Predict Treatment Outcome Research Articles

Development and validation of the General attitude towards Medication Questionnaire (GAMQ)

BackgroundAttitudes towards medication can affect treatment outcomes and adherence through mechanisms such as placebo and nocebo effects. Questionnaires assessing both negative and positive attitudes towards medication in general, which can be used across a variety of settings and in both patient and non-patient samples, are however lacking. To fill this gap, we developed and validated the General Attitude towards Medication Questionnaire (GAMQ).MethodsItems were selected and adapted from existing questionnaires by a group of experts. Validation of the original Dutch version took place in 4 samples: 2 recruited from the general population (n = 508; n = 279) and 2 patient samples (patients with rheumatoid arthritis, n = 121; patients with atopic dermatitis, n = 70). We evaluated the psychometric properties of the GAMQ by determining the factor structure and its stability across samples, internal consistency, and convergent validity.ResultsThe GAMQ contains 6 positive and 6 negatively worded items. A factor structure was observed with three subscales, representing ‘Trust in medication’, ‘Concerns about medication’, and ‘Reluctance to use medication’. The fit of the factor structure was satisfactory across samples, considering classic cut-offs, with an adequate or close to adequate fit. The total scale showed good internal consistency, good convergent validity with related scales (i.e., Beliefs about Medicines Questionnaire - General and a single medication attitude scale), and concurrent validity as reflected in associations with expectations about pain- and itch-relieving medication. It was not consistently or strongly associated with demographic or health-related characteristics.ConclusionsThe newly developed GAMQ showed satisfactory psychometric properties in a variety of populations, although limitations should be considered. The GAMQ is the first scale to assess both positive and negative attitudes towards medication in general, providing indicators of Trust, Concerns, and Reluctance regarding medication. The scale may be an informative measure for predicting treatment outcomes and adherence, as well as placebo and nocebo effects in diverse samples.

A novel approach to engineering three-dimensional bladder tumor models for drug testing.

Bladder cancer (BCa) poses a significant health challenge, particularly affecting men with higher incidence and mortality rates. Addressing the need for improved predictive models in BCa treatment, this study introduces an innovative 3D in vitro patient-derived bladder cancer tumor model, utilizing decellularized pig bladders as scaffolds. Traditional 2D cell cultures, insufficient in replicating tumor microenvironments, have driven the development of sophisticated 3D models. The study successfully achieved pig bladder decellularization through multiple cycles of immersion in salt solutions, resulting in notable macroscopic and histological changes. This process confirmed the removal of cellular components while preserving the native extracellular matrix (ECM). Quantitative analysis demonstrated the efficacy of decellularization, with a remarkable reduction in DNA concentration, signifying the removal of over 95% of cellular material. In the development of the in vitro bladder cancer model, muscle invasive bladder cancer patients' cells were cultured within decellularized pig bladders, yielding a three-dimensional cancer model. Optimal results were attained using an air-liquid interface technique, with cells injected directly into the scaffold at three distinct time points. Histological evaluations showcased characteristics resembling in vivo tumors derived from bladder cancer patients' cells. To demonstrate the 3D cancer model's effectiveness as a drug screening platform, the study treated it with Cisplatin (Cis), Gemcitabine (Gem), and a combination of both drugs. Comprehensive cell viability assays and histological analyses illustrated changes in cell survival and proliferation. The model exhibited promising correlations with clinical outcomes, boasting an 83.3% reliability rate in predicting treatment responses. Comparison with traditional 2D cultures and spheroids underscored the 3D model's superiority in reliability, with an 83.3% predictive capacity compared to 50% for spheroids and 33.3% for 2D culture. Acknowledging limitations, such as the absence of immune and stromal components, the study suggests avenues for future improvements. In conclusion, this innovative 3D bladder cancer model, combining decellularization and patient-derived cells, marks a significant advancement in preclinical drug testing. Its potential for predicting treatment outcomes and capturing patient-specific responses opens new avenues for personalized medicine in bladder cancer therapeutics. Future refinements and validations with larger patient cohorts hold promise for revolutionizing BCa research and treatment strategies.

Impact of IL-8 on survival after TARE in HCC: a comprehensive investigation and external validation from the SORAMIC trial.

In the treatment of hepatocellular carcinoma (HCC) with transarterial radioembolization (TARE), identifying reliable biomarkers for predicting survival outcomes remains a critical challenge. We aimed to address this gap by investigating the significance of serum cytokines associated with inflammation as potential biomarkers for the selection of patients for TARE. Our retrospective study involved 161 patients diagnosed with HCC who underwent Y90 radioembolization at our medical center between 2010 and 2020. Serum samples from a subset of 78 patients were retrospectively analyzed to determine the concentrations of pro-inflammatory cytokines. The results from the prospective SORAMIC trial were used for independent validation. With a median overall survival of 36 weeks (range 4-436), our study showed the strongest correlation between 12-week survival and IL-8 levels before treatment (p < 0.001), while other relevant interleukins, interferon-α2, INF-γ, TNF-α and MCP-1 were not associated with survival. IL-8 levels below the cut-off of 190 pg/mL were significantly associated with increased 12-week and 24-week survival, with hazard ratios of 19.01 (95% CI: 2.29-157.89) and 2.57 (95% CI: 1.05-6.31), respectively (p = 0.006 and p = 0.039, respectively). In the adjusted multivariate analysis, the 190 pg/mL cut-off for IL-8 remained independently associated with 12- (p = 0.011) and 24-week survival (p = 0.039). Similarly, the SORAMIC population showed a strong association between IL-8 levels and 36-week survival (p = 0.03). Our study emphasizes the pivotal role of IL-8 as a valuable parameter, demonstrating its potential for predicting treatment outcomes and assessing liver function in patients with HCC undergoing TARE. The robustness of these findings warrants further validation.

Duration of insomnia and success expectancy predict treatment outcome of iCBT for insomnia

IntroductionIdentifying prognostic factors of treatment outcome may assist in customizing an intervention to a patient's needs. Hence, we conducted a secondary analysis of data from a randomized controlled trial to investigate the effectiveness of an internet-based cognitive behavioral therapy for insomnia (iCBT-I) to find patient characteristics that may predict the change of insomnia severity after treatment.Materials and methodsIn this exploratory analysis involving 94 chronic insomnia patients, we examined the predictive value of several self-reported measures, medical history, and sociodemographic variables to psychological distress with separate linear regression models. The main outcome was the Insomnia Severity Index score improvement from pre- to post-treatmentResultsThe study found that duration of insomnia, b (SE) = −0.02 (0.01), p = 0.01, and attitudes about the expected treatment success, b (SE) = 0.80 (0.27), p = 0.004, were predictors of a better outcome. Moreover, a better outcome was associated with a lower level of the following traits: attention seeking, b (SE) = −1.06 (0.51), p = 0.04; grandiosity, b (SE) = −1.50 (0.57), p = 0.01; distractibility, b (SE) = −1.57 (0.75), p = 0.04; and rigid perfectionism, b (SE) = −1.32 (0.65), p = 0.05.ConclusionOur results suggest that iCBT-I might be particularly beneficial for patients with higher expectations from the therapy and those who have a shorter duration of insomnia. Some pronounced personality traits, such as attention seeking, grandiosity, distractibility, and rigid perfectionism, may predict worse outcomes. However, because this was a post-hoc analysis, our results must be considered exploratory and verified in further studies.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT04300218?cond=NCT04300218&amp;amp;rank=1, Identifier NCT04300218.

De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data.

In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy-number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variants calling, LongSom re-annotates marker gene based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.

Pre-treatment DTI markers: predicting clinical outcomes in microvascular decompression for classic trigeminal neuralgia - a systematic review.

Trigeminal neuralgia (TN) is a severe chronic pain condition, typically affecting patients over 50-year-old, caused by the compression of the nerve at the root entry zone (REZ) by blood vessels. While the diagnosis is clinical, advanced imaging like diffusion tensor imaging (DTI) is crucial to identify underlying causes and assessing nerve damage. DTI may help develop neuroimaging markers to improve understanding of TN and predict surgical outcomes. The goal of the present systematic review is to evaluate the effectiveness of DTI and tractography in order to better assess treatment planning and outcome prediction through the analysis of trigeminal nerve alterations. The authors conducted a systematic review and meta-analysis of the literature to compare radiological parameters identified in pre- and post-operative MRI with DTI sequences, including fractional anisotropy (FA), quantity of anisotropy (QA), radial diffusivity (RD), and to correlate these findings with post-operative clinical outcomes. A comprehensive search of the PubMed and Scopus databases was carried out for studies published between April 2010 and January 2024. This review included 11 studies and 603 patients. Of the 363 patients with trigeminal neuralgia (TN), 193 underwent microvascular decompression (MVD), with 72.5% showing clinical improvement and 27.5% not improving, possibly due to chronic nerve damage. Four studies assessed radiological parameters before and after MVD, while two focused only on post-MVD data. The mean fractional anisotropy (FA) in affected nerves increased from 0.328 before MVD to 0.382 afterward. Five studies did not report postoperative outcomes, just comparing radiological parameters in TN patients versus healthy controls. Recent studies show that MRI-DTI parameters, including FA, RD, and QA, are useful for diagnosing trigeminal neuralgia and predicting treatment outcomes. Lower FA and higher RD values indicate better results after surgery. More research is mandatory to guide treatment decisions and enhance patients' care.

Prediction of Treatment Response Based on Nutritional Status and Tumor Immunity in Oropharyngeal Cancer Patients Treated With Chemoradiotherapy.

Radiotherapy (RT) for advanced oropharyngeal cancer (OPC) is effective, especially when combined with chemotherapy (CRT). However, its success can vary depending on factors, such as tumor stage, HPV infection (p16 status), and the patient's nutritional and immune status. This study examined the controlling nutritional status (CONUT) score and tumor immunity as predictive factors for treatment outcomes in OPC, aiming to develop a personalized risk score. A retrospective analysis was conducted on 84 patients with OPC treated with definitive RT or CRT, and survival outcomes were compared based on various factors, including BMI, CONUT score, CD8 expression, and HLA class II expression. We observed better overall survival (OS) rates in CD8-positive patients and those with higher HLA class II expression. The univariate analysis identified stage, p16 status, BMI, CONUT score, and CD8 expression as significantly associated with OS. In multivariate analysis, stage, BMI, and CONUT score remained significant predictors of OS. A risk scoring system was developed based on stage, p16 status, BMI, CONUT score, and CD8 expression. Patients were categorized into low-risk and high-risk groups, with significantly better survival in the low-risk group. A combined risk score incorporating clinical, nutritional, and immune factors can improve the prediction of treatment outcomes for OPC patients. This risk stratification may enable personalized treatment plans and improve ΟS rates.

Microbiome-Based Colon Cancer Patient Stratification and Survival Analysis.

Colorectal cancer (CRC) is any cancer that starts in the colon or the rectum and presents a significant health concern. It is the third most diagnosed and the second deadliest cancer, with an estimated 153,020 new cases and 52,550 deaths in 2023. The severity of colon cancer may be attributed to its ability to avoid the host immune system and growth suppressors, its asymptomatic nature in the early stages, its association with a continually ageing population and unfavourable diet and obesity. The composition of the gut microbiome plays an important role in the development of CRC and presents as an important target in early detection and in predicting treatment outcomes in CRC. This study aims to identify microbiome-specific derived clusters in CRC patients and conduct subsequent survival analysis using the specific microbiome features within clusters. Consensus clustering and feature selection, involving a Kruskal-Wallis test, a random forest and least absolute shrinkage and selection operator (LASSO) were applied resulting in the identification of differently expressed microbiomes between clusters. Lastly, survival analysis was performed on the selected features using Kaplan-Meier curves and Cox regression. K-means clustering, as selected using consensus clustering interpretation, presented three distinct clusters with clear differences in alpha and beta diversity and baseline clinical variables. A total 1311 of the 1406 microbes were selected using the Kruskal Wallis and passed to the random forest and LASSO, which narrowed the dataset to 140 features. Following the survival analysis, eight microbiome species, namely N4likevirus, Ambidensovirus, Synechococcus, Thermithiobacillus, Hydrocarboniphaga, Rhodovibrio, Gloeobacter and Candidatus Nitrosotenuis, were selected as significant in clustering and survival. This study reveals the heterogeneity of the CRC microbiome and its effect on disease prognosis and necessitates further exploration of the biological mechanisms of these selected microbiomes as well further investigation of whether the approach depicted here is applicable to other cancer types.

Identification of Molecular Subgroups of Muscle-Invasive Urothelial Bladder Cancer and Their Impact on Treatment Outcome.

Neoadjuvant chemotherapy (NACT) improves muscle-invasive bladder cancer (MIBC) survival. However, its efficacy is limited to a group of patients. This study explored CK5/6 and GATA3 for molecular subtyping and their prediction to response in patients with MIBC. This is a prospective study that includes 50 patients with TCC bladder. All Patients received 4 cycles neoadjuvant gemcitabine/ cisplatin then guided to further treatment according to the response to NACT. Responders (CR & PR) went for CCRTH whereas non-responders (SD & PD) went surgery if resectable or second line chemotherapy if non-resectable. The baseline TUR pathology specimens were examined for histopathological feature and CK5/6 and GATA3 and divided into 4 molecular subgroups. The patients were divided into four molecular subgroups: luminal (n=12/26.7%), basal (n=8/17.8%), double-positive (n=21/26.7%), and double-negative (n=4/8.9%). There was no clinicopathological difference seen among the 4 molecular subgroups. The PFS was higher in patients with GATA3 positive (24 months) than GATA3 negative (17 months). Yet, it did not reach a statistically significant value (P = 0.1605). On the other hand, PFS was not affected by either CK5/6 status or different molecular subgroups. The OS was better in the luminal subgroup than the basal (20.8 months versus 16.16 months respectively, While the double positive showed the highest OS of 26 months(P=0.0352). GATA3 and CK5/6 IHC can classify MIBCs into four subtypes. These subtypes predicted treatment outcomes, however, were not correlated with the response to NACT. GATA3-positive tumors, luminal and double-positive subtypes tend to have higher OS and PFS. CK5/6 positivity did not impact the treatment outcome.

Unilateral and bilateral theta burst stimulation for treatment-resistant depression: Follow up on a naturalistic observation study

ObjectivesTheta burst stimulation (TBS) is a novel and faster modality of transcranial magnetic stimulation, which is showing promise as a treatment-resistant depression (TRD) treatment. Though TBS can be applied unilaterally or bilaterally, few studies have compared the effectiveness of both approaches in a naturalistic clinical sample. In this retrospective chart review, we aimed to: (1) replicate previous bilateral sequential TBS effectiveness in a larger cohort of patients at a single centre, (2) present treatment outcome data between unilateral and bilateral TBS approaches, (3) investigate baseline factors associated with our observed outcomes, and (4) examine the sustainability of response, with follow-up data up to 6 months from patients. MethodsWe included 161 patients who received TBS (unilateral: n = 64 (40%), 45.55 ± 14.25 years old, 55% females; bilateral: n = 97 (60%), 47.67 ± 15.11 years old, 58% females). ResultsFirstly, we observed 47% response and 34% remission in the bilateral group, replicating findings from a smaller naturalistic study from our group; patients receiving unilateral TBS displayed 36% response and 26% remission, with no significant differences found between unilateral and bilateral TBS in remission and response rates. Secondly, bilaterally stimulated patients needed fewer treatments than those stimulated unilaterally (27 vs 29 on average respectively, t [159] = 3.31, p = .001), and had significantly lower anxiety symptoms post treatment (GAD-7) relative to patients receiving unilateral stimulation, F (1,148) = 3.95, p = .049. Thirdly, no baseline factors were found to predict treatment outcomes. Lastly, after six months, 69% of patients who met the response criteria did not require additional treatment or a change in medication. ConclusionsOur findings support the efficacy and tolerability of TBS in TRD and indicate that bilateral TBS may have a superior anxiolytic effect and offer a slightly faster time to response.

Predicting Early Treatment Effectiveness in Bell's Palsy Using Machine Learning: A Focus on Corticosteroids and Antivirals.

Facial nerve paralysis, particularly Bell's palsy, manifests as a rapid onset of unilateral facial weakness or paralysis. Despite most patients recovering within three to six months, a significant proportion experience poor recovery. This study utilized six machine learning models to investigate the effectiveness of early treatment in Bell's palsy. We applied data from 17hospitals in Scotland to predict treatment outcomes. Patients were randomized into four groups: Prednisolone (corticosteroids), Acyclovir (antivirals), both, and placebo. Outcomes, defined as full resolution of symptoms, were assessed using the House-Brackmann scale at 3 and 9 months post-treatment. We employed six different machine learning models to predict recovery outcomes and evaluated model performance using AUC, precision, recall, and F1-score. Among 493 patients, 72.6% recovered after three months and 89.5% after nine months. Logistic regression demonstrated the highest predictive performance for both 3-month (AUC = 0.751) and 9-month recovery (AUC = 0.720). Additionally, several models achieved Precision levels exceeding 0.9. We further employed the best-performing logistic regression for feature ranking, indicating that the patient's age and prednisolone administration are the most significant predictors of recovery. The results highlight the potential of machine learning models in predicting the effectiveness of early treatment. This study conducted a comprehensive comparison of six different machine learning models, with the logistic regression showing the highest predictive performance for both 3-month and 9-month recovery. Additionally, feature ranking using logistic regression supported the importance of Prednisolone in treatment. Notably, our findings revealed the significance of age in prognosis evaluation for the first time. This suggests that future research should further develop age-specific prognostic models, enabling clinicians to tailor individualized treatment strategies more effectively. This previously unrecognized discovery provides a foundation for prognostic analysis in Bell's palsy patients.

Prediction of early recurrence of adult-type diffuse gliomas following radiotherapy using multi-modal magnetic resonance images.

Adult-type diffuse gliomas are among the central nervous system's most aggressive malignant primary neoplasms. Despite advancements in systemic therapies and technological improvements in radiation oncology treatment delivery, the survival outcome for these patients remains poor. Fast and accurate assessment of tumor response to oncologic treatments is crucial, as it can enable the early detection of recurrent or refractory gliomas, thereby allowing timely intervention with life-prolonging salvage therapies. Radiomics is a developing field with great potential to improve medical image interpretation. This study aims to apply a radiomics-based predictive model for classifying response to radiotherapy within the first 3 months post-treatment. Ninety-five patients were selected from the Burdenko Glioblastoma Progression Dataset. Tumor regions were delineated in the axial plane on contrast-enhanced T1(CE T1W) and T2 fluid-attenuated inversion recovery (T2_FLAIR) magnetic resonance imaging (MRI). Hand-crafted radiomic (HCR) features, including first- and second-order features, were extracted using PyRadiomics (3.7.6) in Python (3.10). Then, recursive feature elimination with a random forest (RF) classifier was applied for feature dimensionality reduction. RF and support vector machine (SVM) classifiers were built to predict treatment outcomes using the selected features. Leave-one-out cross-validation was employed to tune hyperparameters and evaluate the models. For each segmented target, 186 HCR features were extracted from the MRI sequence. Using the top-ranked radiomic features from a combination of CE T1W and T2_FLAIR, an optimized classifier achieved the highest averaged area under the curve (AUC) of 0.829±0.075 using the RF classifier. The HCR features of CE T1W produced the worst outcomes among all models (0.603±0.024 and 0.615±0.075 for RF and SVM classifiers, respectively). We developed and evaluated a radiomics-based predictive model for early tumor response to radiotherapy, demonstrating excellent performance supported by high AUC values. This model, harnessing radiomic features from multi-modal MRI, showed superior predictive performance compared to single-modal MRI approaches. These results underscore the potential of radiomics in clinical decision support for this disease process.

Bibliometric and Content Analysis of Articles on Artificial Intelligence in Healthcare

The use of artificial intelligence in the healthcare sector is becoming widespread for reasons such as analyzing digital patient data, including it in decision-making processes, improving the quality of healthcare services, and providing cost, time, and access advantages. This study aims to evaluate published articles on bibliometric indicators and the use of artificial intelligence in the healthcare sector and examine the content of the most cited articles. Articles about artificial intelligence in the health sector in the Web of Science database were included in the study using the criteria of “keyword, publication year, and publication language”. The research covers 2,680 articles published in English by 14,195 authors from 106 countries in 1084 journals between 2020-2024. 4,671 different keywords were used in the published articles. The country that published the most was “USA”, the journal was “Journal of Medical Internet Research”, the author was “Meng Ji”, and the most cited author was “Weihua Li”. The 55 most cited (≥50) articles focused on themes related to “diagnosis of COVID-19 disease”, “diagnosis of diseases”, “detection and classification of cancerous cells”, “identification of disease risk factors and disease prediction”, “prediction of treatment outcomes”, “prediction of disease course”, “personalized treatment recommendations”, “decision-making processes”, “ethical considerations, risks, and responsibilities”. With the COVID-19 pandemic, it is seen that the number of articles on artificial intelligence in the healthcare sector has increased exponentially. In the research, articles related to artificial intelligence in the health sector were examined, and a framework was created for researchers by revealing the most publishing countries, journals, authors, most cited authors, and keywords that were used the most.

Identifying Predictors of Unfavorable Treatment Outcomes in Tuberculosis Patients.

In South Korea, there has been a continuous decrease in the incidence of tuberculosis (TB) attributable to a national TB elimination program; however, TB still poses a significant socioeconomic burden. This study aimed to analyze factors associated with successful TB treatment and to identify refractory patient groups with unfavorable outcomes. We analyzed anonymized data on 89,150 patients with TB provided by the Korea Disease Control and Prevention Agency. Specifically, we collected independent variables, which were categorized as individual, regional, and medical facility factors. Individual factors included age, sex, nationality, TB type, drug-resistant status, category of TB, housing type, underlying disease status, number of referrals, and smoking status. Regional factors referred to the region where the TB case was reported. Medical facility factors included the first visit to a medical facility, categorized by hospital type and the distinction between private-public mix (PPM) and non-PPM depending on the presence or absence of dedicated TB nurses. These factors were analyzed in relation to treatment success to identify refractory patient groups with unfavorable outcomes. Multivariable logistic regression analysis revealed the following significant factors associated with successful TB treatment: sex, nationality, status of drug-resistant TB, category of TB, number of referrals, region of TB registry, underlying diseases, and smoking status. Specifically, compared with their relevant counterparts, male patients had a lower rate of successful treatment (adjusted odds ratio [aOR]: 0.66, reference [Ref.]: women); Korean nationals had a higher rate of treatment success (aOR: 7.20, Ref. foreign residents in Korea); resistant TB was associated with a lower rate of treatment success (aOR: 0.35, Ref.: non-resistant TB status); newly treated patients had a higher rate of treatment success (aOR: 1.75, Ref.: retreatment patient); switching hospitals once (aOR: 1.78), never (aOR: 1.41), or twice (aOR: 1.37) was associated with increased treatment success (Ref.: three or more times); having zero (aOR: 1.45), one (aOR: 1.31), or two (aOR: 1.24) underlying diseases was associated with a higher rate of treatment success (Ref. three or more underlying diseases); and past smokers (aOR: 1.40) and non-smokers (aOR: 1.35) had a higher rate of treatment success (Ref.: current smokers). Our study identified several factors contributing to unfavorable treatment outcomes in tuberculosis patients, including male patients, foreign residents in Korea, drug-resistant TB, retreatment patients, frequent hospital switching, multiple underlying diseases, and current smoking status. These research findings could inform the development of efficient management strategies and policies for improving the treatment success rate among patients with TB.

Endoscopic morphology of gastric MALT lymphoma correlate with API2/MALT1 fusion and predict treatment response after helicobacter pylori eradication

BackgroundThe presence of API2/MALT1 fusion in gastric mucosa-associated lymphoid tissue (MALT) lymphoma predicts poor response to Helicobacter pylori (Hp) eradication therapy. This study aimed to assess the correlation between endoscopic morphology of MALT lymphoma and API2/MALT1 fusion and evaluate treatment response to Hp eradication based on morphological subtypes.MethodsA retrospective review was conducted on patients diagnosed with gastric MALT lymphoma between January 2011 and December 2022. Endoscopic morphology was categorized as superficial, non-superficial, or mixed type. The superficial type was further classified into gastritis superficial lesion and localized superficial lesion based on border clarity. Logistic regression models evaluated the impact of clinical and endoscopic characteristics on anti-Hp therapy effectiveness.ResultsAmong the 114 patients included, 93 (81.6%) were Hp-positive, and API2/MALT1 fusion was detected in 58 (50.9%) cases, The superficial type was the predominate morphology (73/114, 64%). The regular arrangement of collecting venules (RAC) sign was noted in 21 (18.4%) cases. In superficial subtypes, the RAC signs were more frequently observed in localized lesion than gastritis lesion (35.6% vs. 7.1%, p = 0.01). and the superficial localized lesion was more common in individuals with positive API/MALT1 fusion than negative ones (76.9% vs. 44.1%, p = 0.01). Following Hp eradication, the remission rate for localized lesion was 34.3%, significantly lower than for gastritis lesion (66.7%, p = 0.01). Both endoscopic morphology (OR = 0.26, 95% CI 0.09–0.75) and API2-MALT1 fusion (OR = 14.29, 95% CI 4.19–48.67) impacted the efficacy of anti-Hp therapy. However, multivariate analysis identified API2-MALT1 fusion as the only independent predictor of treatment outcome (OR = 12.18, 95% CI 3.49–42.55, p < 0.001).ConclusionGastric MALT lymphomas with superficial-type morphology, particularly those with defined borders resembling early gastric cancer, were associated with API2/MALT1 fusion and a lower remission rate after Hp eradication therapy. This suggests that endoscopic morphology, along with API2/MALT1 fusion status, could help predict the therapeutic response, with API2/MALT1 fusion serving as a critical indicator of treatment resistance.

HLA-DRB1*01 predicts treatment outcome in juvenile idiopathic arthritis: A retrospective-prospective cohort study.

Juvenile idiopathic arthritis (JIA) is the most common chronic inflammatory autoimmune disease in childhood, significantly contributing to both short- and long-term disability. While certain human leukocyte antigen (HLA) class II alleles are known to be associated with specific subgroups of JIA, emerging evidence suggests a strong correlation between these alleles and treatment response. This study involved 143 JIA patients diagnosed according to International League of Associations for Rheumatology criteria. Each patient underwent HLA class II typing, including HLA-B27, as well as tests for rheumatoid factor (RF) and antinuclear antibodies (ANA). Comprehensive rheumatological assessments were conducted at diagnosis, with follow-ups at three and six months post-onset. After six months of methotrexate (MTX) treatment, patients were categorized as responders or non-responders. Responders achieved clinically inactive disease based on the American College of Rheumatology Provisional Criteria for Defining Clinical Inactive Disease and Clinical Remission. Non-responders, who did not reach clinically inactive disease after six months of treatment, required the addition of another non-biological disease-modifying antirheumatic drug (DMARD) or a biological DMARD. Our analysis revealed that the HLA-DRB1*01 allele is a significant prognostic marker for therapeutic response, predicting therapeutic resistance (P=0.01). The most prevalent HLA-DRB1 alleles in the treatment-resistant group were HLA-DRB1*08:11 (11.3%), HLA-DRB1*01:01 (8.5%), HLA-DRB1*01:13, HLA-DRB1*04:11 (7%), HLA-DRB1*08:13, and HLA-DRB1*08:15 (4.2%). These findings highlight the critical role of HLA class II alleles in pediatric rheumatology, particularly in relation to treatment response and disease prognosis. In the era of personalized medicine, understanding the genetic contributions to treatment response and outcomes in JIA patients is essential. A key limitation of this study was the lack of comparison of treatment responses across different JIA subtypes. Future studies should prioritize evaluating MTX efficacy within specific JIA subgroups to enable a more tailored understanding of its effectiveness.

Preoperative Tumor Growth Rate Does Not Predict Overall or Progression-free Survival in Patients With Glioblastoma.

Presurgical tumor volume progression in glioblastoma (GBM) may be a predictor of survival. This study aims to evaluate the potential impact of preoperative tumor growth and other clinical as well as laboratory parameters on overall survival (OS) of GBM patients. We retrospectively analyzed 98 adult patients with GBM who received two magnetic resonance imaging (MRI) scans between 2013 and 2023, before primary surgery and concurrent Stupp chemoradiotherapy. Tumor growth rates were calculated to classify GBM into slower and faster growing categories. Statistical analyses, including Kaplan-Meier and multivariable Cox regression survival analyses, were performed to evaluate the impact of various clinical and treatment-related factors on OS and progression-free survival (PFS). Slower growing tumors had a significantly longer doubling time than faster growing lesions. Univariable analysis showed no significant differences in OS (p=0.12) or PFS (p=0.4) when analyzed according to tumor growth. When stratified by O6-methylguanin-DNA-methyltransferase (MGMT) status, there were still no differences in OS (p=0.14), but in PFS (p=0.009). In the multivariable Cox regression analysis, radiation dose (p=0.02) and the number of adjuvant cycles of temozolomide (TMZ) (p=0.002) were significantly associated with OS. MGMT status (p=0.02) and the number of adjuvant TMZ cycles (p<0.001) were significantly associated with prolonged PFS. Specific volume growth rate (SVGR), patient age, baseline tumor volume, Karnofsky performance status, extent of resection, and total radiation dose were not significantly associated with PFS. SVGR was not significantly associated with OS or PFS. In contrast, MGMT status, radiation dose, and number of adjuvant TMZ cycles were identified as predictors of treatment outcomes. These factors can guide physicians when designing personalized treatment concepts for patients with GBM.

Cachexia and efficiency of trifluridine/thymidine phosphorylase inhibitor + bevacizumab in metastatic colorectal cancer

In later-line treatment of metastatic colorectal cancer (mCRC), there may be large differences in treatment efficacy depending on cancer cachexia. Recently, the cachexia index (CXI), which was calculated from the skeletal muscle mass index (SMI), serum albumin concentration, and neutrophil-to-lymphocyte ratio, was developed to evaluate cancer cachexia. We retrospectively examined the CXI of 80 patients who were treated with trifluridine/thymidine phosphorylase inhibitor (FTD/TPI) + bevacizumab (Bmab) therapy as a later-line treatment for mCRC, and assessed the impact of cancer cachexia on chemotherapeutic efficacy using CXI. Progression-free and overall survival rates were significantly worse in the low CXI group than in the high CXI group, although there were no marked differences in tumor factors, such as the number of metastatic organs or gene mutations, between the two groups. As the cross-sectional area of the iliopsoas muscle was significantly associated with that of the skeletal muscle, the accuracy of the CXI based on the psoas mass index (P-CXI), which is easier to calculate than the SMI, in predicting treatment outcomes was equivalent to that of the CXI based on the SMI (S-CXI). Cancer cachexia is an important factor related to treatment efficacy in later-line treatments, such as FTD/TPI + Bmab therapy.

Optimizing the Prediction of Depression Remission: A Longitudinal Machine Learning Approach.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. Participants (n = 714) from the Genome-Based Therapeutic Drugs for Depression study received escitalopram or nortriptyline over 12 weeks. Remission was defined as scoring ≤ 7 on the Hamilton Rating Scale. Predictors included demographic, clinical, and genetic variables (at 0 weeks) and measures of symptom severity (at 0, 2, 4, and 6 weeks). Longitudinal descriptors extracted with growth curves and topological data analysis were used to inform prediction of remission. Repeated assessments produced gradual and drug-specific improvements in predictive performance. By Week 4, models' discrimination in all samples reached levels that might usefully inform treatment decisions (area under the receiver operating curve (AUC) = 0.777 for nortriptyline; AUC = 0.807 for escitalopram; AUC = 0.794 for combined sample). Decisions around switching or modifying treatments for depression can be informed by repeated symptom assessments collected during treatment, but not until 4 weeks after the start of treatment.

MammaPrint Genomic Assay Providing Prognostic Information in Early Breast Cancer: 10-Year Follow-Up From a Retrospective German Breast Cancer Registry Analysis

Introduction: Gene expression assays, such as the MammaPrint® (Agendia, Amsterdam, the Netherlands) 70-gene signature, are increasingly used by oncologists to understand breast cancer biology and improve treatment planning. This study assesses the utility of MammaPrint genomic risk in predicting treatment outcomes for women with breast cancer in a retrospective German cohort with a 10-year follow-up, treated based on clinicopathological features alone. Methods: The sample set of 117 tumours from the ‘Patients Tumour Bank of Hope’ (PATH) biobank with 10-year follow-up were classified using MammaPrint into high or low risk of distant metastasis. Patients were previously treated according to St. Gallen and Adjuvant! Online high- or low-risk criteria. Statistical analyses compared overall survival (OS) and treatment outcomes between clinical and genomic risk groups. Results: Among the 78 patients with clinically high-risk tumours, 50% (39) were reclassified as MammaPrint low risk. In total, 57.3% (67/117) patients with MammaPrint low-risk tumours demonstrated a significantly higher 10-year OS of 93.4%, irrespective of nodal status, compared to patients with MammaPrint high-risk tumours (71.2%; p=0.001). Chemotherapy improved OS in patients with MammaPrint high-risk tumours by 29.4%, but not for patients with MammaPrint low-risk tumours (p=0.016). Discussion: The findings confirm the prognostic utility of MammaPrint for identifying genomically low-risk patients who may safely omit chemotherapy while suggesting genomically high-risk cases may benefit from chemotherapy. By providing a more precise assessment of cancer risk than traditional clinicopathological methods alone, MammaPrint may help reduce unnecessary treatments and improve long-term quality of life for patients diagnosed with early-stage breast cancer.