Diagnostic performance of ADC and ADCratio in MRI-based prostate cancer assessment: A systematic review and meta-analysis.

OBJECTIVES: To identify factors influencing the diagnostic performance of the quantitative imaging biomarkers ADC and ADCratio in prostate cancer (PCa) detection. MATERIALS AND METHODS: A systematic literature search was conducted in Embase, Medline and Web of Science, for studies evaluating ADC values and ADCratio for PCa diagnosis, using the same patient cohorts and using histopathological references as ground truth. Pooled sensitivities, specificities, summary ROC curves and AUCs were calculated from constructed contingency data tables. Diagnostic performance (AUC) was quantitatively pooled using a bivariate mixed effects model. For identifying influencing factors, subgroup analysis, publication bias and heterogeneity assessment were investigated. RESULTS: Thirteen studies, involving 1038 patients and 1441 lesions, were included. For ADC, the pooled sensitivity and specificity was 80% (95% CI: 74-85%) and 78% (95% CI: 70-85%), respectively. For ADCratio pooled sensitivity and specificity was 80% (95% CI: 74-84%) and 80% (95% CI: 71-87%). Summary ROC analysis revealed AUCs of 0.86 (95% CI: 0.83-0.89) and 0.86 (95% CI: 0.83-0.89), respectively. Meta-regression showed heterogeneity between both imaging biomarkers. Subgroup analysis showed that ADCratio improved diagnostic performance in comparison to ADC when including both peripheral and transitional zone lesions (AUC: 0.87 [95% CI: 0.84-0.90] and 0.82 [95% CI: 0.79-0.85], respectively). CONCLUSION: Both ADC and ADCratio imaging biomarkers showed good and comparable diagnostic performance in PCa diagnosis. However, ADCratio shows better diagnostic performance than ADC in diagnosing transition zone cancers. CLINICAL RELEVANCE STATEMENT: In quantitative MRI-based PCa diagnosis, the imaging biomarker ADCratio is useful in challenging MRI readings of lesions. Understanding the performance of quantitative imaging biomarkers better can aid diagnostic MRI protocols, enhancing the precision of PCa assessments. KEY POINTS: MRI diffusion-weighted imaging-based ADC and ADCratio have comparable diagnostic performance in PCa assessment. In contrast to ADC, the ADCratio improves diagnostic performance, when assessing whole gland lesions. Compared to ADCratio, the ADC demonstrates enhanced diagnostic performance when evaluating peripheral zone lesions.

Transcatheter pseudo-vascular isolation for localization and concentration of a large molecule theranostic probe into a transgenic OncoPIG kidney tumor.

INTRODUCTION: Great strides have been made identifying molecular and genetic changes expressed by various tumor types. These molecular and genetic changes are used as pharmacologic targets for precision treatment using large molecule (LM) proteins with high specificity. Theranostics exploits these LM biomolecules via radiochemistry, creating sensitive diagnostic and therapeutic agents. Intravenous (i.v.) LM drugs have an extended biopharmaceutical half-life thus resulting in an insufficient therapeutic index, permitting only palliative brachytherapy due to unacceptably high rates of systemic nontarget radiation doses to normal tissue. We employ tumor arteriole embolization isolating a tumor from the systemic circulation, and local intra-arterial (i.a.) infusion to improve uptake of a LM drug within a porcine renal tumor (RT). METHODS: In an oncopig RT we assess the in vivo biodistribution of 99mTc-labeled macroaggregated albumin (MAA) a surrogate for a LM theranostics agent in the RT, kidney, liver, spleen, muscle, blood, and urine. Control animals underwent i.v. infusion and experimental group undergoing arteriography with pseudovascular isolation (PVI) followed by direct i.a. injection. RESULTS: Injected dose per gram (%ID/g) of the LM at 1 min was 86.75 ± 3.76 and remained elevated up to 120 min (89.35 ± 5.77) with i.a. PVI, this increase was statistically significant (SS) compared to i.v. (13.38 ± 1.56 and 12.02 ± 1.05; p = 0.0003 p = 0.0006 at 1 and 120 min respectively). The circulating distribution of LM in the blood was less with i.a. vs i.v. infusion (2.28 ± 0.31 vs 25.17 ± 1.84 for i.v. p = 0.033 at 1 min). Other organs displayed a trend towards less exposure to radiation for i.a. with PVI compared to i.v. which was not SS. CONCLUSION: PVI followed by i.a. infusion of a LM drug has the potential to significantly increase the first pass uptake within a tumor. This minimally invasive technique can be translated into clinical practice, potentially rendering monoclonal antibody based radioimmunotherapy a viable treatment for renal tumors.

Neoadjuvant Immunotherapy in Locally Advanced Mismatch Repair-Deficient Colon Cancer.

BACKGROUND: Mismatch repair-deficient (dMMR) tumors can be found in 10 to 15% of patients with nonmetastatic colon cancer. In these patients, the efficacy of chemotherapy is limited. The use of neoadjuvant immunotherapy has shown promising results, but data from studies of this approach are limited. METHODS: We conducted a phase 2 study in which patients with nonmetastatic, locally advanced, previously untreated dMMR colon cancer were treated with neoadjuvant nivolumab plus ipilimumab. The two primary end points were safety, defined by timely surgery (i.e., ≤2-week delay of planned surgery owing to treatment-related toxic events), and 3-year disease-free survival. Secondary end points included pathological response and results of genomic analyses. RESULTS: Of 115 enrolled patients, 113 (98%; 97.5% confidence interval [CI], 93 to 100) underwent timely surgery; 2 patients had surgery delayed by more than 2 weeks. Grade 3 or 4 immune-related adverse events occurred in 5 patients (4%), and none of the patients discontinued treatment because of adverse events. Among the 111 patients included in the efficacy analysis, a pathological response was observed in 109 (98%; 95% CI, 94 to 100), including 105 (95%) with a major pathological response (defined as ≤10% residual viable tumor) and 75 (68%) with a pathological complete response (0% residual viable tumor). With a median follow-up of 26 months (range, 9 to 65), no patients have had recurrence of disease. CONCLUSIONS: In patients with locally advanced dMMR colon cancer, neoadjuvant nivolumab plus ipilimumab had an acceptable safety profile and led to a pathological response in a high proportion of patients. (Funded by Bristol Myers Squibb; NICHE-2 ClinicalTrials.gov number, NCT03026140.).

Can blood-based markers predict RECIST progression in non-small cell lung cancer treated with immunotherapy?

PURPOSE: In this study, we aimed to evaluate the potential of routine blood markers, serum tumour markers and their combination in predicting RECIST-defined progression in patients with stage IV non-small cell lung cancer (NSCLC) undergoing treatment with immune checkpoint inhibitors. METHODS: We employed time-varying statistical models and machine learning classifiers in a Monte Carlo cross-validation approach to investigate the association between RECIST-defined progression and blood markers, serum tumour markers and their combination, in a retrospective cohort of 164 patients with NSCLC. RESULTS: The performance of the routine blood markers in the prediction of progression free survival was moderate. Serum tumour markers and their combination with routine blood markers generally improved performance compared to routine blood markers alone. Elevated levels of C-reactive protein (CRP) and alkaline phosphatase (ALP) ranked as the top predictive routine blood markers, and CYFRA 21.1 was consistently among the most predictive serum tumour markers. Using these classifiers to predict overall survival yielded moderate to high performance, even when cases of death-defined progression were excluded. Performance varied across the treatment journey. CONCLUSION: Routine blood tests, especially when combined with serum tumour markers, show moderate predictive value  of RECIST-defined progression in NSCLC patients receiving immune checkpoint inhibitors. The relationship between overall survival and RECIST-defined progression may be influenced by confounding factors.

An International Expert-Based Consensus on the Definition of a Clinical Near-Complete Response After Neoadjuvant (Chemo)radiotherapy for Rectal Cancer.

BACKGROUND: A variety of definitions for a clinical near-complete response after neoadjuvant (chemo) radiotherapy for rectal cancer are currently used. This variety leads to inconsistency in clinical practice, long-term outcome, and trial enrollment. OBJECTIVE: The aim of this study was to reach expert-based consensus on the definition of a clinical near-complete response after (chemo) radiotherapy. DESIGN: A modified Delphi process, including a systematic review, 3 surveys, and 2 meetings, was performed with an international expert panel consisting of 7 surgeons and 4 radiologists. The surveys consisted of individual features, statements, and feature combinations (endoscopy, T2-weighted MRI, and diffusion-weighted MRI). SETTING: The modified Delphi process was performed in an online setting; all 3 surveys were completed online by the expert panel, and both meetings were hosted online. MAIN OUTCOME MEASURES: The main outcome was to reach consensus (80% or more agreement). RESULTS: The expert panel reached consensus on a 3-tier categorization of the near-complete response category based on the likelihood of the response to evolve into a clinical complete response after a longer waiting interval. The panelists agreed that a near-complete response is a temporary entity only to be used in the first 6 months after (chemo)radiotherapy. Furthermore, consensus was reached that the lymph node status should be considered when deciding on a near-complete response and that biopsies are not always needed when a near-complete response is found. No consensus was reached on whether primary staging characteristics have to be taken into account when deciding on a near-complete response. LIMITATIONS: This 3-tier subcategorization is expert-based; therefore, there is no supporting evidence for this subcategorization. Also, it is unclear whether this subcategorization can be generalized into clinical practice. CONCLUSIONS: Consensus was reached on the use of a 3-tier categorization of a near-complete response, which can be helpful in daily practice as guidance for treatment and to inform patients with a near-complete response on the likelihood of successful organ preservation. See Video Abstract. UN CONSENSO INTERNACIONAL BASADO EN EXPERTOS ACERCA DE LA DEFINICIN DE UNA RESPUESTA CLNICA CASI COMPLETA DESPUS DE QUIMIORADIOTERAPIA NEOADYUVANTE CONTRA EL CNCER DE RECTO: ANTECEDENTES:Actualmente, se utilizan una variedad de definiciones para una respuesta clínica casi completa después de quimioradioterapia neoadyuvante contra el cáncer de recto. Esta variedad resulta en inconsistencia en la práctica clínica, los resultados a largo plazo y la inscripción en ensayos.OBJETIVO:El objetivo de este estudio fue llegar a un consenso de expertos sobre la definición de una respuesta clínica casi completa después de quimioradioterapia.DISEÑO:Se realizó un proceso Delphi modificado que incluyó una revisión sistemática, 3 encuestas y 2 reuniones con un panel internacional de expertos compuesto por siete cirujanos y 4 radiólogos. Las encuestas consistieron en características individuales, declaraciones y combinaciones de características (endoscopía, T2W-MRI y DWI).AJUSTE:El proceso Delphi modificado se realizó en un entorno en línea; el panel de expertos completó las tres encuestas en línea y ambas reuniones se realizaron en línea.PRINCIPALES MEDIDAS DE RESULTADO:El resultado principal fue llegar a un consenso (≥80% de acuerdo).RESULTADOS:El panel de expertos llegó a un consenso sobre una categorización de tres niveles de la categoría de respuesta casi completa basada en la probabilidad de que la respuesta evolucione hacia una respuesta clínica completa después de un intervalo de espera más largo. Los panelistas coincidieron en que una respuesta casi completa es una entidad temporal que sólo debe utilizarse en los primeros 6 meses después de la quimioradioterapia. Además, se llegó a un consenso en que se debe considerar el estado de los nódulos linfáticos al decidir sobre una respuesta casi completa y que no siempre se necesitan biopsias cuando se encuentra una respuesta casi completa. No se llegó a un consenso sobre si se deben tener en cuenta las características primarias de estadificación al decidir una respuesta casi completa.LIMITACIONES:Esta subcategorización de 3 niveles está basada en expertos; por lo tanto, no hay evidencia que respalde esta subcategorización. Además, no está claro si esta subcategorización puede generalizarse a la práctica clínica.CONCLUSIONES:Se alcanzó consenso sobre el uso de una categorización de 3 niveles de una respuesta casi completa que puede ser útil en la práctica diaria como guía para el tratamiento y para informar a los pacientes con una respuesta casi completa sobre la probabilidad de una preservación exitosa del órgano. (Traducción - Dr. Aurian Garcia Gonzalez).

Reproducing RECIST lesion selection via machine learning: Insights into intra and inter-radiologist variation.

Background: The Response Evaluation Criteria in Solid Tumors (RECIST) aims to provide a standardized approach to assess treatment response in solid tumors. However, discrepancies in the selection of measurable and target lesions among radiologists using these criteria pose a significant limitation to their reproducibility and accuracy. This study aimed to understand the factors contributing to this variability. Methods: Machine learning models were used to replicate, in parallel, the selection process of measurable and target lesions by two radiologists in a cohort of 40 patients from an internal pan-cancer dataset. The models were trained on lesion characteristics such as size, shape, texture, rank, and proximity to other lesions. Ablation experiments were conducted to evaluate the impact of lesion diameter, volume, and rank on the selection process. Results: The models successfully reproduced the selection of measurable lesions, relying primarily on size-related features. Similarly, the models reproduced target lesion selection, relying mostly on lesion rank. Beyond these features, the importance placed by different radiologists on different visual characteristics can vary, specifically when choosing target lesions. Worth noting that substantial variability was still observed between radiologists in both measurable and target lesion selection. Conclusions: Despite the successful replication of lesion selection, our results still revealed significant inter-radiologist disagreement. This underscores the necessity for more precise guidelines to standardize lesion selection processes and minimize reliance on individual interpretation and experience as a means to bridge existing ambiguities.

Overcoming data scarcity in radiomics/radiogenomics using synthetic radiomic features.

PURPOSE: To evaluate the potential of synthetic radiomic data generation in addressing data scarcity in radiomics/radiogenomics models. METHODS: This study was conducted on a retrospectively collected cohort of 386 colorectal cancer patients (n = 2570 lesions) for whom matched contrast-enhanced CT images and gene TP53 mutational status were available. The full cohort data was divided into a training cohort (n = 2055 lesions) and an independent and fixed test set (n = 515 lesions). Differently sized training sets were subsampled from the training cohort to measure the impact of sample size on model performance and assess the added value of synthetic radiomic augmentation at different sizes. Five different tabular synthetic data generation models were used to generate synthetic radiomic data based on "real-world" radiomics data extracted from this cohort. The quality and reproducibility of the generated synthetic radiomic data were assessed. Synthetic radiomics were then combined with "real-world" radiomic training data to evaluate their impact on the predictive model's performance. RESULTS: A prediction model was generated using only "real-world" radiomic data, revealing the impact of data scarcity in this particular data set through a lack of predictive performance at low training sample numbers (n = 200, 400, 1000 lesions with average AUC = 0.52, 0.53, and 0.56 respectively, compared to 0.64 when using 2055 training lesions). Synthetic tabular data generation models created reproducible synthetic radiomic data with properties highly similar to "real-world" data (for n = 1000 lesions, average Chi-square = 0.932, average basic statistical correlation = 0.844). The integration of synthetic radiomic data consistently enhanced the performance of predictive models trained with small sample size sets (AUC enhanced by 9.6%, 11.3%, and 16.7% for models trained on n_samples = 200, 400, and 1000 lesions, respectively). In contrast, synthetic data generated from randomised/noisy radiomic data failed to enhance predictive performance underlining the requirement of true signal data to do so. CONCLUSION: Synthetic radiomic data, when combined with real radiomics, could enhance the performance of predictive models. Tabular synthetic data generation might help to overcome limitations in medical AI stemming from data scarcity.

Association of Lateral Pelvic Lymph Nodes with Disease Recurrence and Organ Preservation in Patients with Distal Rectal Adenocarcinoma Treated with Total Neoadjuvant Therapy.

OBJECTIVE: Assess the significance of enlarged lateral lymph nodes (LLN) for disease recurrence, metastasis, and organ preservation in patients with rectal cancer. BACKGROUND: Optimal treatment of rectal adenocarcinoma involving LLN is subject to debate. METHODS: A post hoc analysis of the OPRA trial, a multicenter study of patients with rectal cancer treated with total neoadjuvant therapy (TNT) followed by total mesorectal excision or watch-and-wait management. We analyzed the association of visible LLN (LLN+), LLN≥7 mm (short axis) on baseline MRI, and LLN≥4 mm on restaging MRI with recurrence, metastasis, and rectum preservation. RESULTS: At baseline, 57 out of 324 (18%) patients had LLN+. In 30 (53%) of 57 patients with LLN+ on baseline MRI, the LLN disappeared after TNT. Disease recurrence in LLN was rare (3.5% of patients with LLN+ and 0.4% of patients with LLN-). All patients with recurrence in LLN also had distant metastasis. The rate of organ preservation was significantly lower in patients with LLN≥4 mm on restaging MRI (P=0.013). We found no significant differences in rates of local recurrence or metastasis between patients with LLN+ vs. LLN- and in patients with LLN≥7 vs.<7 mm on baseline MRI. LLN dissection was performed in 3 patients; 2 of them died of distant metastasis. CONCLUSIONS: LLN involvement is not associated with disease recurrence or metastasis, but persistence of LLN≥4 mm after TNT is negatively associated with rectum preservation in patients with locally advanced rectal cancer treated with TNT. Dissection of lateral nodes likely benefits few patients.

Prognostic significance of MRI-detected extramural venous invasion according to grade and response to neo-adjuvant treatment in locally advanced rectal cancer A national cohort study after radiologic training and reassessment.

BACKGROUND: Detection of grade 3-4 extra mural venous invasion (mrEMVI) on magnetic resonance imaging (MRI) is associated with an increased distant metastases (DM)-rate. This study aimed to determine the impact of different grades of mrEMVI and their disappearance after neoadjuvant therapy. METHODS: A Dutch national retrospective cross-sectional study was conducted, including patients who underwent resection for rectal cancer in 2016 from 60/69 hospitals performing rectal surgery. Patients with a cT3-4 tumour ≤8 cm from the anorectal junction were selected and their MRI-scans were reassessed by trained abdominal radiologists. Positive mrEMVI grades (3 and 4) were analyzed in regard to 4-year local recurrence (LR), DM, disease-free survival (DFS) and overall survival (OS). RESULTS: The 1213 included patients had a median follow-up of 48 months (IQR 30-54). Positive mrEMVI was present in 324 patients (27%); 161 had grade 3 and 163 had grade 4. A higher mrEMVI stage (grade 4 vs grade 3 vs no mrEMVI) increased LR-risk (21% vs 18% vs 7%, <0.001) and DM-risk (49% vs 30% vs 21%, p < 0.001) and decreased DFS (42% vs 55% vs 69%, p < 0.001) and OS (62% vs 76% vs 81%, p < 0.001), which remained independently associated in multivariable analysis. When mrEMVI had disappeared on restaging MRI, DM-rate was comparable to initial absence of mrEMVI (both 26%), whereas LR-rate remained high (22% vs 9%, p = 0.006). CONCLUSION: The negative oncological impact of mrEMVI on recurrence and survival rates was dependent on grading. Disappearance of mrEMVI on restaging MRI decreased the risk of DM, but not of LR.

Impact of the new rectal cancer definition on multimodality treatment and interhospital variability: Results from a nationwide cross-sectional study.

AIM: This study aimed to determine the consequences of the new definition of rectal cancer for decision-making in multidisciplinary team meetings (MDT). The new definition of rectal cancer, the lower border of the tumour is located below the sigmoid take-off (STO), was implemented in the Dutch guideline in 2019 after an international Delphi consensus meeting to reduce interhospital variations. METHOD: All patients with rectal cancer according to the local MDT, who underwent resection in 2016 in the Netherlands were eligible for this nationwide collaborative cross-sectional study. MRI-images were rereviewed, and the tumours were classified as above or on/below the STO. RESULTS: This study registered 3107 of the eligible 3178 patients (98%), of which 2784 patients had an evaluable MRI. In 314 patients, the tumour was located above the STO (11%), with interhospital variation between 0% and 36%. Based on TN-stage, 175 reclassified patients with colon cancer (6%) would have received different treatment (e.g., omitting neoadjuvant radiotherapy, candidate for adjuvant chemotherapy). Tumour location above the STO was independently associated with lower risk of 4-year locoregional recurrence (HR 0.529; p = 0.030) and higher 4-year overall survival (HR 0.732; p = 0.037) compared to location under the STO. CONCLUSION: By using the STO, 11% of the prior MDT-based diagnosis of rectal cancer were redefined as sigmoid cancer, with potential implications for multimodality treatment and prognostic value. Given the substantial interhospital variation in proportion of redefined cancers, the use of the STO will contribute to standardisation and comparability of outcomes in both daily practice and trial settings.

Imaging in the era of risk-adapted treatment in colon cancer.

The treatment landscape for patients with colon cancer is continuously evolving. Risk-adapted treatment strategies, including neoadjuvant chemotherapy and immunotherapy, are slowly finding their way into clinical practice and guidelines. Radiologists are pivotal in guiding clinicians toward the most optimal treatment for each colon cancer patient. This review provides an overview of recent and upcoming advances in the diagnostic management of colon cancer and the radiologist's role in the multidisciplinary approach to treating colon cancer.

Artificial intelligence-assisted double reading of chest radiographs to detect clinically relevant missed findings: a two-centre evaluation.

OBJECTIVES: To evaluate an artificial intelligence (AI)-assisted double reading system for detecting clinically relevant missed findings on routinely reported chest radiographs. METHODS: A retrospective study was performed in two institutions, a secondary care hospital and tertiary referral oncology centre. Commercially available AI software performed a comparative analysis of chest radiographs and radiologists' authorised reports using a deep learning and natural language processing algorithm, respectively. The AI-detected discrepant findings between images and reports were assessed for clinical relevance by an external radiologist, as part of the commercial service provided by the AI vendor. The selected missed findings were subsequently returned to the institution's radiologist for final review. RESULTS: In total, 25,104 chest radiographs of 21,039 patients (mean age 61.1 years ± 16.2 [SD]; 10,436 men) were included. The AI software detected discrepancies between imaging and reports in 21.1% (5289 of 25,104). After review by the external radiologist, 0.9% (47 of 5289) of cases were deemed to contain clinically relevant missed findings. The institution's radiologists confirmed 35 of 47 missed findings (74.5%) as clinically relevant (0.1% of all cases). Missed findings consisted of lung nodules (71.4%, 25 of 35), pneumothoraces (17.1%, 6 of 35) and consolidations (11.4%, 4 of 35). CONCLUSION: The AI-assisted double reading system was able to identify missed findings on chest radiographs after report authorisation. The approach required an external radiologist to review the AI-detected discrepancies. The number of clinically relevant missed findings by radiologists was very low. CLINICAL RELEVANCE STATEMENT: The AI-assisted double reader workflow was shown to detect diagnostic errors and could be applied as a quality assurance tool. Although clinically relevant missed findings were rare, there is potential impact given the common use of chest radiography. KEY POINTS: • A commercially available double reading system supported by artificial intelligence was evaluated to detect reporting errors in chest radiographs (n=25,104) from two institutions. • Clinically relevant missed findings were found in 0.1% of chest radiographs and consisted of unreported lung nodules, pneumothoraces and consolidations. • Applying AI software as a secondary reader after report authorisation can assist in reducing diagnostic errors without interrupting the radiologist's reading workflow. However, the number of AI-detected discrepancies was considerable and required review by a radiologist to assess their relevance.

Multi-omics staging of locally advanced rectal cancer predicts treatment response: a pilot study.

Treatment response assessment of rectal cancer patients is a critical component of personalized cancer care and it allows to identify suitable candidates for organ-preserving strategies. This pilot study employed a novel multi-omics approach combining MRI-based radiomic features and untargeted metabolomics to infer treatment response at staging. The metabolic signature highlighted how tumor cell viability is predictively down-regulated, while the response to oxidative stress was up-regulated in responder patients, showing significantly reduced oxoproline values at baseline compared to non-responder patients (p-value < 10-4). Tumors with a high degree of texture homogeneity, as assessed by radiomics, were more likely to achieve a major pathological response (p-value < 10-3). A machine learning classifier was implemented to summarize the multi-omics information and discriminate responders and non-responders. Combining all available radiomic and metabolomic features, the classifier delivered an AUC of 0.864 (± 0.083, p-value < 10-3) with a best-point sensitivity of 90.9% and a specificity of 81.8%. Our results suggest that a multi-omics approach, integrating radiomics and metabolomic data, can enhance the predictive value of standard MRI and could help to avoid unnecessary surgical treatments and their associated long-term complications.

Value of Size and Malignant Features of Lateral Lymph Nodes in Risk Stratification at Lateral Local Recurrence of Rectal Cancer: A National Cohort Study.

BACKGROUND: Patients with rectal cancer who have enlarged lateral lymph nodes (LLNs) have an increased risk of lateral local recurrence (LLR). However, little is known about prognostic implications of malignant features (internal heterogeneity, irregular margins, loss of fatty hilum, and round shape) on MRI and number of enlarged LLNs, in addition to LLN size. METHODS: Of the 3,057 patients with rectal cancer included in this national, retrospective, cross-sectional cohort study, 284 with a cT3-4 tumor located ≤8 cm from the anorectal junction who received neoadjuvant treatment and who had visible LLNs on MRI were selected. Imaging was reassessed by trained radiologists. LLNs were categorized based on size. Influence of malignant features and the number of LLNs on LLR was investigated. RESULTS: Of 284 patients with at least 1 visible LLN, 122 (43%) had an enlarged node (≥7.0 mm) and 157 (55%) had malignant features. Of the 122 patients with enlarged nodes, 25 had multiple (≥2). In patients with a single enlarged node (n=97), a single malignant feature was associated with a 4-year LLR rate of 0% and multiple malignant features was associated with a rate of 17% (P=.060). In the group with multiple malignant features, their disappearance on restaging was associated with an LLR rate of 13% compared with an LLR rate of 20% for persistent malignant features (P=.532). The presence of intermediate-size LLNs (5.0-6.9 mm) with at least 1 malignant feature was associated with a 4-year LLR rate of 8%; the 4-year LLR rate was 13% when the malignant features persisted on restaging MRI (P=.409). Patients with multiple enlarged LLNs had a 4-year LLR rate of 28% compared with 11% for those with a single enlarged LLN (P=.059). CONCLUSIONS: The presence of multiple enlarged LLNs (≥7.0 mm), as well as multiple malignant features in an enlarged node contribute to the risk of developing an LLR. These radiologic features can be used for clinical decision-making regarding the potential benefit of LLN dissection.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Multi-sequence MRI radiomics of colorectal liver metastases: Which features are reproducible across readers?

PURPOSE: To assess the inter-reader reproducibility of radiomics features on multiple MRI sequences after segmentations of colorectal liver metastases (CRLM). METHOD: 30 CRLM (in 23 patients) were manually delineated by three readers on MRI before the start of chemotherapy on the contrast enhanced T1-weighted images (CE-T1W) in the portal venous phase, T2-weighted images (T2W) and b800 diffusion weighted images (DWI). DWI delineations were copied to the ADC-maps. 107 radiomics features were extracted per sequence. The intraclass correlation coefficient (ICC) was calculated per feature. Features were considered reproducible if ICC > 0.9. RESULTS: 90% of CE-T1W features were reproducible with a median ICC of 0.98 (range 0.76-1.00). 81% of DWI features were robust with median ICC = 0.97 (range 0.38-1.00). The T2W features had a median ICC of 0.96 (range 0.55-0.99) and were reproducible in 80%. ADC showed the lowest number of reproducible features with 58% and median ICC = 0.91 (range 0.38-0.99) When considering the lower bound of the ICC 95% confidence intervals, 58%, 66%, 54% and 29% reached 0.9 for the CE-T1W, DWI, T2W and ADC features, respectively. The feature class with the best reproducibility differed per sequence. CONCLUSIONS: The majority of MRI radiomics features from CE-T1W, T2W, DWI and ADC in colorectal liver metastases were robust for segmentation variability between readers. The CE-T1W yielded slightly better reproducibility results compared to DWI and T2W. The ADC features seem more susceptible to reader differences compared to the other three sequences.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

PURPOSE: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. METHODS: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. RESULT: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. CONCLUSION: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. CRITICAL RELEVANCE STATEMENT: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. KEY POINTS: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

An automated deep learning pipeline for EMVI classification and response prediction of rectal cancer using baseline MRI: a multi-centre study.

The classification of extramural vascular invasion status using baseline magnetic resonance imaging in rectal cancer has gained significant attention as it is an important prognostic marker. Also, the accurate prediction of patients achieving complete response with primary staging MRI assists clinicians in determining subsequent treatment plans. Most studies utilised radiomics-based methods, requiring manually annotated segmentation and handcrafted features, which tend to generalise poorly. We retrospectively collected 509 patients from 9 centres, and proposed a fully automated pipeline for EMVI status classification and CR prediction with diffusion weighted imaging and T2-weighted imaging. We applied nnUNet, a self-configuring deep learning model, for tumour segmentation and employed learned multiple-level image features to train classification models, named MLNet. This ensures a more comprehensive representation of the tumour features, in terms of both fine-grained detail and global context. On external validation, MLNet, yielding similar AUCs as internal validation, outperformed 3D ResNet10, a deep neural network with ten layers designed for analysing spatiotemporal data, in both CR and EMVI tasks. For CR prediction, MLNet showed better results than the current state-of-the-art model using imaging and clinical features in the same external cohort. Our study demonstrated that incorporating multi-level image representations learned by a deep learning based tumour segmentation model on primary MRI improves the results of EMVI classification and CR prediction with good generalisation to external data. We observed variations in the contributions of individual feature maps to different classification tasks. This pipeline has the potential to be applied in clinical settings, particularly for EMVI classification.

Outcomes and potential impact of a virtual hands-on training program on MRI staging confidence and performance in rectal cancer.

OBJECTIVES: To explore the potential impact of a dedicated virtual training course on MRI staging confidence and performance in rectal cancer. METHODS: Forty-two radiologists completed a stepwise virtual training course on rectal cancer MRI staging composed of a pre-course (baseline) test with 7 test cases (5 staging, 2 restaging), a 1-day online workshop, 1 month of individual case readings (n = 70 cases with online feedback), a live online feedback session supervised by two expert faculty members, and a post-course test. The ESGAR structured reporting templates for (re)staging were used throughout the course. Results of the pre-course and post-course test were compared in terms of group interobserver agreement (Krippendorf's alpha), staging confidence (perceived staging difficulty), and diagnostic accuracy (using an expert reference standard). RESULTS: Though results were largely not statistically significant, the majority of staging variables showed a mild increase in diagnostic accuracy after the course, ranging between + 2% and + 17%. A similar trend was observed for IOA which improved for nearly all variables when comparing the pre- and post-course. There was a significant decrease in the perceived difficulty level (p = 0.03), indicating an improved diagnostic confidence after completion of the course. CONCLUSIONS: Though exploratory in nature, our study results suggest that use of a dedicated virtual training course and web platform has potential to enhance staging performance, confidence, and interobserver agreement to assess rectal cancer on MRI virtual training and could thus be a good alternative (or addition) to in-person training. CLINICAL RELEVANCE STATEMENT: Rectal cancer MRI reporting quality is highly dependent on radiologists' expertise, stressing the need for dedicated training/teaching. This study shows promising results for a virtual web-based training program, which could be a good alternative (or addition) to in-person training. KEY POINTS: • Rectal cancer MRI reporting quality is highly dependent on radiologists' expertise, stressing the need for dedicated training and teaching. • Using a dedicated virtual training course and web-based platform, encouraging first results were achieved to improve staging accuracy, diagnostic confidence, and interobserver agreement. • These exploratory results suggest that virtual training could thus be a good alternative (or addition) to in-person training.