Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Radiation enhancement using focussed ultrasound-stimulated microbubbles for breast cancer: A Phase 1 clinical trial.

BACKGROUND: Preclinical studies have demonstrated that tumour cell death can be enhanced 10- to 40-fold when radiotherapy is combined with focussed ultrasound-stimulated microbubble (FUS-MB) treatment. The acoustic exposure of microbubbles (intravascular gas microspheres) within the target volume causes bubble cavitation, which induces perturbation of tumour vasculature and activates endothelial cell apoptotic pathways responsible for the ablative effect of stereotactic body radiotherapy. Subsequent irradiation of a microbubble-sensitised tumour causes rapid increased tumour death. The study here presents the mature safety and efficacy outcomes of magnetic resonance (MR)-guided FUS-MB (MRgFUS-MB) treatment, a radioenhancement therapy for breast cancer. METHODS AND FINDINGS: This prospective, single-center, single-arm Phase 1 clinical trial included patients with stages I-IV breast cancer with in situ tumours for whom breast or chest wall radiotherapy was deemed adequate by a multidisciplinary team (clinicaltrials.gov identifier: NCT04431674). Patients were excluded if they had contraindications for contrast-enhanced MR or microbubble administration. Patients underwent 2 to 3 MRgFUS-MB treatments throughout radiotherapy. An MR-coupled focussed ultrasound device operating at 800 kHz and 570 kPa peak negative pressure was used to sonicate intravenously administrated microbubbles within the MR-guided target volume. The primary outcome was acute toxicity per Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Secondary outcomes were tumour response at 3 months and local control (LC). A total of 21 female patients presenting with 23 primary breast tumours were enrolled and allocated to intervention between August/2020 and November/2022. Three patients subsequently withdrew consent and, therefore, 18 patients with 20 tumours were included in the safety and LC analyses. Two patients died due to progressive metastatic disease before 3 months following treatment completion and were excluded from the tumour response analysis. The prescribed radiation doses were 20 Gy/5 fractions (40%, n = 8/20), 30 to 35 Gy/5 fractions (35%, n = 7/20), 30 to 40 Gy/10 fractions (15%, n = 3/20), and 66 Gy/33 fractions (10%, n = 2/20). The median follow-up was 9 months (range, 0.3 to 29). Radiation dermatitis was the most common acute toxicity (Grade 1 in 16/20, Grade 2 in 1/20, and Grade 3 in 2/20). One patient developed grade 1 allergic reaction possibly related to microbubbles administration. At 3 months, 18 tumours were evaluated for response: 9 exhibited complete response (50%, n = 9/18), 6 partial response (33%, n = 6/18), 2 stable disease (11%, n = 2/18), and 1 progressive disease (6%, n = 1/18). Further follow-up of responses indicated that the 6-, 12-, and 24-month LC rates were 94% (95% confidence interval [CI] [84%, 100%]), 88% (95% CI [75%, 100%]), and 76% (95% CI [54%, 100%]), respectively. The study's limitations include variable tumour sizes and dose fractionation regimens and the anticipated small sample size typical for a Phase 1 clinical trial. CONCLUSIONS: MRgFUS-MB is an innovative radioenhancement therapy associated with a safe profile, potentially promising responses, and durable LC. These results warrant validation in Phase 2 clinical trials. TRIAL REGISTRATION: clinicaltrials.gov, identifier NCT04431674.

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.

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.

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.

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.

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.

Predicting Patterns of Distant Metastasis in Breast Cancer Patients following Local Regional Therapy Using Machine Learning.

Up to 30% of breast cancer (BC) patients will develop distant metastases (DM), for which there is no cure. Here, statistical and machine learning (ML) models were developed to estimate the risk of site-specific DM following local-regional therapy. This retrospective study cohort included 175 patients diagnosed with invasive BC who later developed DM. Clinicopathological information was collected for analysis. Outcome variables were the first site of metastasis (brain, bone or visceral) and the time interval (months) to developing DM. Multivariate statistical analysis and ML-based multivariable gradient boosting machines identified factors associated with these outcomes. Machine learning models predicted the site of DM, demonstrating an area under the curve of 0.74, 0.75, and 0.73 for brain, bone and visceral sites, respectively. Overall, most patients (57%) developed bone metastases, with increased odds associated with estrogen receptor (ER) positivity. Human epidermal growth factor receptor-2 (HER2) positivity and non-anthracycline chemotherapy regimens were associated with a decreased risk of bone DM, while brain metastasis was associated with ER-negativity. Furthermore, non-anthracycline chemotherapy alone was a significant predictor of visceral metastasis. Here, clinicopathologic and treatment variables used in ML prediction models predict the first site of metastasis in BC. Further validation may guide focused patient-specific surveillance practices.

Novel MRI-guided focussed ultrasound stimulated microbubble radiation enhancement treatment for breast cancer.

Preclinical studies have demonstrated focused ultrasound (FUS) stimulated microbubble (MB) rupture leads to the activation of acid sphingomyelinase-ceramide pathway in the endothelial cells. When radiotherapy (RT) is delivered concurrently with FUS-MB, apoptotic pathway leads to increased cell death resulting in potent radiosensitization. Here we report the first human trial of using magnetic resonance imaging (MRI) guided FUS-MB treatment in the treatment of breast malignancies. In the phase 1 prospective interventional study, patients with breast cancer were treated with fractionated RT (5 or 10 fractions) to the disease involving breast or chest wall. FUS-MB treatment was delivered before 1st and 5th fractions of RT (within 1 h). Eight patients with 9 tumours were treated. All 7 evaluable patients with at least 3 months follow-up treated for 8 tumours had a complete response in the treated site. The maximum acute toxicity observed was grade 2 dermatitis in 1 site, and grade 1 in 8 treated sites, at one month post RT, which recovered at 3 months. No RT-related late effect or FUS-MB related toxicity was noted. This study demonstrated safety of combined FUS-MB and RT treatment. Promising response rates suggest potential strong radiosensitization effects of the investigational modality.Trial registration: clinicaltrials.gov, identifier NCT04431674.

Drivers of Emergency Department Use Among Oncology Patients in the Era of Novel Cancer Therapeutics: A Systematic Review.

BACKGROUND: Patients diagnosed with cancer are frequent users of the emergency department (ED). While many visits are unavoidable, a significant portion may be potentially preventable ED visits (PPEDs). Cancer treatments have greatly advanced, whereby patients may present with unique toxicities from targeted therapies and are often living longer with advanced disease. Prior work focused on patients undergoing cytotoxic chemotherapy, and often excluded those on supportive care alone. Other contributors to ED visits in oncology, such as patient-level variables, are less well-established. Finally, prior studies focused on ED diagnoses to describe trends and did not evaluate PPEDs. An updated systematic review was completed to focus on PPEDs, novel cancer therapies, and patient-level variables, including those on supportive care alone. METHODS: Three online databases were used. Included publications were in English, from 2012-2022, with sample sizes of ≥50, and reported predictors of ED presentation or ED diagnoses in oncology. RESULTS: 45 studies were included. Six studies highlighted PPEDs with variable definitions. Common reasons for ED visits included pain (66%) or chemotherapy toxicities (69.1%). PPEDs were most frequent amongst breast cancer patients (13.4%) or patients receiving cytotoxic chemotherapy (20%). Three manuscripts included immunotherapy agents, and only one focused on end-of-life patients. CONCLUSION: This updated systematic review highlights variability in oncology ED visits during the last decade. There is limited work on the concept of PPEDs, patient-level variables and patients on supportive care alone. Overall, pain and chemotherapy toxicities remain key drivers of ED visits in cancer patients. Further work is needed in this realm.

Reply to Trudeau, M.; Fraser, B. The CADTH pCODR Expert Review Committee Process Explained. Comment on "Rayson et al. Access to Neoadjuvant Pertuzumab for HER2 Positive Breast Cancer in Canada: A Dilemma Increasingly Difficult to Explain. Curr. Oncol. 2022, 29, 9891-9895".

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Adherence to NCCN Genetic Testing Guidelines in Pancreatic Cancer and Impact on Treatment.

INTRODUCTION: National Comprehensive Cancer Network (NCCN) 2019 Guidelines recommend universal germline (GL) testing for patients (pts) with pancreatic cancer (PC), given germline mutations (gMut) can occur at a similar rate irrespective of an individual's family history of cancer. Molecular analysis of tumors in those with metastatic disease is also recommended. We aimed to determine rates of genetic testing at our institution, factors associated with testing, and outcomes of those tested. METHODS: Frequency of GL and somatic testing was examined in pts diagnosed with non-endocrine PC, with >2 visits between June 2019 and June 2021 at the Mount Sinai Health System. The clinicopathological variables and treatment outcomes were also recorded. RESULTS: A total of 149 pts met the inclusion criteria. Sixty-six pts (44%) underwent GL testing: 42 (28%) at time of diagnosis with the remainder later in treatment. The rate of GL testing increased every year: 33% (2019), 44% (2020), and 61% (2021). A family history of cancer was the only variable associated with the decision to perform GL testing. Eight pts (12% of pts tested) had pathological gMut: BRCA1 (1), BRCA2 (1), ATM (2), PALB2 (2), NTHL1 (1), both CHEK2 and APC (1). Neither gBRCA pt received a PARP inhibitor, all except one received first-line platinum. Ninety-eight pts (65.7%) had molecular tumor testing (66.7% of patients with metastases). Two pts with BRCA2 somatic mut did not have GL testing. Three pts received targeted therapies. CONCLUSION: Genetic testing based on provider discretion results in low rates of GL testing. Early results of genetic testing can have an impact on treatment decisions and trajectory of disease. Initiatives to increase testing are needed but must be feasible in real-world clinic settings.

Case report: Later onset of NRAS-mutant metastatic melanoma in a patient with a partially-excised giant congenital melanocytic nevus.

Despite recent advances in treatment and surveillance, metastatic melanoma still carries a poor prognosis. Large/giant congenital melanocytic nevi (CMNs) constitute a known risk factor for the condition, with the greatest risk for malignant transformation thought to be during childhood (median age at diagnosis of 3 years in a previous cohort). Herein, we present the case of a 30-year-old male who, after undergoing multiple excision/grafting procedures for a giant CMN as a child, was diagnosed with an NRAS-mutant, MDM2-amplified metastatic melanoma more than 20 years later. Response to ipilimumab/nivolumab immunotherapy, cisplatin/vinblastine/temozolomide chemotherapy, and nivolumab/relatlimab immunotherapy was poor. This case highlights the importance of lifetime monitoring with once-yearly dermatological examination (including lymph node palpation) in large/giant CMN patients, as well as the need for further clinical trials evaluating novel therapies for NRAS-mutant melanoma.

Access to Neoadjuvant Pertuzumab for HER2 Positive Breast Cancer in Canada: A Dilemma Increasingly Difficult to Explain.

The addition of pertuzumab to neoadjuvant trastuzumab and chemotherapy for women with early-stage, high-risk, HER2+ breast cancer has been observed to lead to higher pathologic complete response rates (pCR), and improved event-free survival compared to trastuzumab and chemotherapy alone. Based on available data, neoadjuvant pertuzumab is recommended by ESMO, ASCO, and NICE as well as by a Canadian Consensus Guideline Group. We discuss the implications for Canadian patients with HER2+ early breast cancer due to a second and final negative funding decision by the Canadian Agency for Drugs and Technologies in Health (CADTH) related to neoadjuvant pertuzumab. This decision will have adverse impacts for up to 1 in 6 women receiving neoadjuvant therapy for high-risk HER2+ breast cancer, due to suboptimal pCR rates and higher risks of invasive breast cancer recurrent events, resulting in the need for more toxic adjuvant therapy.

A Fatal Etiology of Splenic Infarction.

Hemophagocytic lymphohistiocytosis (HLH) is a rare systemic inflammatory disorder that is rapidly progressive and carries a poor prognosis. We present a case of HLH caused by undiagnosed B cell lymphoma, presenting initially with splenic infarction. Both HLH and splenic infarction are secondary conditions with a wide range of underlying etiologies. When either condition is identified, a prompt search for the underlying trigger is needed to prevent devastating consequences. We demonstrate the difficulties and barriers that can delay the diagnosis of HLH, and emphasize the importance of early treatment in improving survival rates.

Early Changes in Quantitative Ultrasound Imaging Parameters during Neoadjuvant Chemotherapy to Predict Recurrence in Patients with Locally Advanced Breast Cancer.

BACKGROUND: This study was conducted to explore the use of quantitative ultrasound (QUS) in predicting recurrence for patients with locally advanced breast cancer (LABC) early during neoadjuvant chemotherapy (NAC). METHODS: Eighty-three patients with LABC were scanned with 7 MHz ultrasound before starting NAC (week 0) and during treatment (week 4). Spectral parametric maps were generated corresponding to tumor volume. Twenty-four textural features (QUS-Tex1) were determined from parametric maps acquired using grey-level co-occurrence matrices (GLCM) for each patient, which were further processed to generate 64 texture derivatives (QUS-Tex1-Tex2), leading to a total of 95 features from each time point. Analysis was carried out on week 4 data and compared to baseline (week 0) data. ∆Week 4 data was obtained from the difference in QUS parameters, texture features (QUS-Tex1), and texture derivatives (QUS-Tex1-Tex2) of week 4 data and week 0 data. Patients were divided into two groups: recurrence and non-recurrence. Machine learning algorithms using k-nearest neighbor (k-NN) and support vector machines (SVMs) were used to generate radiomic models. Internal validation was undertaken using leave-one patient out cross-validation method. RESULTS: With a median follow up of 69 months (range 7-118 months), 28 patients had disease recurrence. The k-NN classifier was the best performing algorithm at week 4 with sensitivity, specificity, accuracy, and area under curve (AUC) of 87%, 75%, 81%, and 0.83, respectively. The inclusion of texture derivatives (QUS-Tex1-Tex2) in week 4 QUS data analysis led to the improvement of the classifier performances. The AUC increased from 0.70 (0.59 to 0.79, 95% confidence interval) without texture derivatives to 0.83 (0.73 to 0.92) with texture derivatives. The most relevant features separating the two groups were higher-order texture derivatives obtained from scatterer diameter and acoustic concentration-related parametric images. CONCLUSIONS: This is the first study highlighting the utility of QUS radiomics in the prediction of recurrence during the treatment of LABC. It reflects that the ongoing treatment-related changes can predict clinical outcomes with higher accuracy as compared to pretreatment features alone.

A Canadian national guideline on the neoadjuvant treatment of invasive breast cancer, including patient assessment, systemic therapy, and local management principles.

PURPOSE: The neoadjuvant treatment of breast cancer (NABC) is a rapidly changing area that benefits from guidelines integrating evidence with expert consensus to help direct practice. This can optimize patient outcomes by ensuring the appropriate use of evolving neoadjuvant principles. METHODS: An expert panel formulated evidence-based practice recommendations spanning the entire neoadjuvant breast cancer treatment journey. These were sent for practice-based consensus across Canada using the modified Delphi methodology, through a secure online survey. Final recommendations were graded using the GRADE criteria for guidelines. The evidence was reviewed over the course of guideline development to ensure recommendations remained aligned with current relevant data. RESULTS: Response rate to the online survey was almost 30%; representation was achieved from various medical specialties from both community and academic centres in various Canadian provinces. Two rounds of consensus were required to achieve 80% or higher consensus on 59 final statements. Five additional statements were added to reflect updated evidence but not sent for consensus. CONCLUSIONS: Key highlights of this comprehensive Canadian guideline on NABC include the use of neoadjuvant therapy for early stage triple negative and HER2 positive breast cancer, with subsequent adjuvant treatments for patients with residual disease. The use of molecular signatures, other targeted adjuvant therapies, and optimal response-based local regional management remain actively evolving areas. Many statements had evolving or limited data but still achieved high consensus, demonstrating the utility of such a guideline in helping to unify practice while further evidence evolves in this important area of breast cancer management.

Chronic Pancreatitis Is a Risk Factor for Pancreatic Cancer, and Incidence Increases With Duration of Disease: A Systematic Review and Meta-analysis.

INTRODUCTION: Observational studies have suggested an increased risk of pancreatic ductal adenocarcinoma (PDAC) in patients with acute and chronic pancreatitis. We conducted a systematic review and meta-analysis to evaluate the magnitude of this association and summarize the published epidemiological evidence. METHODS: We searched electronic databases (MEDLINE, Embase, Web of Science, Cochrane, and Scopus) and reference lists until January 18, 2021. Studies reporting quantitative association between pancreatitis and PDAC were included and assessed for eligibility, data abstraction, and risk of bias. Standardized incidence ratios (SIRs) were pooled using the random-effects model. RESULTS: Twenty-five cohort and case-control studies met inclusion criteria. Meta-analysis of 12 chronic pancreatitis (CP) studies demonstrated an increased risk of PDAC in patients with CP (SIR: 22.61, 95% confidence interval [CI]: 14.42-35.44). This elevated risk persisted in subgroup analysis of studies that excluded patients diagnosed with PDAC within 2 years of CP diagnosis (SIR: 21.77, 95% CI: 14.43-32.720). The risk was higher in hereditary pancreatitis (SIR: 63.36, 95% CI: 45.39-88.46). The cumulative incidence rates of PDAC in CP increased with follow-up duration. Limited evidence in acute pancreatitis indicates higher PDAC risk in the subset of patients eventually diagnosed with CP. PDAC seems to be uncommon in patients with autoimmune pancreatitis, with 8 reported cases in 358 patients with autoimmune pancreatitis across 4 studies. DISCUSSION: There is an increased risk of PDAC in patients with CP, and incidence rates increase with CP disease duration. Our results indicate that PDAC surveillance may be considered in individuals with long-standing CP.