Potential image-based criteria of neoadjuvant chemotherapy for colon cancer: multireaders’ diagnostic performance

Park, Hyung-Sik; Cho, Seung-Hyun; Kim, Ji Eun; Moon, Sun Young; Park, Byung Geon; Seo, An Na; Kim, See Hyung; Kim, Gab Chul; Ryeom, Hunkyu; Choi, Gyu–Seog

doi:10.1007/s00261-019-02243-5

Cited by 8 publications

(9 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The criteria for selecting patients were determined by colorectal surgeons, radiologists, hemato-oncologists, radiation oncologists, pathologists, and patients using a multidisciplinary team approach. The imaging criteria which were T3/T4 cancer with longitudinal diameter ≥ 4 cm, and T3cd (extramural depth of invasion > 5 mm) showed the highest reproducibility and the lowest overtreatment ratio each in a previous study ( 19 20 ). The routine NAC protocol at our institution comprises four cycles of FOLFOX followed by surgery, and eight cycles of FOLFOX.…”

Section: Methodsmentioning

confidence: 62%

Response Prediction after Neoadjuvant Chemotherapy for Colon Cancer Using CT Tumor Regression Grade: A Preliminary Study

Je,

Cho,

et al. 2023

J Korean Soc Radiol

Self Cite

View full text Add to dashboard Cite

Purpose To investigate whether CT-based tumor regression grade (ctTRG) can be used to predict the response to neoadjuvant chemotherapy (NAC) in colon cancer. Materials and Methods A total of 53 patients were enrolled. Two radiologists independently assessed the ctTRG using the length, thickness, layer pattern, and luminal and extraluminal appearance of the tumor. Changes in tumor volume were also analyzed using the 3D Slicer software. We evaluated the association between pathologic TRG (pTRG) and ctTRG. Patients with Rödel’s TRG of 2, 3, or 4 were classified as responders. In terms of predicting responder and pathologic complete remission (pCR), receiver operating characteristic was compared between ctTRG and tumor volume change. Results There was a moderate correlation between ctTRG and pTRG (ρ = -0.540, p < 0.001), and the interobserver agreement was substantial (weighted κ = 0.672). In the prediction of responder, there was no significant difference between ctTRG and volumetry ( Az = 0.749, criterion: ctTRG ≤ 3 for ctTRG, Az = 0.794, criterion: ≤ -27.1% for volume, p = 0.53). Moreover, there was no significant difference between the two methods in predicting pCR ( p = 0.447). Conclusion ctTRG might predict the response to NAC in colon cancer. The diagnostic performance of ctTRG was comparable to that of CT volumetry.

show abstract

Section: Methodsmentioning

confidence: 62%

Response Prediction after Neoadjuvant Chemotherapy for Colon Cancer Using CT Tumor Regression Grade: A Preliminary Study

Je,

Cho,

et al. 2023

J Korean Soc Radiol

Self Cite

View full text Add to dashboard Cite

show abstract

“…The vast majority of studies have assessed inter-rater agreement of the ctTNM staging system using Cohen’s κ between two radiologists and have focused on agreement of the individual components of the staging system as opposed to overall agreement of high versus low-risk groups. 12,14–17 This is somewhat understandable as in most applications of the ctTNM staging system it is the ‘N” stage (negative vs positive) which defines a patient as being low or high risk. Wide variability in agreement is reported in the literature with T-stage kappas between 0.214 and 0.836, N-stage kappas 0.314–0.806 and EMVI kappas 0.178–0.239.…”

Section: Discussionmentioning

confidence: 99%

“…It is now widely accepted that CT prediction of nodal involvement (whatever the methodology) in the staging of colon cancer is inaccurate and indeed in previous studies has been likened to having an accuracy of flipping a coin. 12,19 Furthermore, the weighting of pathological nodal involvement in the TNM staging of colon cancer is likely responsible for the overlap in survival outcomes (survival paradox) between patients with pathological stage II and III disease which does not adequately take into account the poor prognostic attributes EMVI and tumour deposits. 6 Leijssen et al showed that EMVI positive stage II patients had worse survival outcomes compared with stage III EMVI negative patients, even after adjusting for the use of adjuvant chemotherapy, a finding that has been replicated.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Inter-rater agreement between radiologists using the novel CT-TDV (T3c+; tumour deposits; EMVI) system in patients with potentially curable right colon cancer

Hodges

Duxbury

Corr

et al. 2023

BJR

View full text Add to dashboard Cite

Objectives: The novel CT-TDV scoring system, identifying T3c + disease; presence/absence of tumour deposits and EMVI has been shown to be superior in predicting prognosis when compared to the CT-TNM staging system in the evaluation of colon cancer. Reproducibility of this scoring system between specialist GI radiologists has not been assessed previously. The aim of this study was to assess the inter rater agreement of gastrointestinal radiologists in assessing the novel pre-operative CT-TDV scoring of patients with potentially curable right-sided colon cancer. Methods: 93 right colon cancer pre-operative CT scans were graded as CT TDV ‘good’ versus TDV ‘poor’ by four radiologists. Inter rater agreement was assessed using the intraclass correlation coefficient (ICC) between all four readers and individual readers against the central radiologist using Cohen’s κ statistic. Results: The ICC comparing those graded as TDV ‘good’ versus TDV ‘poor’ for all 93 cases was 0.61 (0.51–0.70) indicating moderate reliability. Individual κ scores across the 93 cases were 0.76, 0.59 and 0.59 (p < 0.001) indicating moderate to substantial agreement. Conclusion: The CT TDV scoring system is reproducible amongst trained gastrointestinal radiologists in the assessment of newly diagnosed right colon cancer. Advances in knowledge: This further validates the clinical utility of the CT TDV scoring system as a prognostic tool to guide the management of patients with potentially curable right colon cancer.

show abstract

“…Patients with cancer have a high risk for nodal metastasis, therefore oncologically adequate surgery consists of segmental colectomy with lymph node * Supported by Australian Research Council through grant DP180103232 and by SA Health eHealth Innovation Grants Program (eIGP) dissection followed by adjuvant treatment [2]. However approximately 20% to 30% of patients develop another cancer months later, thus alternative treatment strategies including neoadjuvant therapy prior to surgery are currently being investigated [3]. Under these circumstances, accurate preoperative metastatic lymph node diagnosis is crucial in determining the eligibility of neoadjuvant treatment and to avoid the over treatment of patients.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

Glaser¹,

Maicas²,

Bedrikovetski³

et al. 2019

Preprint

View full text Add to dashboard Cite

The diagnosis of the presence of metastatic lymph nodes from abdominal computed tomography (CT) scans is an essential task performed by radiologists to guide radiation and chemotherapy treatment. State-of-the-art deep learning classifiers trained for this task usually rely on a training set containing CT volumes and their respective image-level (i.e., global) annotation. However, the lack of annotations for the localisation of the regions of interest (ROIs) containing lymph nodes can limit classification accuracy due to the small size of the relevant ROIs in this problem. The use of lymph node ROIs together with global annotations in a multi-task training process has the potential to improve classification accuracy, but the high cost involved in obtaining the ROI annotation for the same samples that have global annotations is a roadblock for this alternative. We address this limitation by introducing a new training strategy from two data sets: one containing the global annotations, and another (publicly available) containing only the lymph node ROI localisation. We term our new strategy semisupervised multi-domain multi-task training, where the goal is to improve the diagnosis accuracy on the globally annotated data set by incorporating the ROI annotations from a different domain. Using a private data set containing global annotations and a public data set containing lymph node ROI localisation, we show that our proposed training mechanism improves the area under the ROC curve for the classification task compared to several training method baselines.

show abstract

Potential image-based criteria of neoadjuvant chemotherapy for colon cancer: multireaders’ diagnostic performance

Cited by 8 publications

References 18 publications

Response Prediction after Neoadjuvant Chemotherapy for Colon Cancer Using CT Tumor Regression Grade: A Preliminary Study

Response Prediction after Neoadjuvant Chemotherapy for Colon Cancer Using CT Tumor Regression Grade: A Preliminary Study

Inter-rater agreement between radiologists using the novel CT-TDV (T3c+; tumour deposits; EMVI) system in patients with potentially curable right colon cancer

Semi-supervised Multi-domain Multi-task Training for Metastatic Colon Lymph Node Diagnosis From Abdominal CT

Contact Info

Product

Resources

About