The Oncologist's Guide to Synoptic Reporting: A Primer

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Synoptic operative reports for pediatric surgical oncology

Synoptic operative notes for pediatric surgical oncology provide standardized and structured documentation of surgical procedures performed on pediatric patients with cancer. These reports capture essential details such as preoperative diagnosis, intraoperative findings, surgical technique, and tumor characteristics in a concise and uniform format. By promoting consistency, accuracy, and completeness in reporting, synoptic operative notes facilitate effective communication among multidisciplinary healthcare teams, enhance quality assurance efforts, and streamline data extraction for research purposes. The integration of synoptic reporting within electronic medical record systems further enhances accessibility and usability, ensuring efficient documentation practices and improved patient care outcomes in pediatric surgical oncology.

Standardized Protocols for Clinical and Histopathological Characterization of Hidradenitis Suppurativa Tissue Specimens

Methods for describing and reporting the clinical and histologic characteristics of cutaneous tissue samples from patients with hidradenitis suppurativa (HS) are not currently standardized, limiting clinicians' and scientists' ability to uniformly record, report, and communicate about the characteristics of tissue used in translational experiments. A recently published consensus statement outlined morphological definitions of typical HS lesions, but no consensus has been reached regarding clinical characterization and examination of HS tissue samples. In this study, we aimed to establish a protocol for reporting histopathologic and clinical characteristics of HS tissue specimens. This study was conducted from May 2023 to August 2023. Experts in clinical care, dermatopathology, and translational research were recruited, and a modified Delphi technique was used to develop a protocol for histologic reporting and clinical characterization of submitted tissue specimens from patients with HS. A total of 27 experts participated (14 dermatologists, 3 fellowship-trained dermatopathologists, 3 plastic surgeons, 3 general surgeons, and 4 research scientists) in creating and reviewing protocols for the clinical and histopathological examination of HS tissue specimens. The protocols were formatted as a synoptic report and will help to consistently classify specimens in biobanks on the basis of histologic features and more accurately report and select samples used in translational research projects.

Synoptic Reporting of Focal Liver Masses in at Risk Patients: Algorithmic Diagnosis and CEUS LI-RADS

OBJECTIVES

Reporting contrast-enhanced ultrasound (CEUS) for focal liver masses in at risk patients is a challenging task. Traditionally used prose reporting (PR) is inconsistent and lacks standardization. We propose synoptic reporting (SR), encompassing algorithmic interpretation and liver imaging and reporting data system (LI-RADS) categorization.

METHODS

A software worksheet from Kailo Medical (Melbourne, AU), incorporates the CEUS algorithm for liver interpretation and CEUS LI-RADS categorization. Part 1. Feasibility of SR: twenty participants of varying experience were presented a brief lecture on SR, algorithmic approach to liver mass interpretation, and CEUS LI-RADS categorization. Ten representative liver masses were shown as unknown cases. Participants inputted data into SR worksheets. Results and LI-RADS category were generated solely by SR. Data were categorized as "correct" or "incorrect." Part 2. Prospective Analysis: Ninety-one patients for SR and 56 for PR, all were tested for completeness, efficiency, and user satisfaction.

RESULTS

Part 1: Junior participants, pass rate 81.6%, and senior participants, pass rate 83.3% showed no difference in performance. Part 2: Completeness: SR 98.4% and PR 87.0%. Efficiency: Average total time to completion: SR 11 minutes and PR 20 minutes. User satisfaction: Ultrasound technologists, all referring physicians, and six out of seven radiologists preferred SR over PR. Major benefits cited were total time saved, consistency and accuracy in documentation, and report completeness.

CONCLUSIONS

SR is a reliable and useful tool in clinical practice to report liver masses on ultrasound and assign an appropriate LI-RADS categorization and management pathway. This ultimately improves communication with referring clinicians and leads to better patient outcomes.

Emerging Landscape of Targeted Therapy of Breast Cancers With Low Human Epidermal Growth Factor Receptor 2 Protein Expression

CONTEXT.—

Human epidermal growth factor receptor 2 (HER2) status in breast cancer is currently classified as negative or positive for selecting patients for anti-HER2 targeted therapy. The evolution of the HER2 status has included a new HER2-low category defined as an HER2 immunohistochemistry score of 1+ or 2+ without gene amplification. This new category opens the door to a targetable HER2-low breast cancer population for which new treatments may be effective.

OBJECTIVE.—

To review the current literature on the emerging category of breast cancers with low HER2 protein expression, including the clinical, histopathologic, and molecular features, and outline the clinical trials and best practice recommendations for identifying HER2-low-expressing breast cancers by immunohistochemistry.

DATA SOURCES.—

We conducted a literature review based on peer-reviewed original articles, review articles, regulatory communications, ongoing and past clinical trials identified through ClinicalTrials.gov, and the authors' practice experience.

CONCLUSIONS.—

The availability of new targeted therapy potentially effective for patients with breast cancers with low HER2 protein expression requires multidisciplinary recognition. In particular, pathologists need to recognize and identify this category to allow the optimal selection of patients for targeted therapy.

Impact of template-based synoptic reporting on completeness of surgical pathology reports

Synoptic reporting increases completeness and standardization of surgical pathology reports and thereby contributes to an increased quality of clinical cancer care. Nevertheless, its widespread practical implementation remains a challenge, which is in part related to the effort required for setup and maintenance of database structures. This prompted us to assess the effect of a simple template-based, database-free system for synoptic reporting on completeness of surgical pathology reports. For this purpose, we analyzed 200 synoptic reports (100 colon and 100 lung cancer resections each) for completeness as required by the pertinent College of American Pathologists (CAP) protocols and compared these to a control dataset of 200 narrative reports. Introduction of template-based synoptic reporting resulted in improved completeness (98% of mandatory data elements) as compared to narrative reports (77%). Narrative reports showed a high degree of completeness for data elements covered by previously existing dictation templates. In conclusion, template-based synoptic reporting without underlying database structure can be a useful transitory phase in the implementation of synoptic reporting. It can result in a similar degree of completeness as reported in the literature for database solutions and provides other benefits of synoptic reporting while facilitating its implementation.

Synoptic Reporting for Pretreatment CT Examination in Patients With Advanced Ovarian Cancer: Impact on Documentation of Disease Sites and Physician Satisfaction

BACKGROUND. Imaging reports that consistently document all disease sites with a potential to increase surgical complexity or morbidity can facilitate ovarian cancer treatment planning. OBJECTIVE. The aims of this study were to compare simple structured reports and synoptic reports from pretreatment CT examinations in patients with advanced ovarian cancer in terms of completeness of documenting involvement of clinically relevant anatomic sites as well as to evaluate physician satisfaction with synoptic reports. METHODS. This retrospective study included 205 patients (median age, 65 years) who underwent contrast-enhanced abdominopelvic CT before primary treatment of advanced ovarian cancer from June 1, 2018, to January 31, 2022. A total of 128 reports generated on or before March 31, 2020, used a simple structured report (free text organized into sections); 77 reports generated on or after April 1, 2020, used a synoptic report (a list of 45 anatomic sites relevant to ovarian cancer management, each of which was classified in terms of disease absence versus presence). Reports were reviewed for completeness of documentation of involvement of the 45 sites. For patients who underwent neoadjuvant chemotherapy based on diagnostic laparoscopy findings or underwent primary debulking surgery with suboptimal resection, the EMR was reviewed to identify surgically established sites of disease that were unresectable or challenging to resect. Gynecologic oncology surgeons were electronically surveyed. RESULTS. The mean report turnaround time was 29.8 minutes for simple structured reports versus 54.5 minutes for synoptic reports (p < .001). A mean of 17.6 of 45 sites (range, four to 43 sites) were mentioned by simple structured reports versus 44.5 of 45 sites (range, 39-45) for synoptic reports (p < .001). Forty-three patients had surgically established unresectable or challenging-to-resect disease; involvement of anatomic site(s) with such disease was mentioned in 37% (11/30) of simple structured reports versus 100% (13/13) of synoptic reports (p < .001). All eight surveyed gynecologic oncology surgeons completed the survey. CONCLUSION. A synoptic report improved completeness of pretreatment CT reports in patients with advanced ovarian cancer, including for established sites of unresectable or challenging-to-resect disease. CLINICAL IMPACT. The findings indicate the role of disease-specific synoptic reports in facilitating referrer communication and potentially guiding clinical decision-making.

Real-World Evidence Studies in Oncology Therapeutics: Hope or Hype?

Randomized controlled trial (RCT) remains a gold standard in evidence-based medicine for assessing the efficacy and safety of cancer therapies. However, due to some inherent methodological limitations of RCT, such as stringent inclusion criteria, highly specific treatment, ethical and scientific compromise in rare cancer, and inability to adequately assess safety, real-world evidence (RWE) has been adjudged as a suitable option to complement data obtained from RCT. Moreover, in the context of cancer therapeutics, few notable merits pertain to developing a novel product for rare cancer subtypes, establishing new indications for already approved drugs, optimization of treatment regimen and sequence, a better description of long-term safety, and supporting the reimbursement-related decision. However, the implementation of RWE for the aforementioned purposes will be limited by various challenges, especially in the context of developing economies such as India. Special attention should be given to the availability of data, maintaining the quality standard, and establishing stringent regulations for privacy and security along with active regulatory engagement with relevant stakeholders. Such activities will be key to facilitating the use of RWE in cancer therapeutics.

Natural Language Processing to extract SNOMED-CT codes from pathological reports

Objective

The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department.

Methods

Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports.

Results

The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance.

Conclusions

AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.

Implementing structured pathology reporting protocol for non-melanocytic skin cancers: practical considerations

Non-melanocytic skin cancers (NMSCs) account for five times the incidence of all other cancers combined and cost US $6 billion annually. These are the most frequent specimens encountered in community pathology practice in many Western countries. Lack of standardised structured pathology reporting protocols (SPRPs) can result in omission of critical information or miscommunication leading to suboptimal patient management. The lack of standardised data has significant downstream public health implications, including insufficient data for reliable development of prognostic tools and health-economy planning. The Royal College of Pathologists of Australasia has developed an NMSC SPRP. A multidisciplinary expert committee including pathologists, surgeons, dermatologists, and radiation and medical oncologists from high volume cancer centres was convened. A systematic literature review was performed to identify evidence for including elements as mandatory standards or best practice guidelines. The SPRP and accompanying commentary of evidence, definitions and criteria was peer reviewed by external stakeholders. Finally, the protocol was revised following feedback and trialled in multiple centres prior to implementation. Some parameters utilised clinically for determining management and prognosis including tumour depth, lymphovascular invasion or distance to the margins lack high level evidence in NMSC. Dermatologists, surgeons, and radiation oncologists welcomed the SPRP. Pathologists indicated that the variety of NMSC specimens ranging from curettes to radical resections as well as significant differences in the biological behaviour of different tumours covered by the NMSC umbrella made use of a single protocol difficult. The feedback included that using a SPRP for low risk NMSC was neither clinically justified nor compensated adequately by the Australian Medicare Reimbursement Schedule. Following stakeholder feedback, the SPRP implementation was restricted to excision specimens of head and neck NMSC; and low-risk NMSC, such as superficial basal cell carcinoma, were excluded. Implementing NMSC SPRP fulfils an unmet clinical need. Unlike other cancers, NMSCs generate a range of specimen types and are reported in a wide range of pathology practices. Limiting use of SPRP to NMSC at higher risk of progression and providing formatted templates for easy incorporation into laboratory information systems were essential to successful deployment. In the future, further consideration should be given to implementing the SPRP to include all relevant specimens, including non-head and neck and low-risk NMSC specimens.

Synoptic Reporting in Clinical Placental Pathology: A Preliminary Investigation Into Report Findings and Interobserver Agreement

INTRODUCTION

Placental pathology is key for investigating adverse pregnancy outcomes, however, lack of standardization in reporting has limited clinical utility. We evaluated a novel placental pathology synoptic report, comparing its robustness to narrative reports, and assessed interobserver agreement.

METHODS

100 singleton placentas were included. Histology slides were examined by 2 senior perinatal pathologists and 2 pathology residents using a synoptic report (32 lesions). Historical narrative reports were compared to synoptic reports. Kappa scores were calculated for interobserver agreement between senior, resident, and senior vs resident pathologists.

RESULTS

Synoptic reporting detected 169 (51.4%) lesion instances initially not included in historical reports. Amongst senior pathologists, 64% of all lesions examined demonstrated fair-to-excellent agreement (Kappa ≥0.41), with only 26% of Kappas ≥0.41 amongst those examined by resident pathologists. Well-characterized lesions (e.g., chorioamnionitis) demonstrated higher agreement, with lower agreement for uncommon lesions and those previously shown to have poor consensus.

DISCUSSION

Synoptic reporting is one proposed method to address issues in placenta pathology reporting. The synoptic report generally identifies more lesions compared to the narrative report, however clinical significance remains unclear. Interobserver agreement is likely related to differential in experience. Further efforts to improve overall standardization of placenta pathology reporting are needed.

Applied machine learning in hematopathology

An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.

Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations

The digitalization of clinical workflows and the increasing performance of deep learning algorithms are paving the way towards new methods for tackling cancer diagnosis. However, the availability of medical specialists to annotate digitized images and free-text diagnostic reports does not scale with the need for large datasets required to train robust computer-aided diagnosis methods that can target the high variability of clinical cases and data produced. This work proposes and evaluates an approach to eliminate the need for manual annotations to train computer-aided diagnosis tools in digital pathology. The approach includes two components, to automatically extract semantically meaningful concepts from diagnostic reports and use them as weak labels to train convolutional neural networks (CNNs) for histopathology diagnosis. The approach is trained (through 10-fold cross-validation) on 3’769 clinical images and reports, provided by two hospitals and tested on over 11’000 images from private and publicly available datasets. The CNN, trained with automatically generated labels, is compared with the same architecture trained with manual labels. Results show that combining text analysis and end-to-end deep neural networks allows building computer-aided diagnosis tools that reach solid performance (micro-accuracy = 0.908 at image-level) based only on existing clinical data without the need for manual annotations.

Pathological Reporting of Radical Prostatectomy Specimens Following ICCR Recommendation: Impact of Electronic Reporting Tool Implementation on Quality and Interdisciplinary Communication in a Large University Hospital

Prostate cancer represents one of the most common malignant tumors in male patients in Germany. The pathological reporting of radical prostatectomy specimens following a structured process constitutes an excellent prototype for the introduction of software-based standardized structured reporting in pathology. This can lead to reports of higher quality and could create a fundamental improvement for future AI applications. A software-based reporting template was used to generate standardized structured pathological reports of radical prostatectomy specimens of patients treated at the University Hospital Klinikum rechts der Isar of Technische Universität München, Germany. Narrative reports (NR) and standardized structured reports (SSR) were analyzed with regard to completeness, and clinicians' satisfaction with each report type was evaluated. SSR show considerably higher completeness than NR. A total of 10 categories out of 32 were significantly more complete in SSR than in NR (p < 0.05). Clinicians awarded overall high scores in NR and SSR reports. One rater acknowledged a significantly higher level of clarity and time saving when comparing SSR to NR. Our findings highlight that the standardized structured reporting of radical prostatectomy specimens, qualifying as level 5 reports, significantly increases objectively measured content quality and the level of completeness. The implementation of nationwide SSR in Germany, particularly in oncologic pathology, can serve pathologists, clinicians, and patients.

Histopathology reporting for personalised medicine: focus on clinical utility

Histopathology guidelines generally focus on standardised collection of data items to facilitate completeness and reproducibility of histopathology reporting. A data item is categorised as either core (mandatory) or non-core (recommended but not mandatory), irrespective of the clinical scenario. However, a data item that is critical for patient management in one clinical setting may have little clinical significance in another setting. A diagnosis of limited extent Gleason score 3+3=6 prostate cancer is critical in a patient being investigated for raised serum prostate-specific antigen but would be clinically irrelevant in a repeat biopsy from a patient on an active surveillance protocol. We outline an alternative approach that is focused on the clinical utility of the data items and the requirements of personalised medicine. While all core data items are required to be reported, understanding how these parameters are used to guide patient management will enable pathologists to focus time and resources on the critical aspects of an individual case. Detailed immunohistochemical workup and obtaining a second opinion would not be necessary if resolution of the differential diagnosis is of limited clinical significance. We also highlight some challenges encountered when adopting this approach and suggest some solutions that could positively impact histopathology reporting and patient care.

Automated Generation of Synoptic Reports from Narrative Pathology Reports in University Malaya Medical Centre Using Natural Language Processing

Pathology reports represent a primary source of information for cancer registries. University Malaya Medical Centre (UMMC) is a tertiary hospital responsible for training pathologists; thus narrative reporting becomes important. However, the unstructured free-text reports made the information extraction process tedious for clinical audits and data analysis-related research. This study aims to develop an automated natural language processing (NLP) algorithm to summarize the existing narrative breast pathology report from UMMC to a narrower structured synoptic pathology report with a checklist-style report template to ease the creation of pathology reports. The development of the rule-based NLP algorithm was based on the R programming language by using 593 pathology specimens from 174 patients provided by the Department of Pathology, UMMC. The pathologist provides specific keywords for data elements to define the semantic rules of the NLP. The system was evaluated by calculating the precision, recall, and F1-score. The proposed NLP algorithm achieved a micro-F1 score of 99.50% and a macro-F1 score of 98.97% on 178 specimens with 25 data elements. This achievement correlated to clinicians’ needs, which could improve communication between pathologists and clinicians. The study presented here is significant, as structured data is easily minable and could generate important insights.

Technical Standards for Cancer Surgery: Improving Patient Care through Synoptic Operative Reporting

The Operative Standards for Cancer Surgery manuals define critical elements of optimal cancer surgery based on data and expert opinion. These key aspects of commonly performed cancer operations define technical standards that can be used as a quality assurance tool for practicing surgical oncologists and as an educational tool for trainees. This article provides background on these operative standards and their subsequent integration into synoptic operative report templates. With the goal of codifying the most important aspects of surgical oncology care to elevate and harmonize cancer care, the American College of Surgeons Cancer Programs has developed comprehensive synoptic operative reports. Synoptic operative reports are structured so that key data elements are recorded in a standardized format with prespecified terminology. In contrast to the narrative or structured operative reports frequently used by surgeons, these synoptic operative reports improve semantic clarity, provide uniform fields for abstraction, and facilitate passive data collection and real-time analytics while delivering key information for downstream multidisciplinary patient care. In this way, the synoptic operative report is a key component of a comprehensive effort to elevate the quality of cancer care nationally.

Rule-Based Information Extraction from Free-Text Pathology Reports Reveals Trends in South African Female Breast Cancer Molecular Subtypes and Ki67 Expression

Clinical information on molecular subtypes and the Ki67 index is critical for breast cancer (BC) prognosis and personalised treatment plan. Extracting such information into structured data is essential for research, auditing, and cancer incidence reporting and underpins the potential for automated decision support. Herewith, we developed a rule-based natural language processing algorithm that retrieved and extracted important BC parameters from free-text pathology reports towards exploring molecular subtypes and Ki67-proliferation trends. We considered malignant BC pathology reports with different free-text narrative attributes from the South African National Health Laboratory Service. The reports were preprocessed and parsed through the algorithm. Parameters extracted by the algorithm were validated against manually extracted parameters. For all parameters extracted, we obtained accurate annotations of 83-100%, 93-100%, 91-100%, and 92-100% precision, recall, F₁-score, and kappa, respectively. There was a significant trend in the proportion of each molecular subtype by patient age, histologic type, grade, Ki67, and race. The findings also showed significant association in the Ki67 trend with hormone receptors, human epidermal growth factors, age, grade, and race. Our approach bridges the gap between data availability and actionable knowledge and provides a framework that could be adapted and reused in other cancers and beyond cancer studies. Information extracted from these reports showed interesting trends that may be exploited for BC screening and treatment resources in South Africa. Finally, this study strongly encourages the implementation of a synoptic style pathology report in South Africa.

Systematic reporting of medical kidney biopsies

The medical kidney biopsy has an important added value in patient care in nephrology. In order to facilitate communication between the pathologist and the nephrologist and optimize patient care, both the content and form of the medical kidney biopsy report matter. With some exceptions, current guidelines in nephropathology focus on content rather than form and, not surprisingly, medical kidney biopsy reports mostly consist of unformatted and often lengthy free text. In contrast, in oncology, a more systematic reporting called synoptic reporting has become the dominant method. Synoptic formats enable complete, concise and clear reports that comply with agreed upon standards. In this review we discuss the possibilities of systematic reporting in nephropathology (including synoptic reporting). Furthermore, we explore applications of electronic formats with structured data and usage of international terminologies or coding systems. The benefits include the timely collection of high-quality data for benchmarking between centres as well as for epidemiologic and other research studies. Based on these developments, a scenario for future medical kidney biopsy reporting is drafted.

Improving Interdisciplinary Communication: Barriers and Facilitators for Implementation of Standardized Structured Reporting in Oncology

BACKGROUND

Standardized structured reporting (SSR) improves quality of diagnostic cancer reporting and interdisciplinary communication in multidisciplinary team (MDT) meetings, resulting in more adequate treatment decisions and better health outcomes. However, use of SSR varies widely among pathologists, but might be encouraged by MDT members (MDTMs). Our objectives were to identify barriers and facilitators (influencing factors) for SSR implementation in oncologic pathology from the perspective of MDTMs and their determinants.

METHODS

In a multimethod design, we identified influencing factors for SSR implementation related to MDT meetings, using 5 domains: (1) innovation factors, (2) individual professional factors, (3) social setting factors, (4) organizational factors, and (5) political and legal factors. Four focus groups with MDTMs in urologic, gynecologic, and gastroenterologic oncology were conducted. We used an eSurvey among MDTMs to quantify the qualitative findings and to analyze determinants affecting these influencing factors.

RESULTS

Twenty-three MDTMs practicing in 9 oncology-related disciplines participated in the focus groups and yielded 28 barriers and 28 facilitators in all domains. The eSurvey yielded 211 responses. Main barriers related to lack of readability of SSR: difficulties with capturing nuances (66%) and formulation of the conclusion (43%); lack of transparency in the development (50%) and feedback processes of SSR templates (38%); and lack of information exchange about SSR between pathologists and other MDTMs (45%). Main facilitators were encouragement of pathologists' SSR use by MDTMs (90%) and expanding the recommendation of SSR use in national guidelines (80%). Oncology-related medical discipline and MDT type were the most relevant determinants for SSR implementation barriers.

CONCLUSIONS

Although SSR makes diagnostic reports more complete, this study shows important barriers in implementing SSR in oncologic pathology. The next step is to use these factors for developing and testing implementation tools to improve SSR implementation.

[Standardized structured reports for gastrointestinal tumors]

Synoptic reporting in pathology as opposed to traditional narrative reporting is defined by a laboratory value-like format and use of standardized checklists. It contributes to completeness and comprehensibility of pathology reports and ultimately patient care. As of today, two major institutions publish synoptic reporting templates, the College of American Pathologists (CAP) and the International Collaboration for Cancer Reporting (ICCR). Synoptic protocols are available for all major cancer types and provide not only a standardized terminology and a checklist for completeness of reports, but also facilitate uniform utilization of diagnostic criteria. Additionally, both CAP and ICCR protocols are accompanied by detailed and up-to-date reference lists and thereby represent a valuable source of information even when synoptic reporting is not used. The benefits and challenges of implementation of synoptic reporting are discussed, in particular with regard to reporting in German.

Toward a Platform for Structured Data Acquisition in Oncology: A Pilot Study on Prostate Cancer Screening

INTRODUCTION

Physicians spend an ever-rising amount of time to collect relevant information from highly variable medical reports and integrate them into the patient's health condition.

OBJECTIVES

We compared synoptic reporting based on data elements to narrative reporting in order to evaluate its capabilities to collect and integrate clinical information.

METHODS

We developed a novel system to align medical reporting to data integration requirements and tested it in prostate cancer screening. We compared expenditure of time, data quality, and user satisfaction for data acquisition, integration, and evaluation.

RESULTS

In a total of 26 sessions, 2 urologists, 2 radiologists, and 2 pathologists conducted the diagnostic work-up for prostate cancer screening with both narrative reporting and the novel system. The novel system led to a significantly reduced time for collection and integration of patient information (91%, p < 0.001), reporting in radiology (44%, p < 0.001) and pathology (33%, p = 0.154). The system usage showed a high positive effect on evaluated data quality parameters completeness, format, understandability, as well as user satisfaction.

CONCLUSION

This study provides evidence that synoptic reporting based on data elements is effectively reducing time for collection and integration of patient information. Further research is needed to assess the system's impact for different patient journeys.

Quality of Histopathological Reporting in Breast Cancer: Results From Four South African Breast Units

PURPOSE

High-quality histopathology reporting forms the basis for treatment decisions. The quality indicator for pathology reports from the European Society of Breast Cancer Specialists was applied to a cohort from four South African breast units.

METHODS

The study included 1,850 patients with invasive breast cancer and evaluated 1,850 core biopsies and 1,158 surgical specimen reports with cross-center comparisons. A core biopsy report required histologic type; tumor grade; and estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 (HER2) status, with a confirmatory test for equivocal HER2 results. Ki-67 was regarded as optional. Pathologic stage, tumor size, lymphovascular invasion, and distance to nearest invasive margin were mandatory for surgical specimens. Specimen turnaround time (TAT) was added as a locally relevant indicator.

RESULTS

Seventy-five percent of core biopsy and 74.3% of surgical specimen reports were complete but showed large variability across study sites. The most common reason for an incomplete core biopsy report was missing tumor grade (17.9%). Half of the equivocal HER2 results lacked confirmatory testing (50.6%). Ki-67 was reported in 89.3%. For surgical specimens, the closest surgical margin was reported in 78.1% and lymphovascular invasion in 84.8% of patients. Mean TAT was 11.9 days (standard deviation [SD], 10.8 days) for core biopsies and 16.1 days (SD, 11.3) for surgical specimens.

CONCLUSION

Histopathology reporting is at a high level but can be improved, especially for tumor grade, HER2, and Ki-67, as is reporting of margins and lymphovascular invasion. A South African pathology consensus will reduce variability among laboratories. Routine use of standardized data sheets with synoptic reports and ongoing audits will improve completeness of reports over time.

Update on Histological Reporting Changes in Neuroendocrine Neoplasms

PURPOSE OF REVIEW

Classification and nomenclature of neuroendocrine neoplasms (NEN) have frequently changed over the last years. These changes reflect both increasing knowledge and international standardisation.

RECENT FINDINGS

The most recent changes in the Gastro-Entero-Pancreatic system induced the concept of well-differentiated NET with high proliferation rate (NET G3), explaining partially the heterogeneity of G3 NEN. Even if the nomenclature in pulmonary NEN is still different, the terms 'carcinoid' and 'atypical carcinoid' are widely overlapping with NET G1 and NET G2. Molecular data shows an additional heterogeneity both in well-differentiated NET and poorly differentiated NEC. However, no studies are available demonstrating clinical usefulness yet. The heterogeneity of NEN regarding the organ of origin, differentiation and molecular subtypes make development of personalised therapy a challenge needing more international and interdisciplinary collaborations and clinical trials allowing stratification according to biological subgroups.