Optimal Treatment Selection Research Articles

Identification of novel ABCB4 variants and genotype-phenotype correlation in progressive familial intrahepatic cholestasis type 3

Progressive familial intrahepatic cholestasis type 3 (PFIC3) is a severe hepatic disorder characterized by cholestasis. Elucidating the genotype-phenotype correlations and expanding the mutational spectrum of the ABCB4 gene are crucial for enhancing diagnostic accuracy and therapeutic strategies.Clinical and genetic data from 2 original PFIC3 patients from our institution, along with 118 additional cases identified through a comprehensive literature review, were integrated for a comprehensive analysis. The study included statistical analysis of clinical information, genetic analysis, multi-species sequence alignment, protein structure modeling, and pathogenicity assessment. Machine learning techniques were applied to identify genotype-phenotype relationships. We identified three novel ABCB4 mutations: two missense mutations (c.904G > T and c.2493G > C) and one splicing mutation (c.1230 + 1G > A). Homozygous mutations were associated with significantly earlier disease onset compared to compound heterozygous mutations (p < 0.0001). Missense mutations were predominant (76.9%), with Exon 7 being the most frequently affected region. A random forest model indicated that Exon 10 had the highest feature importance score (9.9%). Liver transplantation remains the most effective treatment modality for PFIC3. This investigation broadens the known mutation spectrum of the ABCB4 gene and identifies key variant sites associated with clinical manifestations. These insights lay a foundation for early diagnosis, optimal treatment selection, and further research into PFIC3.

Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis

AbstractThe prevalence of digestive system tumours (DST) poses a significant challenge in the global crusade against cancer. These neoplasms constitute 20% of all documented cancer diagnoses and contribute to 22.5% of cancer‐related fatalities. The accurate diagnosis of DST is paramount for vigilant patient monitoring and the judicious selection of optimal treatments. Addressing this challenge, the authors introduce a novel methodology, denominated as the Multi‐omics Graph Transformer Convolutional Network (MGTCN). This innovative approach aims to discern various DST tumour types and proficiently discern between early‐late stage tumours, ensuring a high degree of accuracy. The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. A rigorous experimental evaluation was undertaken on the DST dataset from The Cancer Genome Atlas to scrutinise the efficacy of the MGTCN model. The outcomes unequivocally underscore the efficiency and precision of MGTCN in diagnosing diverse DST tumour types and successfully discriminating between early‐late stage DST cases. The source code for this groundbreaking study is readily accessible for download at https://github.com/bigone1/MGTCN.

Uterine-Conserving Treatment Options for Atypical Endometrial Hyperplasia and Early Endometrial Cancer.

This review aims to synthesize available literature on uterine-conserving treatment options for atypical endometrial hyperplasia and grade 1 endometrial carcinoma while highlighting remaining unanswered questions. The need for uterine-conserving treatment options for atypical endometrial hyperplasia and grade 1 endometrial carcinoma is growing with the increasing number of cases in younger patients or those who cannot undergo surgery. We reviewed the oncological and reproductive outcomes associated with endocrine therapies used for atypical endometrial hyperplasia and grade 1 endometrial carcinoma. The rising prevalence of delayed childbearing, obesity, and diabetes in reproductive-age individuals and of medical comorbidities associated with high surgical risk continues to amplify the demand for uterine-conserving therapies. Appropriate patient selection for such therapies is imperative to maximize likelihood of treatment response. The ideal candidates are patients with atypical endometrial hyperplasia or early-stage, low-grade endometrial cancer with no evidence of myometrial invasion or extrauterine disease. The most accepted conservative therapeutic approach is hormonal therapy with close surveillance, with or without eventual hysterectomy following childbearing or failure of treatment. Further prospective and randomized trials are needed to address optimal patient and treatment selection, as well as the use of molecular profiling for treatment individualization and prognostication.

Validation of the prognostic index for spine metastasis (PRISM) for stratifying survival in patients treated with spinal stereotactic body radiation

PurposeThe Prognostic Index for Spinal Metastasis (PRISM) is a scoring system derived from prospective data from a single institution that stratifies patients undergoing spine stereotactic radiosurgery (SSRS) for spinal metastases into subgroups by overall (OS). We sought to further demonstrate its generalizability by performing validation with a large dataset from a second high-volume institution, Mayo Clinic. Methods and materialsEight hundred seventy-nine patients—424 from Mayo Clinic and 455 from MD Anderson Cancer Center (MDACC)—who received SSRS between 2007 and 2019 were identified. Patients were stratified by PRISM criteria, and overall survival (OS) for the PRISM groups for each cohort was compared using Kaplan-Meier estimations and univariate Cox proportional analyses. Model calibration and concordance indices (C-indices) were calculated for each cohort to assess the quality of the scoring system. ResultsPatient and tumor characteristics varied significantly between both cohorts including histology, sex, performance status, and number of organs involved (all P < 0.001). Median OS was 30.3 and 22.1 months for the Mayo and MDACC cohorts, respectively. Kaplan-Meier survival curves revealed robust separation between prognostic groups within both cohorts. The Mayo cohort showed median OS of 57.1, 37.0, 23.7, and 8.8 months for Groups 1, 2, 3, and 4, respectively. Univariate analysis revealed hazard ratios of 3.0 (95 % confidence interval [CI], 1.9–4.9), 5.2 (95 % CI, 3.2–8.3), and 12.9 (95 % CI, 7.8–21.4) for groups 2, 3 and 4, respectively all P < 0.001). The C-indices were 0.69 and 0.66 for the unstratified and stratified scores for the Mayo cohort, and 0.70 and 0.68 for the MDACC cohort, respectively. ConclusionThese data demonstrate robust validation of the PRISM score to stratify OS in patients treated with SSRS by a large external cohort, despite substantial differences among the cohorts. Overall, the PRISM scoring may help guide optimal treatment selection for patients with spine metastases.

Advancing gastrointestinal stromal tumor management: The role of imagomics features in precision risk assessment.

Gastrointestinal stromal tumors (GISTs) vary widely in prognosis, and traditional pathological assessments often lack precision in risk stratification. Advanced imaging techniques, especially magnetic resonance imaging (MRI), offer potential improvements. This study investigates how MRI imagomics can enhance risk assessment and support personalized treatment for GIST patients. To assess the effectiveness of MRI imagomics in improving GIST risk stratification, addressing the limitations of traditional pathological assessments. Analyzed clinical and MRI data from 132 GIST patients, categorizing them by tumor specifics and dividing into risk groups. Employed dimension reduction for optimal imagomics feature selection from diffusion-weighted imaging (DWI), T1-weighted imaging (T1WI), and contrast enhanced T1WI with fat saturation (CE-T1WI) fat suppress (fs) sequences. Age, lesion diameter, and mitotic figures significantly correlated with GIST risk, with DWI sequence features like sphericity and regional entropy showing high predictive accuracy. The combined T1WI and CE-T1WI fs model had the best predictive efficacy. In the test group, the DWI sequence model demonstrated an area under the curve (AUC) value of 0.960 with a sensitivity of 80.0% and a specificity of 100.0%. On the other hand, the combined performance of the T1WI and CE-T1WI fs models in the test group was the most robust, exhibiting an AUC value of 0.834, a sensitivity of 70.4%, and a specificity of 85.2%. MRI imagomics, particularly DWI and combined T1WI/CE-T1WI fs models, significantly enhance GIST risk stratification, supporting precise preoperative patient assessment and personalized treatment plans. The clinical implications are profound, enabling more accurate surgical strategy formulation and optimized treatment selection, thereby improving patient outcomes. Future research should focus on multicenter studies to validate these findings, integrate advanced imaging technologies like PET/MRI, and incorporate genetic factors to achieve a more comprehensive risk assessment.

Plasma Cell-Free DNA Concentration and Fragmentomes Predict Neoadjuvant Chemotherapy Response in Cervical Cancer Patients.

Cervical cancer remains one of the most lethal gynecological malignancies. However, biomarkers for more precise patient care are an unmet need. Herein, the concentration of 285 plasma cell-free DNA (cfDNA) samples are analyzed from 84 cervical patients and the clinical significance of cfDNA fragmentomic characteristics across the neoadjuvant chemotherapy (NACT) treatment. Patients with poor NACT response exhibit a significantly greater escalation in cfDNA levels following the initial cycle of treatment, in comparison to patients with a favorable response. Distinctive end motif profiles and promoter coverages of cfDNA in initial plasma are observed between patients with differing NACT responses. Notably, the DNASE1L3 analysis further demonstrates the intrinsic association between cfDNA characteristics and chemotherapy resistance. The cfDNA and motif ratios show a good discriminative capacity for predicting non-responders from responders (area under the curve (AUC) > 0.8). In addition, transcriptional start sites (TSS) coverages around promoters discern the alteration of biological processes associated with chemotherapy resistance and reflect the potential value in predicting chemotherapy response. These findings in predictive biomarkers may optimize treatment selection, minimize unnecessary treatment, and assist in establishing personalized treatment strategies for cervical cancer patients.

Biologics Agents in Periodontal Regeneration:A Review

The field of periodontology and dental implantology has witnessed significant advancements in the use of molecular mediators and biologic agents over the past decade. Periodontal regeneration, which involves the complete restoration of cementum, bone, and connective tissue following removal of epithelial tissues, is considered superior to tissue repair. Recent research has focused on utilizing biologic materials as complementary therapies to enhance regeneration by accelerating wound healing. These biologic agents were investigated using the following search terms: tissue engineering, intercellular signaling molecules, and biological factors. Enamel matrix derivative (EMD), platelet-derived growth factor (PDGF), platelet-rich plasma, bone morphogenetic proteins (BMPs), fibroblast growth factor (FGF), and parathyroid hormone (PTH) have demonstrated the capacity to stimulate both hard and soft tissue regeneration. However, no single biologic agent is universally effective, necessitating a case-by-case evaluation for optimal treatment selection. Currently, EMD and PDGF are the only biologic therapies approved by the FDA for periodontal regeneration, while BMP-2 is indicated for bone augmentation. Due to a lack of FDA approval for periodontal applications, the clinical use of FGF and PTH in this context is not recommended.

Gender medicine: endocrine and neuroendocrine diseases : Implications for surgery and perioperative management

Gender medicine is also becoming increasingly more important in the field of surgery of endocrine and neuroendocrine diseases. Gender differences in the incidence, symptoms and disease progression are common to all (neuro)endocrine diseases. Understanding these special features, which include socioeconomic aspects as well as different anatomical and biological factors, is essential for the selection of optimal diagnostics and treatment but in some cases further scientific research is required. To date, there is apaucity of gender-specific recommendations in established guideline recommendations. There is an enormous potential in all areas of endocrine surgery to take advantage of differences between men and women in the diagnostics, surgical treatment and perioperative management. Individualized approaches could lead to improved surgical outcomes, reduced perioperative complications and improved follow-up.

Collaboration of Surgeons and Speech Pathologists in the Selection of Optimal Treatment; Role of Speech Pathologist and Audiologist in the Interdisciplinary Management of Patients With Clefts.

Children born with cleft lip and/or cleft palate may have problems with feeding, speech, and hearing. Collaboration of surgeons and speech pathologists guide cleft care treatment decisions and vary throughout the cleft timeline. This review aims to discuss the role of the speech pathologist and audiologist in the interdisciplinary management of patients with cleft lip and palate, specifically, how the speech pathologists' findings guide surgical decision-making.

Best practices for achieving consensus in HER2-low expression in breast cancer: current perspectives from practising pathologists.

Human epidermal growth factor receptor 2 (HER2) expression is an important biomarker in breast cancer (BC). Most BC cases categorised as HER2-negative (HER2-) express low levels of HER2 [immunohistochemistry (IHC) 1+ or IHC 2+/in-situ hybridisation not amplified (ISH-)] and represent a clinically relevant therapeutic category that is amenable to targeted therapy using a recently approved HER2-directed antibody-drug conjugate. A group of practising pathologists, with expertise in breast pathology and BC biomarker testing, outline best practices and guidance for achieving consensus in HER2 IHC scoring for BC. The authors describe current knowledge and challenges of IHC testing and scoring of HER2-low expressing BC and provide best practices and guidance for accurate identification of BCs expressing low levels of HER2. These expert pathologists propose an algorithm for assessing HER2 expression with validated IHC assays and incorporate the 2023 American Society of Clinical Oncology and College of American Pathologist guideline update. The authors also provide guidance on when to seek consensus for HER2 IHC scoring, how to incorporate HER2-low into IHC reporting and present examples of HER2 IHC staining, including challenging cases. Awareness of BC cases that are negative for HER protein overexpression/gene amplification and the related clinical relevance for targeted therapy highlight the importance of accurate HER2 IHC scoring for optimal treatment selection.

PANDAS: Twenty-Five Years Later

Background: It has been 25 years since the definition of the concept of Pediatric Autoimmune Neuropsychiatric Disorders Associated with Streptococcal Infections (PANDAS). The abrupt onset of neuropsychiatric symptoms requires a comprehensive differential diagnosis involving complementary tests and optimal treatment selection. Methods: This paper describes aspects related to the diagnosis and clinical management of PANDAS. A clinical perspective is developed starting from an example that meets the Swedo's criteria. Discussion: A comprehensive approach, including interdisciplinary management and urgent evaluation of potential organic causes, is crucial for effective treatment. Treatment decisions should consider severity, symptoms, and available evidence. Collaboration with neuropediatric or neurological services is needed. ASLO/anti-DNase B and 25-OH-Vitamin D tests are valuable for atypical OCD/Tic presentations, always maintaining a broader organic screening.

Multi-parametric atlas of the pre-metastatic liver for prediction of metastatic outcome in early-stage pancreatic cancer.

Metastasis occurs frequently after resection of pancreatic cancer (PaC). In this study, we hypothesized that multi-parametric analysis of pre-metastatic liver biopsies would classify patients according to their metastatic risk, timing and organ site. Liver biopsies obtained during pancreatectomy from 49 patients with localized PaC and 19 control patients with non-cancerous pancreatic lesions were analyzed, combining metabolomic, tissue and single-cell transcriptomics and multiplex imaging approaches. Patients were followed prospectively (median 3 years) and classified into four recurrence groups; early (<6 months after resection) or late (>6 months after resection) liver metastasis (LiM); extrahepatic metastasis (EHM); and disease-free survivors (no evidence of disease (NED)). Overall, PaC livers exhibited signs of augmented inflammation compared to controls. Enrichment of neutrophil extracellular traps (NETs), Ki-67 upregulation and decreased liver creatine significantly distinguished those with future metastasis from NED. Patients with future LiM were characterized by scant T cell lobular infiltration, less steatosis and higher levels of citrullinated H3 compared to patients who developed EHM, who had overexpression of interferon target genes (MX1 and NR1D1) and an increase of CD11B+ natural killer (NK) cells. Upregulation of sortilin-1 and prominent NETs, together with the lack of T cells and a reduction in CD11B+ NK cells, differentiated patients with early-onset LiM from those with late-onset LiM. Liver profiles of NED closely resembled those of controls. Using the above parameters, a machine-learning-based model was developed that successfully predicted the metastatic outcome at the time of surgery with 78% accuracy. Therefore, multi-parametric profiling of liver biopsies at the time of PaC diagnosis may determine metastatic risk and organotropism and guide clinical stratification for optimal treatment selection.

Advancing neoadjuvant therapies in resectable non-small cell lung cancer: implications for novel treatment strategies and biomarker discovery.

The delivery of neoadjuvant and perioperative therapies for non-small cell lung cancer has been radically altered by significant advances and by the incorporation of targeted therapies as well as immune checkpoint inhibitors alone or alongside conventional chemotherapy. This evolution has been particularly notable in the incorporation of immunotherapy and targeted therapy into the treatment of resectable NSCLC, where recent FDA approvals of drugs such as nivolumab and pembrolizumab, in combination with platinum doublet chemotherapy, have led to considerable improvements in pathological complete response rates and the potential for enhanced long-term survival outcomes. This review emphasizes the growing importance of biomarkers in optimizing treatment selection and explores the impact of emerging studies that challenge existing treatment paradigms and investigate novel therapeutic combinations poised to redefine standard of care practices. Furthermore, the discussion extends to the unmet needs within perioperative treatment assessment and prognostication, highlighting the prospective value of biomarkers in evaluating treatment responses and prognosis.

Precision Drug Repurposing: A Deep Learning Toolkit for Identifying 34 Hyperpigmentation-Associated Genes and Optimizing Treatment Selection.

Hyperpigmentation is a skin disorder characterized by a localized darkening of the skin due to increased melanin production. When patients fail first line topical treatments, secondary treatments such as chemical peels and lasers are offered. However, these interventions are not devoid of risks and are associated with postinflammatory hyperpigmentation. In the quest for novel therapeutic potentials, this study aims to investigate computational methods in the identification of new targeted therapies in the treatment of hyperpigmentation. We used a comprehensive approach, which integrated text mining, interpreting gene lists through enrichment analysis and integration of diverse biological information (GeneCodis), protein-protein association networks and functional enrichment analyses (STRING), and plug-in network centrality parameters (Cytoscape) to pinpoint genes closely associated with hyperpigmentation. Subsequently, analysis of drug-gene interactions to identify potential drugs (Cortellis) was utilized to select drugs targeting these identified genes. Lastly, we used Deep Learning Based Drug Repurposing Toolkit (DeepPurpose) to conduct drug-target interaction predictions to ultimately identify candidate drugs with the most promising binding affinities. Thirty-four hyperpigmentation-related genes were identified by text mining. Eight key genes were highlighted by utilizing GeneCodis, STRING, Cytoscape, gene enrichment, and protein-protein interaction analysis. Thirty-five drugs targeting hyperpigmentation-associated genes were identified by Cortellis, and 29 drugs, including 16 M2PK1 inhibitors, 11 KRAS inhibitors, and 2 BRAF inhibitors were recommended by DeepPurpose. The study highlights the promise of advanced computational methodology for identifying potential treatments for hyperpigmentation.

Safety of Antiandrogens for the Treatment of Female Androgenetic Alopecia with Respect to Gynecologic Malignancies.

The most common type of alopecia in women is female androgenetic alopecia (FAGA), characterized by progressive hair loss in a patterned distribution. Many oral therapies, including spironolactone (an aldosterone antagonist), androgen receptor blockers (e.g., flutamide/bicalutamide), 5-alpha-reductase inhibitors (e.g., finasteride/dutasteride), and oral contraceptives, target the mechanism of androgen conversion and binding to its respective receptor and therefore could be administered for the treatment of FAGA. Despite significant advances in the oral treatment of FAGA, its management in patients with a history of gynecological malignancies, the most common cancers in women worldwide, may still be a concern. In this review, we focus on the safety of antiandrogens for the treatment of FAGA patients. For this purpose, a targeted literature review was conducted on PubMed, utilizing the relevant search terms. To sum up, spironolactone seems to be safe for the systemic treatment of FAGA, even in high-risk populations. However, a general uncertainty remains regarding the safety of other medications in patients with a history of gynecologic malignancies, and further studies are needed to evaluate their long-term safety in patients with FAGA and risk factors to establish an optimal risk assessment and treatment selection protocol.

Sodium-glucose cotransporter-2 inhibitors and incidence of atrial fibrillation in older adults with type 2 diabetes: a retrospective cohort analysis

ObjectivesTo investigate the risk of atrial fibrillation (AF) with sodium-glucose cotransporter-2 inhibitors (SGLT2is) compared to dipeptidyl peptidase-4 inhibitor (DPP4i) use in older US adults and across diverse subgroups.MethodsWe conducted a retrospective cohort analysis using claims data from 15% random samples of Medicare fee-for-service beneficiaries. Patients were adults with type 2 diabetes (T2D), no preexisting AF, and were newly initiated on SGLT2i or DPP4i. The outcome was the first incident AF. Inverse probability treatment weighting (IPTW) was used to balance the baseline covariates between the treatment groups including sociodemographics, comorbidities, and co-medications. Cox regression models were used to assess the effect of SGLT2i compared to DPP4i on incident AF.ResultsOf the 97,436 eligible individuals (mean age 71.2 ± 9.8 years, 54.6% women), 1.01% (n = 983) had incident AF over a median follow-up of 361 days. The adjusted incidence rate was 8.39 (95% CI: 6.67–9.99) and 11.70 (95% CI: 10.9–12.55) per 1,000 person-years in the SGLT2i and DPP4i groups, respectively. SGLT2is were associated with a significantly lower risk of incident AF (HR 0.73; 95% CI, 0.57 to 0.91; p = 0.01) than DPP4is. The risk reduction of incident AF was significant in non-Hispanic White individuals and subgroups with existing atherosclerotic cardiovascular diseases and chronic kidney disease.ConclusionCompared to the use of DPP4i, that of SGLT2i was associated with a lower risk of AF in patients with T2D. Our findings contribute to the real-world evidence regarding the effectiveness of SGLT2i in preventing AF and support a tailored therapeutic approach to optimize treatment selection based on individual characteristics.

Preoperative prediction of microvascular invasion classification in hepatocellular carcinoma based on clinical features and MRI parameters.

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is a critical pathological factor and the degree of MVI influences treatment decisions and patient prognosis. The present study aimed to predict the MVI classification based on preoperative MRI features and clinical parameters. The present retrospective cohort study included 150 patients (training cohort, n=108; validation cohort, n=42) with pathologically confirmed HCC. Clinical and imaging characteristics data were collected from Shengli Oilfield Central Hospital (Dongying, China). Univariate and multivariate logistic regression analyses were conducted to assess the association of clinical variables and MRI parameters with MVI (grade M1 and M2) and the M2 classification. Nomograms were developed based on the predictive factors of MVI and the M2 classification. The discrimination capability, calibration and clinical usefulness of the nomograms were evaluated. Multivariate analysis revealed an association between the Lens culinaris agglutinin-reactive fraction of α-fetoprotein, protein induced by vitamin K absence-II and tumor margin and MVI-positive status, while peritumoral enhancement and tumor size were demonstrated to be marginal predictors, but were also included in the nomogram. However, among MVI-positive patients, only peritumoral hypointensity and tumor size were demonstrated to be risk factors for the M2 classification. The nomograms, incorporating these variables, exhibited a strong ability to discriminate between MVI-positive and MVI-negative patients with HCC in both the training and validation cohort [area under the curve (AUC), 0.877 and 0.914, respectively] and good performance in predicting the M2 classification in the training and validation cohorts (AUC, 0.720 and 0.782, respectively). Nomograms incorporating clinical parameters and preoperative MRI features demonstrated promising potential as straightforward and effective tools for predicting MVI and the M2 classification in patients with HCC. Such predictive tools could aid in the judicious selection of optimal clinical treatments.

Abstract PO2-02-11: Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment

Abstract Background: Neoadjuvant therapy (NAT) has become a standard-of-care (SOC) for patients diagnosed with locally advanced or early-stage breast cancer. A critical step towards optimizing treatment selection and coordinating surgical care is to estimate the tumor’s response to NAT. While pathological complete response (pCR) is commonly used to measure NAT effectiveness, patients who do not achieve pCR still benefit from NAT depending on the tumor’s response. A reduction in tumor extent could alter the surgical approach from mastectomy to breast conserving therapy and improve recurrence and overall survival. In this study we used pre-treatment imaging data to provide a comprehensive prediction of clinically relevant metrics including tumor volume, tumor convex hull volume, and spatially derived metrics; this research represents an important advancement in predicting NAT effectiveness. Methods: 262 breast cancer patients from 5 institutions who underwent NAT with pre- and post-NAT dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were included in this study. Each MRI was automatically segmented using a convolutional neural network with manual edits and verification. Radiomic features were extracted from the pre-NAT MRI and tumor segmentation. Significant radiomic features were selected based on their correlation with post-NAT volume, feature-to-feature correlation, and LASSO regression. Altogether, 12 and 17 radiomic features were selected for volume and convex hull volume prediction, respectively. The selected features were inputted into a Huber loss regression model to predict either the post-NAT tumor volume or the convex hull volume (surgical excision volume). To enable spatial and morphological comparison between the predicted and post-NAT tumor, an erosion model was developed in which the pre-NAT and post-NAT MRIs were co-registered, and the pre-NAT tumor was symmetrically eroded until its volume matched the predicted volume. Clinically relevant metrics such as the distance from the tumor to the nipple, skin, and chest were computed to assess the morphological and spatial prediction capabilities of the model. All models were tested on patients from cohort holdout sets (n=110) unseen during training. Results: The volume prediction analysis yielded promising results, with an R^2 value of 0.62 for post-NAT tumor volume prediction and an R^2 value of 0.56 for post-NAT tumor convex hull volume prediction. The mean volume and convex hull volume error was 1.1 cc and 1.3 cc, respectively. Table 1 provides an overview of the morphological and spatial comparisons between the predicted and post-NAT tumors. Overall, the predicted tumors demonstrated close agreement with their post-NAT counterparts. Conclusion: This study demonstrates a successful approach towards estimating post-NAT tumor volume, convex hull volume, and morphological and spatial characteristics. By providing reliable estimates of post-NAT tumor characteristics using SOC pre-NAT data, our predictive models enable personalized treatment planning and patient stratification to optimize patient care. Table 1. Predicted vs. post-NAT tumor comparison. Results demonstrate accurate predictions for clinically relevant distance metrics. Citation Format: Bradley Feiger, John Pfeiffer, John Cole, Anuja Antony, Joseph Peterson. Radiomics and an Erosion Model to Predict Post-Neoadjuvant Therapy Tumor Characteristics for Personalized Breast Cancer Treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-02-11.

A Comprehensive Review of Taxane Treatment in Breast Cancer: Clinical Perspectives and Toxicity Profiles.

Taxanes, such as paclitaxel and docetaxel, have transformed the landscape of breast cancer treatment, playing pivotal roles in chemotherapy protocols for both early-stage and advanced/metastatic diseases. While these agents have demonstrated remarkable efficacy in enhancing patient outcomes, they are also linked to a range of adverse effects that can impact treatment tolerability and quality of life. This comprehensive review offers an in-depth exploration of taxane therapy in breast cancer, with a focus on clinical perspectives and toxicity profiles. We delineate the mechanisms of action of taxanes, their clinical effectiveness across various breast cancer subtypes, and the prevalent adverse effects encountered in clinical practice. Moreover, we deliberate on strategies for mitigating taxane-associated toxicity and optimizing treatment selection and sequencing based on individual patient characteristics and therapeutic objectives. Finally, we underscore areas for future research and advancement, encompassing the development of novel formulations, the identification of predictive biomarkers for treatment response, and the exploration of combination therapies to bolster therapeutic outcomes. By amalgamating existing evidence and clinical insights, this review aims to apprise clinicians and researchers of the current status of taxane treatment in breast cancer and steer endeavors towardfurther enhancing patient care and outcomes.

Impact of Gram-Negative Bacilli Resistance Rates on Risk of Death in Septic Shock and Pneumonia.

Sepsis is a major cause of morbidity and mortality worldwide. When selecting empiric antibiotics for sepsis, clinicians are encouraged to use local resistance rates, but their impact on individual outcomes is unknown. Improved methods to predict outcomes are needed to optimize treatment selection and improve antibiotic stewardship. We expanded on a previously developed theoretical model to estimate the excess risk of death in gram-negative bacilli (GNB) sepsis due to discordant antibiotics using 3 factors: the prevalence of GNB in sepsis, the rate of antibiotic resistance in GNB, and the mortality difference between discordant and concordant antibiotic treatments. We focused on ceftriaxone, cefepime, and meropenem as the anti-GNB treatment backbone in sepsis, pneumonia, and urinary tract infections. We analyzed both publicly available data and data from a large urban hospital. Publicly available data were weighted toward culture-positive cases. Excess risk of death with discordant antibiotics was highest in septic shock and pneumonia. In septic shock, excess risk of death was 4.53% (95% confidence interval [CI], 4.04%-5.01%), 0.6% (95% CI, .55%-.66%), and 0.19% (95% CI, .16%-.21%) when considering resistance to ceftriaxone, cefepime, and meropenem, respectively. Results were similar in pneumonia. Local data, which included culture-negative cases, showed an excess risk of death in septic shock of 0.75% (95% CI, .57%-.93%) for treatment with discordant antibiotics in ceftriaxone-resistant infections and 0.18% (95% CI, .16%-.21%) for cefepime-resistant infections. Estimating the excess risk of death for specific sepsis phenotypes in the context of local resistance rates, rather than relying on population resistance data, may be more informative in deciding empiric antibiotics in GNB infections.