validate the interaction between microRNA-582-3p and ATG7. Our experimental results confirmed that microRNA-582-3p is lowly expressed in gastric cancer tissues and cells, and the expression level of miR-582-5p is correlated with the T stage of patients, while showing no correlation with the patients' gender, age, tumor size, degree of differentiation, or N stage. Additionally, we found that microRNA-582-3p functions as a tumor suppressor in gastric cancer cells, as its overexpression inhibits the biological functions of gastric cancer cells and increases their sensitivity to oxaliplatin. Furthermore, we identified binding sites between microRNA-582-3p and the autophagy-related gene ATG7, observing that knockdown of microRNA-582-3p increases ATG7 expression, while its overexpression reduces ATG7 levels. Moreover, ATG7 is overexpressed in gastric cancer cells; knockdown of ATG7 inhibits the biological functions of gastric cancer cells and increases their sensitivity to oxaliplatin, whereas overexpression of ATG7 reverses the inhibitory effect of miR-582-5p on gastric cancer. Our study confirms that microRNA-582-3p acts as a tumor suppressor in gastric cancer cells, and its role may be mediated through the regulation of ATG7 expression levels. MicroRNA-582-3p may serve as a potential target for gastric cancer treatment and a predictive biomarker.

Prediction of prostate cancer recurrence after radiotherapy using a fused machine learning approach: utilizing radiomics from pretreatment T2W MRI images with clinical and pathological information

The risk of biochemical recurrence (BCR) after radiotherapy for localized prostate cancer (PCa) varies widely within standard risk groups. There is a need for low-cost tools to more robustly predict recurrence and personalize therapy. Radiomic features from pretreatment MRI show potential as noninvasive biomarkers for BCR prediction. However, previous research has not fully combined radiomics with clinical and pathological data to predict BCR in PCa patients following radiotherapy.&#xD;Purpose: This study aims to predict 5-year BCR using radiomics from pretreatment T2W MRI and clinical-pathological data in PCa patients treated with radiation therapy, and to develop a unified model compatible with both 1.5T and 3T MRI scanners.&#xD;Methods: A total of 150 T2W scans and clinical parameters were preprocessed. Of these, 120 cases were used for training and validation, and 30 for testing. Four distinct machine learning models were developed: Model 1 used radiomics, Model 2 used clinical and pathological data, and Model 3 combined these using late fusion. Model 4 integrated radiomic and clinical-pathological data using early fusion.&#xD;Results: Model 1 achieved an AUC of 0.73, while Model 2 had an AUC of 0.64 for predicting outcomes in 30 new test cases. Model 3, using late fusion, had an AUC of 0.69. Early fusion models showed strong potential, with Model 4 reaching an AUC of 0.84, highlighting the effectiveness of the early fusion model.&#xD;Conclusions: This study is the first to use a fusion technique for predicting BCR in PCa patients following radiotherapy, utilizing pre-treatment T2W MRI images and clinical-pathological data. The methodology improves predictive accuracy by fusing radiomics with clinical-pathological information, even with a relatively small dataset, and introduces the first unified model for both 1.5T and 3T MRI images.