Radiological-pathological correlation of negative CT biopsy results enables high negative predictive value for thoracic malignancy

AIM

To evaluate multidisciplinary team (MDT) practice of radiological-pathological correlation of non-malignant biopsy results to examine the additive effect on the predictive values of computed tomography (CT) biopsy for malignancy and their subsequent management and outcomes.

MATERIALS AND METHODS

A service evaluation of the MDT management of non-malignant lung biopsy results (May 2014- May 2017) was undertaken.

RESULTS

Sixty patients had a non-malignant diagnosis on initial CT biopsy. Five patients were lost to follow-up leaving 55 in the final cohort. Forty-eight of the 55 patients had biopsy results classified as potentially non-specific, of which 26 were classified as concordant with radiology (e.g., organising pneumonia with compatible CT features), and 22 were classified as discordant (e.g., non-specific inflammation and yet sufficiently suspicious CT features). Patients with concordant negative pathology showed resolution (n=19) or stability (n=6) on imaging follow-up. One lesion demonstrated growth and was proven malignant on surgical resection. Discordant lesions were managed with repeat biopsy (n=8) or surgical resection (n=13), with 12 final benign diagnoses and nine malignancies. The negative predictive value of CT biopsy alone was 44/55 (80%), following repeat biopsy was 44/50 (88%), and following radiological-pathological assessment was 32/33 (97%). No patients underwent a shift in stage from time of biopsy to resection.

CONCLUSION

Combining radiological-pathological interpretation of negative biopsy results offers superior negative predictive value for lung malignancy without delayed diagnosis of lung cancer.

Sentinel and Non-Sentinel Lymph Node Metastasis Prediction and Validation of the MSKCC Nomograms for Indian Breast Cancer Patients

Background: Sentinel lymph node (SLN) biopsy accurately stages the axilla, but is time consuming and resource intensive. Nomograms and scoring systems have been developed, based on clinical and pathologic data available before surgery, to attempt to predict the likelihood of lymph node metastasis before surgery. As the management of the axilla in patients with low nodal burden changes, it is also important to predict whether there will be further axillary disease in patients with a positive SLN. Aim: To explore the risk factors for SLN and non-SLN metastasis in Indian women with breast cancer, by analysis of clinical and pathologic data. To assess the validity and clinical utility of two MSKCC nomograms that predicts axillary lymph node status for Western patients. Methods: Clinical data, and pathologic data available from core biopsy, for a consecutive series of women having SLNB was analyzed, and was plotted on two MSKCC nomograms. Univariate analysis was done by χ2 and Fischer exact tests and multivariate analysis was done by logistic regression method. A receiver-operating characteristic (ROC) curve was drawn and predictive accuracy was assessed by calculating the area under the ROC curve (AUC). Results: 34% (89 out of 256) of our patients had SLN positivity. When correlated with SLN metastasis by univariate analysis, LVI (χ2 = 80, P ≤ 0.001), PNI (χ2 = 13.36, P ≤ 0.001), ER+ (χ2 = 6.85, P = 0.009), PR+ (χ2 = 7.1, P = 0.008) and age ( P = 0.03) were significant. However, multivariate analysis showed that age (OR=1.04, P = 0.007) and LVI (OR=0.07, P ≤ 0.001) were identified as independent predictors for SLN metastasis. The area under the ROC curve was 0.772 and it fairly correlated with MSKCC nomogram. Patients with MSKCC scores lower than 38% had a frequency of SLN metastasis of 7.7% (5/65) and this cut-off could be used as a guide for not doing frozen section analysis in this subgroup. Further axillary dissection showed 41% (38 out of 92) had non-sentinel nodes positive. When correlated with non-SLN metastasis by univariate analysis, LVI (χ2 = 8.8, P = 0.003), PNI (χ2 = 6.85, P = 0.009), and extracapsular extension (χ2 = 4.18, P = 0.04) were significant. Number of SLN negative ( P = 0.01), SLN ratio (number of SLN positive/total number of SLN removed) ( P = 0.01) and size of SLN metastasis ( P = 0.002) were significant. However, multivariate analysis showed that only size of SLN metastasis (OR=0.845, P = 0.02) was identified as independent predictor for non-SLN metastasis. The area under the ROC curve was 0.66 and it poorly correlated with MSKCC nomogram. Conclusion: The MSKCC nomogram can provide a fairly accurate prediction of the probability of SLN metastasis, but is not for non-SLN metastasis. An institutional nomogram for non-SLN metastasis, including additional factors such as size of SLN metastasis, may improve prediction.