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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.

UNASSIGNED: There is limited evidence of comparative results among different treatments for patients with unresectable colorectal liver metastases (CRLM) who have failed at least one line of previous systemic therapy. We aimed to compare the efficacy of systemic treatments among these patients through this investigation.
UNASSIGNED: 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 II or III trials that evaluated at least two therapeutic regimens were included. Primary outcome was overall survival (OS), secondary outcome was progression-free survival (PFS). Hazards ratios (HRs) with 95% confidence intervals (CIs) were used as effect size. Subgroup analysis was performed based on metastatic sites. The current systematic review protocol was registered on PROSPERO (CRD42023420498).
UNASSIGNED: 30 RCTs were included, with a total of 13,511 patients. Compared to chemotherapy, multi-targeted therapy (HR 0.57, 95% CI 0.37-0.87) and targeted therapy plus chemotherapy (HR 0.78, 95% CI 0.67-0.91) show significant advantages. Targeted therapy (HR 0.92, 95% CI 0.54-1.57) and local treatment plus chemotherapy (HR 1.03, 95% CI 0.85-1.23) had comparable performance. For patients with liver metastases, TAS-102 plus bevacizumab, aflibercept plus fluorouracil-based combination chemotherapy (CTFU), and bevacizumab plus capecitabine-based combination chemotherapy (CTCA) showed the best outcomes in terms of OS. Bevacizumab plus intensified CTFU, bevacizumab plus CTCA, and HAI followed by single-agent chemotherapy (SingleCT) performed the best regarding PFS. For patients with liver-limited metastases, aflibercept plus CTFU is the optimal choice in OS. For PFS, the best options were HAI followed by SingleCT, aflibercept plus CTFU, and panitumumab plus CTFU. For patients with multiple-site metastases, the best treatments were TAS-102 plus bevacizumab, bevacizumab plus CTCA, bevacizumab plus CTFU, and aflibercept plus CTFU.
UNASSIGNED: Multi-targeted therapy and targeted therapy plus chemotherapy are the best treatment mechanisms. TAS-102 plus bevacizumab is superior in OS, the combination of anti-VEGF drugs like bevacizumab and aflibercept with standard chemotherapy is the preferred option for CRLM patients.

The introduction of tyrosine kinase inhibitors (TKIs) has transformed the treatment of chronic myeloid leukemia (CML). Each approved TKI has its own risk-benefit profile, and patients have choices across lines of therapy. Identifying the initial and subsequent treatment that will lead to the best possible outcome for individual patients is challenging. In this review, we summarize data for each approved TKI across lines of therapy in patients with CML in chronic phase, highlighting elements of each agent\'s safety and efficacy profile that may impact patient selection, and provide insights into individualized treatment sequencing decision-making aimed at optimizing patient outcomes.

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.

Including poly(ADP-ribose) polymerase (PARP) inhibitors in managing patients with inoperable tumors has significantly improved outcomes. The PARP inhibitors hamper single-strand deoxyribonucleic acid (DNA) repair by trapping poly(ADP-ribose)polymerase (PARP) at sites of DNA damage, forming a non-functional \"PARP enzyme-inhibitor complex\" leading to cell cytotoxicity. The effect is more pronounced in the presence of PARP upregulation and homologous recombination (HR) deficiencies such as breast cancer-associated gene (BRCA1/2). Hence, identifying HR-deficiencies by genomic analysis-for instance, BRCA1/2 used in triple-negative breast cancer-should be a part of the selection process for PARP inhibitor therapy. Published data suggest BRCA1/2 germline mutations do not consistently predict favorable responses to PARP inhibitors, suggesting that other factors beyond tumor mutation status may be at play. A variety of factors, including tumor heterogeneity in PARP expression and intrinsic and/or acquired resistance to PARP inhibitors, may be contributing factors. This justifies the use of an additional tool for appropriate patient selection, which is noninvasive, and capable of assessing whole-body in vivo PARP expression and evaluating PARP inhibitor pharmacokinetics as complementary to the currently available BRCA1/2 analysis. In this review, we discuss [18F]Fluorine PARP inhibitor radiotracers and their potential in the imaging of PARP expression and PARP inhibitor pharmacokinetics. To provide context we also briefly discuss possible causes of PARP inhibitor resistance or ineffectiveness. The discussion focuses on TNBC, which is a tumor type where PARP inhibitors are used as part of the standard-of-care treatment strategy.

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.

In precision medicine, there is much interest in estimating the expected-to-benefit (EB) subset, i.e. the subset of patients who are expected to benefit from a new treatment based on a collection of baseline characteristics. There are many statistical methods for estimating the EB subset, most of which produce a \'point estimate\' without a confidence statement to address uncertainty. Confidence intervals for the EB subset have been defined only recently, and their construction is a new area for methodological research. This article proposes a pseudo-response approach to EB subset estimation and confidence interval construction. Compared to existing methods, the pseudo-response approach allows us to focus on modelling a conditional treatment effect function (as opposed to the conditional mean outcome given treatment and baseline covariates) and is able to incorporate information from baseline covariates that are not involved in defining the EB subset. Simulation results show that incorporating such covariates can improve estimation efficiency and reduce the size of the confidence interval for the EB subset. The methodology is applied to a randomized clinical trial comparing two drugs for treating HIV infection.

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).

BACKGROUND: In patients with relapsing remitting multiple sclerosis (RRMS) on low-efficacy disease modifying therapies (DMT), the optimal strategy on how to escalate treatment once needed, remains unknown.
METHODS: We studied RRMS patients on low-efficacy DMTs listed in the Swiss National Treatment Registry, who underwent escalation to either medium- or high-efficacy DMTs. Propensity score-based matching was applied using 12 clinically relevant variables. Both groups were also separately matched with control subjects who did not escalate therapy. Time to relapse and to disability worsening were evaluated using Cox proportional hazard models.
RESULTS: Of 1037 eligible patients, we 1:1 matched 450 MS patients who switched from low-efficacy to medium-efficacy (n = 225; 76.0% females, aged 42.4 ± 9.9 years [mean ± SD], median EDSS 3.0 [IQR 2-4]) or high-efficacy DMTs (n = 225; 72.4% females, aged 42.2 ± 10.6 years, median EDSS 3.0 [IQR 2-4]). Escalation to high-efficacy DMTs was associated with lower hazards of relapses than medium-efficacy DMTs (HR = 0.67, 95% CI 0.47-0.95, p = .027) or control subjects (HR = 0.61, 95% CI 0.44-0.84, p = .003). By contrast, escalation from low to medium-efficacy DMTs did not alter the hazard for relapses when compared to controls (i.e. patients on low-efficacy DMT who did not escalate DMT during follow-up) CONCLUSION: Our nationwide registry analysis suggests that, once escalation from a low-efficacy DMT is indicated, switching directly to a high-efficacy treatment is superior to a stepwise escalation starting with a moderate-efficacy treatment.