Impact of Location of Residence and Distance to Cancer Centre on Medical Oncology Consultation and Neoadjuvant Chemotherapy for Triple-Negative and HER2-Positive Breast Cancer.

Despite consensus guidelines, most patients with early-stage triple-negative (TN) and HER2-positive (HER2+) breast cancer do not see a medical oncologist prior to surgery and do not receive neoadjuvant chemotherapy (NAC). To understand barriers to care, we aimed to characterize the relationship between geography (region of residence and cancer centre proximity) and receipt of a pre-treatment medical oncology consultation and NAC for patients with TN and HER2+ breast cancer. Using linked administrative datasets in Ontario, Canada, we performed a retrospective population-based analysis of women diagnosed with stage I-III TN or HER2+ breast cancer from 2012 to 2020. The outcomes were a pre-treatment medical oncology consultation and the initiation of NAC. We created choropleth maps to assess the distribution of the outcomes and cancer centres across census divisions. To assess the relationship between distance to the nearest cancer centre and outcomes, we performed multivariable regression analyses adjusted for relevant factors, including tumour extent and nodal status. Of 14,647 patients, 29.9% received a pre-treatment medical oncology consultation and 77.7% received NAC. Mapping demonstrated high interregional variability, ranging across census divisions from 12.5% to 64.3% for medical oncology consultation and from 8.8% to 64.3% for NAC. In the full cohort, compared to a distance of ≤5 km from the nearest cancer centre, only 10-25 km was significantly associated with lower odds of NAC (OR 0.83, 95% CI 0.70-0.99). Greater distances were not associated with pre-treatment medical oncology consultation. The interregional variability in medical oncology consultation and NAC for patients with TN and HER2+ breast cancer suggests that regional and/or provider practice patterns underlie discrepancies in the referral for and receipt of NAC. These findings can inform interventions to improve equitable access to NAC for eligible patients.

Quantitative ultrasound radiomics guided adaptive neoadjuvant chemotherapy in breast cancer: early results from a randomized feasibility study.

Background: In patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy. Methods: The phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination. Results: Between June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation. Conclusion: The study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study. Clinical trial registration: clinicaltrials.gov, ID NCT04050228.

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.

Objective: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Materials and methods: Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Results: Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Conclusion: Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Preferred labels and language to discuss low-risk lesions that may be cancer precursors: A review.

OBJECTIVES: Patients diagnosed with low-risk lesions are confused about whether they have cancer, and experience similar anxiety to those with invasive cancer, which affects quality of life. Current labels for low-risk lesions were chosen by clinicians and lack meaning to patients. METHODS: We reviewed published research on preferred labels and language for low-risk lesions, and the rationale for those preferences. RESULTS: Of 6569 titles screened, we included 13 studies. Among healthy adults with cervix or prostate lesions, use of the term "cancer" rather than "nodule" or "lesion" resulted in greater anxiety, higher perceived disease severity, and selection of more invasive treatment. Physicians asked about removing "carcinoma" from thyroid lesion labels to reduce patient anxiety and discourage over-treatment did not support this change, instead preferring a term that included "neoplasm". CONCLUSIONS: This review revealed a startling paucity of research on preferences for low-risk lesion labels and language, and associated rationale. Future research is needed to understand how to improve communication about low-risk lesions. PRACTICE IMPLICATIONS: To reduce anxiety and improve the overall well-being of patients, it is crucial to gain a deeper understanding of how to improve patient-provider conversations regarding screen-detected lesions with a low risk of developing into invasive cancer.

3D CT Radiomic Analysis Improves Detection of Axillary Lymph Node Metastases Compared to Conventional Features in Patients With Locally Advanced Breast Cancer.

OBJECTIVE: Preoperative detection of axillary lymph node metastases (ALNMs) from breast cancer is suboptimal; however, recent work suggests radiomics may improve detection of ALNMs. This study aims to develop a 3D CT radiomics model to improve detection of ALNMs compared to conventional imaging features in patients with locally advanced breast cancer. METHODS: Retrospective chart review was performed on patients referred to a specialty breast cancer center between 2015 and 2020 with US-guided biopsy-proven ALNMs and pretreatment chest CT. One hundred and twelve patients (224 lymph nodes) met inclusion and exclusion criteria and were assigned to discovery (n = 150 nodes) and testing (n = 74 nodes) cohorts. US-biopsy images were referenced in identifying ALNMs on CT, with contralateral nodes taken as negative controls. Positive and negative nodes were assessed for conventional features of lymphadenopathy as well as for 107 radiomic features extracted following 3D segmentation. Diagnostic performance of individual and combined radiomic features was evaluated. RESULTS: The strongest conventional imaging feature of ALNMs was short axis diameter ≥ 10 mm with a sensitivity of 64%, specificity of 95%, and area under the curve (AUC) of 0.89 (95% CI, 0.84-0.94). Several radiomic features outperformed conventional features, most notably energy, a measure of voxel density magnitude. This feature demonstrated a sensitivity, specificity, and AUC of 91%, 79%, and 0.94 (95% CI, 0.91-0.98) for the discovery cohort. On the testing cohort, energy scored 92%, 81%, and 0.94 (95% CI, 0.89-0.99) for sensitivity, specificity, and AUC, respectively. Combining radiomic features did not improve AUC compared to energy alone (P = .08). CONCLUSION: 3D radiomic analysis represents a promising approach for noninvasive and accurate detection of ALNMs.

Hysterectomy versus chemotherapy for low-risk non-metastatic gestational trophoblastic neoplasia (GTN): A cost-effectiveness analysis.

OBJECTIVE: Determine the cost-effectiveness for hysterectomy versus standard of care single agent chemotherapy for low-risk gestational trophoblastic neoplasia (GTN). METHODS: A cost-effectiveness analysis was conducted comparing single agent chemotherapy with hysterectomy using decision analysis and Markov modeling from a healthcare payer perspective in Canada. The base case was a 40-year-old patient with low-risk non-metastatic GTN that completed childbearing. Outcomes were life years (LYs), quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER), and adjusted 2022 costs (CAD). Discounting was 1.5% annually and the time horizon was the patient's lifetime. Model validation included face validity, deterministic sensitivity analyses, and scenario analysis. RESULTS: Mean costs for chemotherapy and hysterectomy arms were $34,507 and $17,363, respectively, while effectiveness measure were 30.37 QALYs and 31.04 LYs versus 30.14 QALYs and 30.82 Lys, respectively. The ICER was $74,526 (USD $54,516) per QALY. Thresholds favoring hysterectomy effectiveness were 30-day hysterectomy mortality below 0.2% and recurrence risk during surveillance above 9.2% (low-risk) and 33.4% (high-risk). Scenario analyses for Dactinomycin and Methotrexate led to similar results. Sensitivity analysis using tornado analysis found the cost to be most influenced by single agent chemotherapy cost and risk of resistance, number of weeks of chemotherapy, and probability of postoperative mortality. CONCLUSION: Compared to hysterectomy, single agent chemotherapy as a first-line treatment costs $74,526 for each additional QALY gained. Given that this cost falls below the accepted $100,000 willingness-to-pay threshold and waitlist limitations within public healthcare systems, these results support the continued use of chemotherapy as standard of care approach for low-risk GTN.

Neoadjuvant chemotherapy for triple-negative and Her2 +ve breast cancer: striving for the standard of care.

PURPOSE: Neoadjuvant chemotherapy (NAC) for triple-negative (TN) and Her2-positive (HER2) breast cancers is supported by international guidelines as it can decrease extent of surgery, provide prognostic information, and allow response-driven adjuvant therapies. Our goal was to describe practice patterns for patients with TN and HER2-positive breast cancer and identify the factors associated with the receipt of NAC versus surgery as initial treatment. METHODS: A retrospective population-based cohort study of adult women diagnosed with stage I-III TN or HER2-positive breast cancer (2012-2020) in Ontario was completed using linked administrative datasets. The primary outcome was NAC as first treatment. The association between NAC and patient, tumor, and practice-related factors was examined using multivariable logistic regression models. RESULTS: Of 14,653 patients included, 23.9% (n = 3500) underwent NAC as first treatment. Patients who underwent NAC were more likely to be younger and have larger tumors, node-positive disease, and stage 3 disease. Of patients who underwent surgery first, 8.8% were seen by a medical oncologist prior to surgery. On multivariable analysis, increasing tumor size (T2 vs T1/T0: 2.75 (2.31-3.28)) and node-positive (N1 vs N0: OR 3.54 (2.92-4.30)) disease were both associated increased odds of receiving NAC. CONCLUSION: A considerable proportion of patients with TN and HER2-positive breast cancer do not receive NAC as first treatment. Of those, most were not assessed by both a surgeon and medical oncologist prior to initiating therapy. This points toward potential gaps in multidisciplinary assessment and disparities in receipt of guideline-concordant care.

COVID-19 pandemic impact on the potential exacerbation of screening mammography disparities: A population-based study in Ontario, Canada.

Strategies to ramp up breast cancer screening after COVID-19 require data on the influence of the pandemic on groups of women with historically low screening uptake. Using data from Ontario, Canada, our objectives were to 1) quantify the overall pandemic impact on weekly bilateral screening mammography rates (per 100,000) of average-risk women aged 50-74 and 2) examine if COVID-19 has shifted any mammography inequalities according to age, immigration status, rurality, and access to material resources. Using a segmented negative binomial regression model, we estimated the mean change in rate at the start of the pandemic (the week of March 15, 2020) and changes in weekly trend of rates during the pandemic period (March 15-December 26, 2020) compared to the pre-pandemic period (January 3, 2016-March 14, 2020) for all women and for each subgroup. A 3-way interaction term (COVID-19*week*subgroup variable) was added to the model to detect any pandemic impact on screening disparities. Of the 3,481,283 mammograms, 8.6 % (n = 300,064) occurred during the pandemic period. Overall, the mean weekly rate dropped by 93.4 % (95 % CI 91.7 % - 94.8 %) at the beginning of COVID-19, followed by a weekly increase of 8.4 % (95 % CI 7.4 % - 9.4 %) until December 26, 2020. The pandemic did not shift any disparities (all interactions p > 0.05) and that women who were under 60 or over 70, immigrants, or with a limited access to material resources had persistently low screening rate in both periods. Interventions should proactively target these underserved populations with the goals of reducing advanced-stage breast cancer presentations and mortality.

A Feasibility Randomized Controlled Trial of Prehabilitation During Neoadjuvant Chemotherapy for Women with Breast Cancer: A Mixed Methods Study.

BACKGROUND: Limited data exist regarding the role of multimodal prehabilitation during neoadjuvant chemotherapy (NACT) for breast cancer. Determining large trial feasibility and identifying signals of prehabilitation benefit are needed. PATIENTS AND METHODS: We conducted a randomized controlled feasibility trial of multimodal prehabilitation versus usual care during NACT among women diagnosed with non-metastatic breast cancer. Intervention participants received an individualized exercise program, dietetic support, and stress management counseling during NACT. The trial assessed feasibility via rates of recruitment, attrition, adherence, and study-related adverse events. Physical fitness (Six Minute Walk Test, grip strength, anthropometrics) and patient-reported outcomes were assessed at baseline, after NACT completion, and 6 months after surgery as exploratory outcomes, and analyzed using linear mixed effects models. Qualitative data were collected from a subsample to understand feasibility and acceptability of prehabilitation. RESULTS: A total of 72 participants were enrolled from the 123 eligible patients (recruitment rate of 53%). There was a 13% attrition rate and no intervention-related adverse events. Participants in the prehabilitation group had better 6-min walk distance at the post-chemotherapy timepoint [between group difference of 49.43 m, 95% confidence interval (CI) - 118.1, 19.2] and at the post-surgery timepoint (27.3, 95% CI -96.8, 42.2) compared with the control group. Prehabilitation participants reported better quality of life, less fatigue, and improved physical activity levels compared with usual care participants. Interviews revealed that the intervention had a positive impact on the treatment experience. CONCLUSIONS: This study demonstrated feasibility and improvement in physical and psychosocial outcomes. Larger trials assessing intervention efficacy to confirm indications of prehabilitation benefit are warranted.

Factors associated with the melanoma diagnostic interval in Ontario, Canada: a population-based study.

BACKGROUND: Protracted times to diagnosis of cancer can lead to increased patient anxiety, and in some cases, disease progression and worse outcomes. This study assessed the time to diagnosis for melanoma, and its variability, according to patient-, disease-, and system-level factors. METHODS: This is a descriptive, cross-sectional study in Ontario, Canada from 2007-2019. We used administrative health data to measure the diagnostic interval (DI)-and its two subintervals-the primary care subinterval (PCI) and specialist care subinterval (SCI). Multivariable quantile regression was used. RESULTS: There were 33,371 melanoma patients. The median DI was 36 days (interquartile range [IQR]: 8-85 days), median PCI 22 days (IQR: 6-54 days), and median SCI 6 days (IQR: 1-42 days). Increasing comorbidity was associated with increasing DI. Residents in the most deprived neighbourhoods and those in rural areas experienced shorter DIs and PCIs, but no differences in SCI. There was substantial variation in the DI and SCI across health regions, but limited differences in the PCI. Finally, patients with a history of non-melanoma skin cancer, and those previously established with a dermatologist experienced significantly longer DI, PCI, and SCI. DISCUSSION: This study found variability in the melanoma DI, notably by system-level factors.

A priori prediction of breast cancer response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivative and molecular subtype.

The purpose of this study was to investigate the performances of the tumor response prediction prior to neoadjuvant chemotherapy based on quantitative ultrasound, tumour core-margin, texture derivative analyses, and molecular parameters in a large cohort of patients (n = 208) with locally advanced and earlier-stage breast cancer and combined them to best determine tumour responses with machine learning approach. Two multi-features response prediction algorithms using a k-nearest neighbour and support vector machine were developed with leave-one-out and hold-out cross-validation methods to evaluate the performance of the response prediction models. In a leave-one-out approach, the quantitative ultrasound-texture analysis based model attained good classification performance with 80% of accuracy and AUC of 0.83. Including molecular subtype in the model improved the performance to 83% of accuracy and 0.87 of AUC. Due to limited number of samples in the training process, a model developed with a hold-out approach exhibited a slightly higher bias error in classification performance. The most relevant features selected in predicting the response groups are core-to-margin, texture-derivative, and molecular subtype. These results imply that that baseline tumour-margin, texture derivative analysis methods combined with molecular subtype can potentially be used for the prediction of ultimate treatment response in patients prior to neoadjuvant chemotherapy.

Budget impact analysis of molecular subtype profiling in endometrial cancer.

OBJECTIVE: This study evaluated the costs associated with four approaches to classifying endometrial cancer (EC), including histomorphological, histomorphological with ancillary immunohistochemical assays, histomolecular and selective molecular classification. METHODS: Direct costs were determined per EC sample from the hospital's perspective. A budget impact analysis and sensitivity analysis were conducted to estimate the mean, minimum and maximum costs per sample and annual institutional costs in adjusted 2022 Canadian dollars. A provincial cost forecast was projected based on expected 2022 EC biopsies. RESULTS: In 2018, our institution performed 190 EC biopsies. The mean cost per biopsy was $158 ($156-$212) for histomorphological classification, $384 ($360-$514) for histomorphological classification with immunohistochemistry and $1297 ($1265-1833) for histomolecular classification. Total annual institutional cost for histomorphological classification was $29,980 and $72,950 with immunohistochemistry. For histomolecular classification, the first year cost was $246,521, accounting for initial educational learning curve, and $233,461 thereafter, assuming a consistent number of biopsies per year. Targeted implementation of histomolecular classification among high-grade, p53 abnormal and/or MMR-deficient ECs (56% of cases) cost $169,688 in the first year and $162,418 annually thereafter. With a projected 3400 EC biopsies in Ontario in 2022, histomorphological classification would annually cost $537,078 and $1,305,677 with immunohistochemistry. Histomolecular classification would cost $4,410,203 in the first year and $4,176,737 annually once established. Selective molecular classification would lead to a cost of $3,044,178 in the first year and $2,913,443 thereafter. CONCLUSIONS: The study highlights the need for informed decision-making when implementing molecular classification in clinical practice, given the substantial incremental healthcare costs associated with these approaches.

Changes in Health Care Costs, Survival, and Time Toxicity in the Era of Immunotherapy and Targeted Systemic Therapy for Melanoma.

Importance: Melanoma treatment has evolved during the past decade with the adoption of adjuvant and palliative immunotherapy and targeted therapies, with an unclear impact on health care costs and outcomes in routine practice. Objective: To examine changes in health care costs, overall survival (OS), and time toxicity associated with primary treatment of melanoma. Design, Setting, and Participants: This cohort study assessed a longitudinal, propensity score (PS)-matched, retrospective cohort of residents of Ontario, Canada, aged 20 years or older with stages II to IV cutaneous melanoma identified from the Ontario Cancer Registry from January 1, 2018, to March 31, 2019. A historical comparison cohort was identified from a population-based sample of invasive melanoma cases diagnosed from the Ontario Cancer Registry from January 1, 2007, to December 31, 2012. Data analysis was performed from October 17, 2022, to March 13, 2023. Exposures: Era of melanoma diagnosis (2007-2012 vs 2018-2019). Main Outcomes and Measures: The primary outcomes were mean per-capita health care and systemic therapy costs (Canadian dollars) during the first year after melanoma diagnosis, time toxicity (days with physical health care contact) within 1 year of initial treatment, and OS. Standardized differences were used to compare costs and time toxicity. Kaplan-Meier methods and Cox proportional hazards regression were used to compare OS among PS-matched cohorts. Results: A PS-matched cohort of 731 patients (mean [SD] age, 67.9 [14.8] years; 437 [59.8%] male) with melanoma from 2018 to 2019 and 731 patients (mean [SD] age, 67.9 [14.4] years; 440 [60.2%] male) from 2007 to 2012 were evaluated. The 2018 to 2019 patients had greater mean (SD) health care (including systemic therapy) costs compared with the 2007 to 2012 patients ($47 886 [$55 176] vs $33 347 [$31 576]), specifically for stage III ($67 108 [$57 226] vs $46 511 [$30 622]) and stage IV disease ($117 450 [$79 272] vs $47 739 [$37 652]). Mean (SD) systemic therapy costs were greater among 2018 to 2019 patients: stage II ($40 823 [$40 621] vs $10 309 [$12 176]), III ($55 699 [$41 181] vs $9764 [$12 771]), and IV disease ($79 358 [$50 442] vs $9318 [$14 986]). Overall survival was greater for the 2018 to 2019 cohort compared with the 2007 to 2012 cohort (3-year OS: 74.2% [95% CI, 70.8%-77.2%] vs 65.8% [95% CI, 62.2%-69.1%], hazard ratio, 0.72 [95% CI, 0.61-0.85]; P < .001). Time toxicity was similar between eras. Patients with stage IV disease spent more than 1 day per week (>52 days) with physical contact with the health care system by 2018 to 2019 (mean [SD], 58.7 [43.8] vs 44.2 [26.5] days; standardized difference, 0.40; P = .20). Conclusions and Relevance: This cohort study found greater health care costs in the treatment of stages II to IV melanoma and substantial time toxicity for patients with stage IV disease, with improvements in OS associated with the adoption of immunotherapy and targeted therapies. These health system-wide data highlight the trade-off with adoption of new therapies, for which there is a greater economic burden to the health care system and time burden to patients but an associated improvement in survival.

Capturing the True Cost of Breast Cancer Treatment: Molecular Subtype and Stage-Specific per-Case Activity-Based Costing.

BACKGROUND: Breast cancer (BC) treatment is rapidly evolving with new and costly therapeutics. Existing costing models have a limited ability to capture current treatment costs. We used an Activity-Based Costing (ABC) method to determine a per-case cost for BC treatment by stage and molecular subtype. METHODS: ABC was used to proportionally integrate multidisciplinary evidence-based patient and provider treatment options for BC, yielding a per-case cost for the total duration of treatment by stage and molecular subtype. Diagnostic imaging, pathology, surgery, radiation therapy, systemic therapy, inpatient, emergency, home care and palliative care costs were included. RESULTS: BC treatment costs were higher than noted in previous studies and varied widely by molecular subtype. Cost increased exponentially with the stage of disease. The per-case cost for treatment (2023C$) for DCIS was C$ 14,505, and the mean costs for all subtypes were C$ 39,263, C$ 76,446, C$ 97,668 and C$ 370,398 for stage I, II, III and IV BC, respectively. Stage IV costs were as high as C$ 516,415 per case. When weighted by the proportion of molecular subtype in the population, case costs were C$ 31,749, C$ 66,758, C$ 111,368 and C$ 289,598 for stage I, II, III and IV BC, respectively. The magnitude of cost differential was up to 10.9 times for stage IV compared to stage I, 4.4 times for stage III compared to stage I and 35.6 times for stage IV compared to DCIS. CONCLUSION: The cost of BC treatment is rapidly escalating with novel therapies and increasing survival, resulting in an exponential increase in treatment costs for later-stage disease. We provide real-time, case-based costing for BC treatment which will allow for the assessment of health system economic impacts and an accurate understanding of the cost-effectiveness of screening.

Phyllodes Tumors of the Breast: Canadian National Consensus Document Using Modified Delphi Methodology.

PURPOSE: Phyllodes tumors are rare breast neoplasms with limited prospective data to guide treatment, leading to heterogeneous management of this disease. We developed National consensus statements using modified Delphi methodology including patients and practitioners across Canada. METHODS: Statements were developed based on a literature review. Two iterations of surveys were distributed with a planned virtual consensus meeting. Panelists were invited from surgery, radiation oncology, medical oncology, pathology, radiology, and plastic surgery. RESULTS: Twenty-three participants attended the virtual conference. One hundred statements regarding diagnostics, pathology, surgical planning, adjuvant therapies, recurrence, surveillance, and patient support were approved with an a priori defined consensus of ≥ 80%. Two tables on locoregional management were developed and approved. The management of borderline phyllodes tumors was a source of uncertainty, and recommendations reflect the lack of evidence in this rare presentation. CONCLUSION: A consensus document containing all approved statements for the care and management of phyllodes tumors was developed to help guide practice and future research.

Association between the COVID-19 pandemic and first cancer treatment modality: a population-based cohort study.

BACKGROUND: Physicians were directed to prioritize using nonsurgical cancer treatment at the beginning of the COVID-19 pandemic. We sought to quantify the impact of this policy on the modality of first cancer treatment (surgery, chemotherapy, radiotherapy or no treatment). METHODS: In this population-based study using Ontario data from linked administrative databases, we identified adults diagnosed with cancer from January 2016 to November 2020 and their first cancer treatment received within 1 year postdiagnosis. Segmented Poisson regressions were applied to each modality to estimate the change in mean 1-year recipient volume per thousand patients (rate) at the start of the pandemic (the week of Mar. 15, 2020) and change in the weekly trend in rate during the pandemic (Mar. 15, 2020, to Nov. 7, 2020) relative to before the pandemic (Jan. 3, 2016, to Mar. 14, 2020). RESULTS: We included 321 535 people diagnosed with cancer. During the first week of the COVID-19 pandemic, the mean rate of receiving upfront surgery over the next year declined by 9% (rate ratio 0.91, 95% confidence interval [CI] 0.88-0.95), and chemotherapy and radiotherapy rates rose by 30% (rate ratio 1.30, 95% CI 1.23-1.36) and 13% (rate ratio 1.13, 95% CI 1.07-1.19), respectively. Subsequently, the 1-year rate of upfront surgery increased at 0.4% for each week (rate ratio 1.004, 95% CI 1.002-1.006), and chemotherapy and radiotherapy rates decreased by 0.9% (rate ratio 0.991, 95% CI 0.989-0.994) and 0.4% (rate ratio 0.996, 95% CI 0.994-0.998), respectively, per week. Rates of each modality resumed to prepandemic levels at 24-31 weeks into the pandemic. INTERPRETATION: An immediate and sustained increase in use of nonsurgical therapy as the first cancer treatment occurred during the first 8 months of the COVID-19 pandemic in Ontario. Further research is needed to understand the consequences.

Early survival for patients newly diagnosed with cancer during COVID-19 in Ontario, Canada: A population-based cohort study.

BACKGROUND: Little is known about the association between the COVID-19 pandemic and early survival among newly diagnosed cancer patients. METHODS: This retrospective population-based cohort study used linked administrative datasets from Ontario, Canada. Adults (≥18 years) who received a cancer diagnosis between March 15 and December 31, 2020, were included in a pandemic cohort, while those diagnosed during the same dates in 2018/2019 were included in a pre-pandemic cohort. All patients were followed for one full year after the date of diagnosis. Cox proportional hazards regression models were used to assess survival in relation to the pandemic, patient characteristics at diagnosis, and the modality of first cancer treatment as a time-varying covariate. Interaction terms were explored to measure the pandemic association with survival for each cancer type. RESULTS: Among 179,746 patients, 53,387 (29.7%) were in the pandemic cohort and 37,741 (21.0%) died over the first post-diagnosis year. No association between the pandemic and survival was found when adjusting for patient characteristics at diagnosis (HR 0.99 [95% CI 0.96-1.01]), while marginally better survival was found for the pandemic cohort when the modality of treatment was additionally considered (HR 0.97 [95% CI 0.95-0.99]). When examining each cancer type, only a new melanoma diagnosis was associated with a worse survival in the pandemic cohort (HR 1.25 [95% CI 1.05-1.49]). CONCLUSIONS: Among patients able to receive a cancer diagnosis during the pandemic, one-year overall survival was not different than those diagnosed in the previous 2 years. This study highlights the complex nature of the COVID-19 pandemic impact on cancer care.