Uncovering molecular features driving lung adenocarcinoma heterogeneity in patients who formerly smoked.

BACKGROUND: An increasing proportion of lung adenocarcinoma (LUAD) occurs in patients even after they have stopped smoking. Here, we aimed to determine whether tobacco smoking induced changes across LUADs from patients who formerly smoked correspond to different biological and clinical factors. METHODS: Random forest models (RFs) were trained utilizing a smoking associated signature developed from differentially expressed genes between LUAD patients who had never smoked (NS) or currently smoked (CS) from TCGA (n = 193) and BCCA (n = 69) cohorts. The RFs were subsequently applied to 299 and 131 formerly smoking patients from TCGA and MSKCC cohorts, respectively. FS were RF-classified as either CS-like or NS-like and associations with patient characteristics, biological features, and clinical outcomes were determined. RESULTS: We elucidated a 123 gene signature that robustly classified NS and CS in both RNA-seq (AUC = 0.85) and microarray (AUC = 0.92) validation test sets. The RF classified 213 patients who had formerly smoked as CS-like and 86 as NS-like from the TCGA cohort. CS-like and NS-like status in formerly smoking patients correlated poorly with patient characteristics but had substantially different biological features including tumor mutational burden, number of mutations, mutagenic signatures and immune cell populations. NS-like formerly smoking patients had 17.5 months and 18.6 months longer overall survival than CS-like patients from the TCGA and MSKCC cohorts, respectively. CONCLUSIONS: Patients who had formerly smoked with LUAD harbor heterogeneous tumor biology. These patients can be divided by smoking induced gene expression to inform prognosis and underlying biological characteristics for treatment selection.

Recurrent Intrahepatic Cholangiocarcinoma: A 10-Point Score to Predict Post-Recurrence Survival and Guide Treatment of Recurrence.

INTRODUCTION: Although up to 50-70% of patients with intrahepatic cholangiocarcinoma (ICC) recur following resection, data to predict post-recurrence survival (PRS) and guide treatment of recurrence are limited. METHODS: Patients who underwent resection of ICC between 2000 and 2020 were identified from an international, multi-institutional database. Data on primary disease as well as laboratory and radiologic data on recurrent disease were collected. Factors associated with PRS were examined and a novel scoring system to predict PRS (PRS score) was developed and internally validated. RESULTS: Among 986 individuals who underwent resection for ICC, 588 (59.6%) patients developed recurrence at a median follow up of 20.3 months. Among patients who experienced a recurrence, 97 (16.5%) underwent re-resection/ablation for recurrent ICC; 88 (15.0%) and 403 (68.5%) patients received intra-arterial treatment or systemic chemotherapy/supportive therapy, respectively. Patient American Society of Anesthesiologists (ASA) class > 2 (1 point), primary tumor N1/Nx status (1 point), primary R1 resection margin (1 point), primary tumor G3/G4 grade (1 point), carbohydrate antigen (CA) 19-9 > 37 UI/mL (2 points) at recurrence and carcinoembryonic antigen (CEA) > 5 ng/mL (2 points) at recurrence, as well as recurrent bilateral disease (1 point) and early recurrence (1 point) were included in the PRS score. The PRS score successfully stratified patients relative to PRS and demonstrated strong discriminatory ability (C-index 0.70, 95% confidence interval 0.68-0.72). While a PRS score of 0-3 was associated with a 3-year PRS of 62.5% following resection/ablation for recurrent ICC, a PRS score > 3 was associated with a low 3-year PRS of 35.5% (p = 0.03). CONCLUSIONS: The PRS score demonstrated strong discriminatory ability to predict PRS among patients who had developed recurrence following initial resection of ICC. The PRS score may be a useful tool to guide treatment among patients with recurrent ICC.

Impacts of systemic treatments on health-related quality of life for patients with metastatic colorectal cancer: a systematic review and network meta-analysis.

OBJECTIVE: There is limited evidence of comparative results among different treatments regarding impacts of Health-Related Quality of Life (HRQoL) for patients with metastatic colorectal cancer (mCRC). We aimed to compare efficacy of systemic treatments on HRQoL among patients with mCRC. METHODS: We collected randomized controlled trials (RCTs) reported in English up until July 2023, from databases including PubMed, Embase, Cochrane Library, ClinicalTrials.gov, and prominent conference databases, for this Bayesian network meta-analysis. Phase 2 or 3 trials that evaluated at least two therapeutic regimens were included. Primary outcomes were short-term and long-term mean changes in EORTC QLQ-C30 global health status/quality of life (GHS/QoL) scores. Secondary outcome was mean change in EQ-5D health utility scores. Mean differences (MDs) with 95% confidence intervals (CIs) were used as effect size. Subgroup analysis was performed based on whether patients received systemic treatments before. We conducted various sensitivity analyses, including differentiating between chemotherapy types, and analyzed patient cohorts with non-specified gene expression levels as well as those with target KRAS expression statuses. The current systematic review protocol was registered on PROSPERO (CRD42023453315 and CRD42023420498). RESULTS: Immunotherapy and targeted therapy significantly improved HRQoL over chemotherapy, with MDs of 9.27 (95% CI: 3.96 to 14.6) and 4.04 (95% CI: 0.11 to 7.94), respectively. Monotherapy significantly outperformed both combination therapy (MD 5.71, 95%CI 0.78 to 10.63) and no active treatment (MD 3.7, 95%CI 1.41 to 6.01) regarding GHS/QoL in the short-term. Combining targeted therapy with chemotherapy did not improve HRQoL. Focusing on HRQoL, cetuximab excelled when gene expression baselines were unspecified. Subgroup and sensitivity analyses upheld these robust findings, unaffected by model or patient baseline characteristics. Evidence from clinical trials without specific gene level data suggested that monotherapies, especially targeted therapies such as cetuximab, demonstrated superiority in HRQoL. For KRAS wild-type patients, no significant HRQoL differences emerged between chemotherapy, targeted therapy, or their combination.. CONCLUSIONS: Targeted therapies and immunotherapy demonstrate superior HRQoL benefits, monotherapy such as cetuximab is associated with significant improvements as compared to combination therapy. However, tailoring these results to individual gene expression profiles requires more evidence.

Shared decision-making related to treatment of haemophilia: A scoping review of influential factors and available support tools.

INTRODUCTION: Treatment selection in haemophilia is increasingly challenging given evolving therapeutic options and the need for individualization. Shared decision-making (SDM) approaches have recently gained interest, though a synthesis of available studies is lacking. AIM: A scoping review was conducted to summarize literature reporting on factors impacting treatment SDM in haemophilia and tools or models available to support such decisions. METHODS: PubMed, Embase, the Cochrane Library, Web of Science and grey literature were searched for studies published through August 2023. Original studies reporting on facilitators and barriers to haemophilia SDM and SDM tools were included and analyzed for themes, characteristics and gaps. RESULTS: A total of 625 records were identified and 14 unique studies were selected (factors influencing treatment SDM, n = 7; SDM tools, n = 7). The studies typically included input from persons with haemophilia, caregivers and healthcare practitioners (HCPs). Thematic organization of factors influencing SDM revealed three main categories: knowledge, patient characteristics and HCP-patient interactions. Availability of information was a commonly reported facilitator of SDM, while poor HCP-patient engagement was a commonly reported barrier. Tools varied in focus, with some facilitating general treatment SDM while others supported selection of certain therapy types. The studies underscored additional factors critical for SDM, such as alignment of HCP-patient perceptions, shared language and tailoring of tools to specific subpopulations. CONCLUSION: Few studies report on treatment SDM factors and tools in haemophilia; available tools vary considerably. It remains unclear whether published tools have been successfully implemented into clinical practice. Additional research is warranted.

Classification systems for chronic pelvic pain in males: a systematic review.

OBJECTIVE: To systematically review the classification systems for male chronic pelvic pain (CPP). METHODS: The Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica dataBASE (EMBASE), and Web of Science were searched. Any publication, with no restriction to publication date, was eligible. Publications had to propose a classification system for CPP in males or provide additional information of a system that had been identified. Systems were assessed with an adapted Critical Appraisal of Classification Systems tool. RESULTS: A total of 33 relevant publications were identified, with 22 proposing an original classification system. Systems aimed to: (i) diagnose CPP and/or differentially diagnose CPP from other conditions, (ii) differentially diagnose subtypes within CPP, or (iii) identify features that could inform underlying mechanisms and/or treatment selection. Conditions referred to as chronic prostatitis/chronic pelvic pain syndrome and interstitial cystitis/bladder pain syndrome were most represented. Clinical signs/symptoms, pathoanatomical investigations, and presumed pain mechanisms were used for classification. Quality of systems was low to moderate, implying limitations to consider for their interpretation. CONCLUSIONS: Many classification systems for CPP in males exist. Careful consideration of their intended purpose is required. Future work should examine whether outcomes for patients are improved when decisions are guided by their use.

Multi-arm multi-stage (MAMS) randomised selection designs: impact of treatment selection rules on the operating characteristics.

BACKGROUND: Multi-arm multi-stage (MAMS) randomised trial designs have been proposed to evaluate multiple research questions in the confirmatory setting. In designs with several interventions, such as the 8-arm 3-stage ROSSINI-2 trial for preventing surgical wound infection, there are likely to be strict limits on the number of individuals that can be recruited or the funds available to support the protocol. These limitations may mean that not all research treatments can continue to accrue the required sample size for the definitive analysis of the primary outcome measure at the final stage. In these cases, an additional treatment selection rule can be applied at the early stages of the trial to restrict the maximum number of research arms that can progress to the subsequent stage(s). This article provides guidelines on how to implement treatment selection within the MAMS framework. It explores the impact of treatment selection rules, interim lack-of-benefit stopping boundaries and the timing of treatment selection on the operating characteristics of the MAMS selection design. METHODS: We outline the steps to design a MAMS selection trial. Extensive simulation studies are used to explore the maximum/expected sample sizes, familywise type I error rate (FWER), and overall power of the design under both binding and non-binding interim stopping boundaries for lack-of-benefit. RESULTS: Pre-specification of a treatment selection rule reduces the maximum sample size by approximately 25% in our simulations. The familywise type I error rate of a MAMS selection design is smaller than that of the standard MAMS design with similar design specifications without the additional treatment selection rule. In designs with strict selection rules - for example, when only one research arm is selected from 7 arms - the final stage significance levels can be relaxed for the primary analyses to ensure that the overall type I error for the trial is not underspent. When conducting treatment selection from several treatment arms, it is important to select a large enough subset of research arms (that is, more than one research arm) at early stages to maintain the overall power at the pre-specified level. CONCLUSIONS: Multi-arm multi-stage selection designs gain efficiency over the standard MAMS design by reducing the overall sample size. Diligent pre-specification of the treatment selection rule, final stage significance level and interim stopping boundaries for lack-of-benefit are key to controlling the operating characteristics of a MAMS selection design. We provide guidance on these design features to ensure control of the operating characteristics.

Multicenter validation study of a treatment selection MAP for pancreatic neuroendocrine tumors.

BACKGROUND: Somatostatin analogs, molecular-targeted agents and cytotoxic anticancer agents are available as therapeutic agents for the systemic treatment of pancreatic neuroendocrine tumors, and we have developed a first-line treatment selection MAP to enable selection of the optimal treatment strategy for pancreatic neuroendocrine tumors. The purpose of this study was to validate the usefulness of the treatment selection MAP. METHODS: Patients who had received systemic therapy for a pancreatic neuroendocrine tumor between January 2017 and December 2020 were compared according to whether they had been treated as recommended by the MAP (matched patients) or not (unmatched patients) to determine whether better outcomes were achieved by the matched patients. The primary endpoint was progression-free survival of the matched group and unmatched groups in the somatostatin analog, molecular-targeted agent and cytotoxic anticancer agents areas of the MAP. RESULTS: There were 41 (55%) MAP-matched patients in all areas among the 74 patients registered at seven hospitals. The MAP-matched rates were 100, 77 and 38% in the somatostatin analog area, molecular-targeted agent area and cytotoxic anticancer agents area, respectively. All of the unmatched patients had been selected for less intensive treatment. The median progression-free survival in the matched group and unmatched group in the molecular-targeted agent area of the MAP were 46.6 and 15.4 months, respectively, and a multivariate analysis identified MAP-matched (hazard ratio 0.18 [95% confidence interval: 0.04-0.87], P = 0.032) as the only significant independent favorable predictive factor. CONCLUSION: The usefulness of the MAP for treatment selection was validated in the molecular-targeted agent area of the MAP.

Adaptive Multiple Comparison Sequential Design (AMCSD) for clinical trials.

We propose an adaptive sequential testing procedure for clinical trials that test the efficacy of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in one trial. At any interim analyses, sample size re-estimation can be conducted, and any option can be dropped for lack of efficacy or unsatisfactory safety profile. Inference after the trial, including p-value, conservative point estimate and confidence intervals, are provided.

Adaptive two-stage seamless sequential design for clinical trials.

We propose an adaptive sequential testing procedure for the selection and testing of multiple treatment options, such as dose/regimen, different drugs, sub-populations, endpoints, or a mixture of them in a seamlessly combined phase II/III trial. The selection is to be made at the end of phase 2 stage. Unlike in many of the published literature, the selection rule is not required to be to "select the best", and does not need to be pre-specified, which provides flexibility and allows the trial investigators to use any efficacy and safety information/criteria, or surrogate or intermediate endpoint to make the selection. Sample size and power calculations are provided. The calculations have been confirmed to be accurate by simulations. Interim analysis can be performed after the selection, sample size can be modified if the observed efficacy deviates from the assumed. Inference after the trial, including p-value, median unbiased point estimate and confidence intervals, are provided. By applying a dominance theorem, the procedure can be applied to normal, binary, Poisson, negative binomial distributed endpoints and time-to-event endpoints, and a mixture of these distributions (in trials involving endpoint selection).

Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy.

BACKGROUND: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. METHODS: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. RESULTS: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. CONCLUSIONS: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.

Optimal sample size division in two-stage seamless designs.

Inferentially seamless 2/3 designs are increasingly popular in clinical trials. It is important to understand their relative advantages compared with separate phase 2 and phase 3 trials, and to understand the consequences of design choices such as the proportion of patients included in the phase 2 portion of the design. Extending previous work in this area, we perform a simulation study across multiple numbers of arms and efficacy response curves. We consider a design space crossing the choice of a separate versus seamless design with the choice of allocating 0%-100% of available patients in phase 2, with the remainder in phase 3. The seamless designs achieve greater power than their separate trial counterparts. Importantly, the optimal seamless design is more robust than the optimal separate program, meaning that one range of values for the proportion of patients used in phase 2 (30%-50% of the total phase 2/3 sample size) is nearly optimal for a wide range of response scenarios. In contrast, a percentage of patients used in phase 2 for separate trials may be optimal for some alternative scenarios but decidedly inferior for other alternative scenarios. When operationally and scientifically viable, seamless trials provide superior performance compared with separate phase 2 and phase 3 trials. The results also provide guidance for the implementation of these trials in practice.

Survival benefits of interventional radiology and surgical teams collaboration during primary trauma surveys: a single-centre retrospective cohort study.

BACKGROUND: A team approach is essential for effective trauma management. Close collaboration between interventional radiologists and surgeons during the initial management of trauma patients is important for prompt and accurate trauma care. This study aimed to determine whether trauma patients benefit from close collaboration between interventional radiology (IR) and surgical teams during the primary trauma survey. METHODS: A retrospective observational study was conducted between 2014 and 2021 at a single institution. Patients were assigned to an embolization group (EG), a surgery group (SG), or a combination group (CG) according to their treatment. The primary and secondary outcomes were survival at hospital discharge compared with the probability of survival (Ps) and the time course of treatment. RESULTS: The analysis included 197 patients, consisting of 135 men and 62 women, with a median age of 56 [IQR, 38-72] years and an injury severity score of 20 [10-29]. The EG, SG, and CG included 114, 48, and 35 patients, respectively. Differences in organ injury patterns were observed between the three groups. In-hospital survival rates in all three groups were higher than the Ps. In particular, the survival rate in the CG was 15.5% higher than the Ps (95% CI: 7.5-23.6%; p < 0.001). In the CG, the median time for starting the initial procedure was 53 [37-79] min and the procedure times for IR and surgery were 48 [29-72] min and 63 [35-94] min, respectively. Those times were significantly shorter among three groups. CONCLUSION: Close collaboration between IR and surgical teams, including the primary survey, improves the survival of severe trauma patients who require both IR procedures and surgeries by improving appropriate treatment selection and reducing the time process.

A DNA Damage Response Related Signature to Predict Prognosis in Patients with Acute Myeloid Leukemia.

The prognosis of acute myeloid leukemia (AML) is disappointing in most subtypes and varies widely. DNA damage response (DDR) is associated with prognosis and immunotherapy in multiple cancers. Here, we identify a signature of eight DDR-related genes associated with overall survival, which stratifies AML patients into high- and low-risk groups. Patients in low-risk group were more likely to respond to sorafenib. The signature could be an independent prognostic predictor for patients treated with ADE and ADE plus gemtuzumab ozogamicin. Therefore, this DDR prognostic signature might be applied to prognostic stratification and treatment selection in AML patients, which warrants further studies.

Patient perspectives on considerations, tradeoffs, and experiences with multiple myeloma treatment selection: a qualitative descriptive study.

BACKGROUND: Advances in multiple myeloma treatment and a proliferation of treatment options have resulted in improved survival rates and periods of symptom-free remission for many multiple myeloma patients. As a result, health-related quality of life (HRQoL) concerns related to myeloma treatments have become increasingly salient for this patient population and represent an important consideration guiding patients' treatment choices. To gain an understanding of patients' experiences with choosing myeloma therapies and explore the HRQoL concerns that are most important to them, we interviewed a diverse sample of US-based multiple myeloma patients about their treatment considerations. METHODS: We conducted a qualitative descriptive study using in-depth interviews. Participants reflected on (1) the factors that were most important to them when thinking about multiple myeloma treatment and how these have changed over time, (2) how they might weigh the importance of treatment efficacy vs. side effects, (3) trade-offs they would be willing to make regarding efficacy vs. HRQoL, and (4) treatment changes they had experienced. Interviews were audio-recorded and transcribed, and narratives were analyzed using applied thematic analysis. RESULTS: We interviewed 21 patients, heterogeneous in their disease trajectory and treatment experience. Participants were 36 to 78 years, 52% female, and 38% Black. Efficacy was named as the most important treatment consideration by almost two-thirds of participants, and over half also valued HRQoL aspects such as the ability to maintain daily functioning and enjoyment of life. Participants expressed concern about potential treatment side effects and preferred more convenient treatment options. Although participants stated largely trusting their clinicians' treatment recommendations, many said they would stop a clinician-recommended treatment if it negatively impacted their HRQoL. Participants also said that while they prioritized treatment efficacy, they would be willing to change to a less efficacious treatment if side effects became intolerable. CONCLUSIONS: Our findings link to other reports reflecting considerations that are important to multiple myeloma patients, including the importance placed on increasing life expectancy and progression-free survival, but also the tension between treatment efficacy and quality of life. Our results extend these findings to a racially diverse US-based patient population at different stages in the disease trajectory.

Evaluating a treatment selection approach for online single-session interventions for adolescent depression.

BACKGROUND: The question 'what works for whom' is essential to mental health research, as matching individuals to the treatment best suited to their needs has the potential to maximize the effectiveness of existing approaches. Digitally administered single-session interventions (SSIs) are effective means of reducing depressive symptoms in adolescence, with potential for rapid, large-scale implementation. However, little is known about which SSIs work best for different adolescents. OBJECTIVE: We created and tested a treatment selection algorithm for use with two SSIs targeting depression in high-symptom adolescents from across the United States. METHODS: Using data from a large-scale RCT comparing two evidence-based SSIs (N = 996; ClinicalTrials.gov: NCT04634903), we utilized a Personalized Advantage Index approach to create and evaluate a treatment-matching algorithm for these interventions. The two interventions were Project Personality (PP; N = 482), an intervention teaching that traits and symptoms are malleable (a 'growth mindset'), and the Action Brings Change Project (ABC; N = 514), a behavioral activation intervention. RESULTS: Results indicated no significant difference in 3-month depression outcomes between participants assigned to their matched intervention and those assigned to their nonmatched intervention. The relationship between predicted response to intervention (RTI) and observed RTI was weak for both interventions (r = .39 for PP, r = .24 for ABC). Moreover, the correlation between a participants' predicted RTI for PP and their predicted RTI for ABC was very high (r = .79). CONCLUSIONS: The utility of treatment selection approaches for SSIs targeting adolescent depression appears limited. Results suggest that both (a) predicting RTI for SSIs is relatively challenging, and (b) the factors that predict RTI for SSIs are similar regardless of the content of the intervention. Given their overall effectiveness and their low-intensity, low-cost nature, increasing youths' access to both digital SSIs may carry more public health utility than additional treatment-matching efforts.

Drivers of physician decision-making and patient perspectives across lines of therapy in multiple myeloma in the USA.

Aim: To explore treatment selection for relapsed/refractory multiple myeloma (RRMM), which remains complex due to heterogeneity of available treatments and lack of defined standard of care. Patients & methods: The Adelphi Real World MM Disease Specific Programme surveyed physicians in the USA and their patients with MM to collect real-world data on patterns and perceptions of MM treatment across lines of therapy (LOT). Results: Triplets were the most common regimens across each LOT. Physicians reported efficacy-related factors, health insurance coverage, and clinical guidelines as key determinants of treatment choice regardless of LOT. Patients identified better quality of life as the most important treatment benefit. Conclusion: The DSP RW data highlight drivers of RRMM treatment choice from physicians' and patients' perspectives and need for a more holistic approach to guidelines and clinical trials that encompasses patient perspectives.

Multiple myeloma (MM) is an incurable cancer of white blood cells. Treatments tend to become less effective when taken for long periods of time, meaning that patients often receive several different treatments over the course of their disease. Many different drugs and drug combinations are now available for patients with MM. As there is no standardized approach for the treatment of MM, it can be challenging for physicians to choose between the various complex treatment options for their patients, and the key factors that influence physicians' treatment choices remain unclear. In this study, we used information from a survey of physicians and their patients with MM in the USA to investigate which treatments were used most commonly and how physicians and patients made decisions about their treatment. We found that patients with MM were typically treated with a combination of three drugs which differed between patients. When deciding which treatment to prescribe to their patients, physicians primarily considered factors related to how well a treatment works and, in-turn, prolong a patient's life, but this was also considered by physicians to be an area for improvement. While patients also favored treatments associated with a longer life, they more commonly favored those associated with a better quality of life. These findings show that quality of life is important to patients receiving treatment for MM and should be taken into account by physicians when choosing a treatment for their patients.

Deep learning in digital pathology for personalized treatment plans of cancer patients.

Over the past decade, many new cancer treatments have been developed and made available to patients. However, in most cases, these treatments only benefit a specific subgroup of patients, making the selection of treatment for a specific patient an essential but challenging task for oncologists. Although some biomarkers were found to associate with treatment response, manual assessment is time-consuming and subjective. With the rapid developments and expanded implementation of artificial intelligence (AI) in digital pathology, many biomarkers can be quantified automatically from histopathology images. This approach allows for a more efficient and objective assessment of biomarkers, aiding oncologists in formulating personalized treatment plans for cancer patients. This review presents an overview and summary of the recent studies on biomarker quantification and treatment response prediction using hematoxylin-eosin (H&E) stained pathology images. These studies have shown that an AI-based digital pathology approach can be practical and will become increasingly important in improving the selection of cancer treatments for patients.

Comparison of causal forest and regression-based approaches to evaluate treatment effect heterogeneity: an application for type 2 diabetes precision medicine.

OBJECTIVE: Precision medicine requires reliable identification of variation in patient-level outcomes with different available treatments, often termed treatment effect heterogeneity. We aimed to evaluate the comparative utility of individualized treatment selection strategies based on predicted individual-level treatment effects from a causal forest machine learning algorithm and a penalized regression model. METHODS: Cohort study characterizing individual-level glucose-lowering response (6 month reduction in HbA1c) in people with type 2 diabetes initiating SGLT2-inhibitor or DPP4-inhibitor therapy. Model development set comprised 1,428 participants in the CANTATA-D and CANTATA-D2 randomised clinical trials of SGLT2-inhibitors versus DPP4-inhibitors. For external validation, calibration of observed versus predicted differences in HbA1c in patient strata defined by size of predicted HbA1c benefit was evaluated in 18,741 patients in UK primary care (Clinical Practice Research Datalink). RESULTS: Heterogeneity in treatment effects was detected in clinical trial participants with both approaches (proportion predicted to have a benefit on SGLT2-inhibitor therapy over DPP4-inhibitor therapy: causal forest: 98.6%; penalized regression: 81.7%). In validation, calibration was good with penalized regression but sub-optimal with causal forest. A strata with an HbA1c benefit > 10 mmol/mol with SGLT2-inhibitors (3.7% of patients, observed benefit 11.0 mmol/mol [95%CI 8.0-14.0]) was identified using penalized regression but not causal forest, and a much larger strata with an HbA1c benefit 5-10 mmol with SGLT2-inhibitors was identified with penalized regression (regression: 20.9% of patients, observed benefit 7.8 mmol/mol (95%CI 6.7-8.9); causal forest 11.6%, observed benefit 8.7 mmol/mol (95%CI 7.4-10.1). CONCLUSIONS: Consistent with recent results for outcome prediction with clinical data, when evaluating treatment effect heterogeneity researchers should not rely on causal forest or other similar machine learning algorithms alone, and must compare outputs with standard regression, which in this evaluation was superior.

[Personalized psychotherapy of posttraumatic stress disorder : Overview on the selection of treatment methods and techniques using statistical procedures]. / Personalisierte Psychotherapie der posttraumatischen Belastungsstörung : Übersicht zur Auswahl von Behandlungsmethoden und -techniken mittels statistischer Verfahren.

BACKGROUND: A relevant heterogeneity of treatment effects in posttraumatic stress disorder (PTSD) is discussed with respect to the debate about the necessity of phase-based treatment and in light of the new diagnosis of complex PTSD and has recently been proven; however, there has been little personalization in the treatment of PTSD. This article presents the current state of research on the personalized selection of specific psychotherapeutic methods for the treatment of PTSD based on patient characteristics using statistical methods. METHODS: A systematic literature search was conducted in the PubMed (including Medline), Embase, Web of Science Core Collection, Google Scholar, PsycINFO and PSYNDEX databases to identify clinical trials and reviews examining personalized treatment for PTSD. RESULTS: A total of 13 relevant publications were identified, of which 5 articles were predictor analyses in samples without control conditions and 7 articles showed analyses of randomized controlled trials (RCT) with a post hoc comparison of treatment effects in optimally and nonoptimally assigned patients. In addition, one article was a systematic review on the treatment of patients with comorbid borderline personality order and PTSD. DISCUSSION: The available manuscripts indicate the importance and benefits of personalized treatment in PTSD. The relevant predictor variables identified for personalization should be used as a suggestion to investigate them in future prospective studies.

Selection of healthcare waste management treatment using fuzzy rough numbers and Aczel-Alsina Function.

The COVID-19 pandemic led to an increase in healthcare waste (HCW). HCW management treatment needs to be re-taken into focus to deal with this challenge. In practice, there are several treatments of HCW with their advantages and disadvantages. This study is conducted to select the appropriate treatment for HCW in the Brcko District of Bosnia and Herzegovina. Six HCW management treatments are analyzed and observed through twelve criteria. Ten-level linguistic values were used to bring this evaluation closer to human thinking. A fuzzy rough approach is used to solve the problem of inaccuracy in determining these values. The OPA method from the Bonferroni operator is used to determine the weights of the criteria. The results of the application of this method showed that the criterion Environmental Impact ( C 4 ) received the highest weight, while the criterion Automation Level ( C 8 ) received the lowest value. The ranking of HCW management treatments was performed using MARCOS methods based on the Aczel-Alsina function. The results of this analysis showed that the best-ranked HCW management treatment is microwave (A6) while landfill treatment (A5) is ranked worst. This study has provided a new approach based on fuzzy rough numbers where the Bonferroni function is used to determine the lower and upper limits, while the application of the Aczel-Alsina function reduced the influence of decision-makers on the final decision because this function stabilizes the decision-making process.