Can we use neutrophil to lymphocyte ratio in the diagnosis and prediction of extracapsular extension in localized prostate cancer?

Merder, Erkan; Dinçer, Selvi Tabak; Altunrende, Fatih

doi:10.1177/03915603211014864

Cited by 2 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In fact, this can reflect the role of machines in the clinic; that is, machine diagnosis is an assistant to clinicians and not a replacement for radiologists. The ECE stage with different anatomic sites of invasion indicates the aggressiveness of the primary tumor 23,24 . To better understand the effects of LiSNet on prognosis, we also explored survival in grades.…”

Section: Discussionmentioning

confidence: 99%

“…The ECE stage with different anatomic sites of invasion indicates the aggressiveness of the primary tumor. 23,24 To better understand the effects of LiSNet on prognosis, we also explored survival in grades. We found that OS was lower in patients with high-defect capsule appearance scaled by LiSNet, showing that staging capsular appearance with AI is clinically relevant.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Sun

Liu

et al. 2022

Medical Physics

View full text Add to dashboard Cite

Background Presurgical assessment of hepatocellular carcinoma (HCC) aggressiveness can benefit patients’ treatment options and prognosis. Purpose To develop an artificial intelligence (AI) tool, namely, LiSNet, in the task of scoring and interpreting HCC aggressiveness with computed tomography (CT) imaging. Methods A total of 358 patients with HCC undergoing curative liver resection were retrospectively included. Three subspecialists were recruited to pixel‐wise annotate and grade tumor aggressiveness based on CT imaging. LiSNet was trained and validated in 193 and 61 patients with a deep neural network to emulate the diagnostic acumen of subspecialists for staging HCC. The test set comprised 104 independent patients. We subsequently compared LiSNet with an experience‐based binary diagnosis scheme and human–AI partnership that combined binary diagnosis and LiSNet for assessing tumor aggressiveness. We also assessed the efficiency of LiSNet for predicting survival outcomes. Results At the pixel‐wise level, the agreement rate of LiSNet with subspecialists was 0.658 (95% confidence interval [CI]: 0.490–0.779), 0.595 (95% CI: 0.406–0.734), and 0.369 (95% CI: 0.134–0.566), for scoring HCC aggressiveness grades I, II, and III, respectively. Additionally, LiSNet was comparable to subspecialists for predicting histopathological microvascular invasion (area under the curve: LiSNet: 0.668 [95% CI: 0.559–0.776] versus subspecialists: 0.699 [95% CI: 0.591–0.806], p > 0.05). In a human–AI partnered diagnosis, combining LiSNet and experience‐based binary diagnosis can achieve the best predictive ability for microvascular invasion (area under the curve: 0.705 [95% CI: 0.589–0.820]). Furthermore, LiSNet was able to indicate overall survival after surgery. Conclusion The designed LiSNet tool warrants evaluation as an alternative tool for radiologists to conduct automatic staging of HCC aggressiveness at the pixel‐wise level with CT imaging. Its prognostic value might benefit patients’ treatment options and survival prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Sun

Liu

et al. 2022

Medical Physics

View full text Add to dashboard Cite

show abstract

Utility of Neutrophil–lymphocyte Ratio and Platelet–lymphocyte Ratio in Predicting Malignancy in Patients with Small Renal Mass

Surag,

Choudhary,

Singh

et al. 2024

Medical Journal of Dr. D.Y. Patil Vidyapeeth

View full text Add to dashboard Cite

Background: Abdominal imaging has become increasingly prevalent, resulting in the incidental detection of renal tumors. The majority of small renal masses (SRMs), which are surgically removed, prove to be malignant. However, some cases turn out to be benign, which can lead to unnecessary surgeries with associated significant morbidity. Recently, many inflammatory biomarkers have shown to be promising prognostic factors in RCC. We aimed to assess the importance of inflammatory biomarkers, which are simple and economical, in SRMs. Objective: We investigated whether preoperative neutrophil–lymphocyte ratio (NLR) and platelet–lymphocyte ratio (PLR) can predict malignancy in patients with SRMs. Material and Methods: A retrospective analysis was performed on 108 patients who underwent partial or radical nephrectomy for SRMs (tumors smaller than 4 cm) from January 2016 to December 2022. The patients were classified into two groups based on their histopathological diagnosis: benign group (n = 28) and malignant group (n = 80). NLR and PLR values were assessed for sensitivity using the ROC curve. Independent t-test, Mann–Whitney test, and Chi-square test were utilized. P <0.05 indicated statistical significance. Results: The mean age of patients was 58.9 ± 12.3 years in the benign group and 53.3 ± 11.9 years in the malignant group (P = 0.03). The mean tumor size was 3.26 ± 0.52 cm in the benign group and 3.04 ± 0.90 cm in the malignant group (P = 0.60). NLR (P = 0.008) and PLR (P ≤ 0.001) values were significantly higher in the malignant group (median: 3.65 and 201) compared to the benign group (median: 2.82 and 125), respectively. The area under a ROC curve was 0.669 (P = 0.002) and 0.758 (P < 0.001), with sensitivity values of 62.5% and 87.5%, specificity of 64.29% and 57.14%, and cutoff points of 3.45 and 130.21, for NLR and PLR, respectively. Conclusion: NLR and PLR can be considered biomarkers for predicting malignancy in SRMs. NLR and PLR should be included in a patient’s clinical evaluation along with imaging studies and overall health status.

show abstract

Can we use neutrophil to lymphocyte ratio in the diagnosis and prediction of extracapsular extension in localized prostate cancer?

Cited by 2 publications

References 20 publications

LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

LiSNet: An artificial intelligence ‐based tool for liver imaging staging of hepatocellular carcinoma aggressiveness

Utility of Neutrophil–lymphocyte Ratio and Platelet–lymphocyte Ratio in Predicting Malignancy in Patients with Small Renal Mass

Contact Info

Product

Resources

About